text:
Susan's Picks
Effectiveness of Coaching
Employees and employers will foster cultures of life-long learning
Employers who create humanistic workplaces will win the war for talentRemote work is here to stayEmployers who invest in quality people-centric development will thriveEmployees and employers will foster cultures of life-long learningArtificial intelligence will change the nature of work and the number of workers needed There have been major disruptions in recent years that promise […]
Employers who create humanistic workplaces will win the war for talent
Remote work is here to stay
Employers who invest in quality people-centric development will thrive
Artificial intelligence will change the nature of work and the number of workers needed
There have been major disruptions in recent years that promise to change the very nature of work. From the ongoing shifts caused by the COVID19 pandemic, the impacts caused by automation, and other possible disruptions to the status quo, many wonder what the future holds in terms of employment. For example, a report by the McKinsey Global Institute that estimated automation will eliminate 73 million jobs by 2030.
To address this open question, we reached out to successful leaders in business, government, and labor, as well as thought leaders about the future of work to glean their insights and predictions on the future of work and the workplace.
As a part of this interview series called Susan S. Freeman.
Susan S. Freeman, MBA, PCC, NCC is an accredited Executive and Team Coach, leadership development consultant, speaker and author of "Step Up Now: 21 Powerful Principles for People Who Influence Others". Her passion is working with senior entrepreneurial leaders and teams by helping them lay the critical foundations required for scale.
She writes on humanistic leadership based on her unique system blending Western strategy and Eastern wisdom to activate the Guru Leader Within™. Visit her at www.susansfreeman.com and www.guruleaderwithin.com.
Can you tell us a bit about your background? Where do you come from? What are the life experiences that most shaped your current self?
I am a midwestern girl at heart, born and raised in the Kansas City area. When I went to college near Boston, I often heard "where's Toto?" I attended an all-girls high school and Wellesley College, one of the last all-women's colleges in the U.S. I went on to get an M.B.A. from Columbia University in New York, and worked on Madison Avenue and in London in strategic marketing and advertising with some of the world's largest packaged goods firms. That was my last stint in corporate America as I soon moved into non-profit management and entrepreneurship when we relocated to Tampa, FL. There I founded a non-profit, helped build a boutique retained executive search firm and most recently, my own coaching and leadership consulting business.
The life experiences that shaped me most growing up were being an only child for seven years and then an oldest where I spent a lot of time around caring adults; close relationships with grandparents who lived nearby and who were important influences in my life; immersing myself in studying piano and music throughout my youth; meeting my current husband of many years while in an art history class, and of course my three children.
More recently, it would be discovering professional coaching and diving deeply into learning; studying yoga and yogic philosophy and writing two books on leadership.
What do you expect to be the major disruptions for employers in the next 10–15 years? How should employers pivot to adapt to these disruptions?
I find it difficult to imagine the major disruptions awaiting employers in the next 10–15 years as we could not ever have imagined covid-19. In the next few years, employers will be re-calibrating from the damage done to workplaces during the pandemic. Rather than a loss, I see this as an opportunity to create humanistic, people-centered workplaces that focus on connection rather than separation, profit that is not gained at the expense of purpose, and physical and mental well-being for all. When we attend to the human dimensions, work not only gets done, it gets done well.
The choice as to whether or not a young person should pursue a college degree was once a "no-brainer". But with the existence of many high-profile millionaires (and billionaires) who did not earn degrees, as well as the fact that many graduates are saddled with crushing student loan debt and unable to find jobs it has become a much more complex question. What advice would you give to young adults considering whether or not to go to college?
My response is that this is an individual decision. There is no "right" way to create a life. I was taught, as were so many in my generation, that education, especially higher education was the key to our future success and happiness. That no longer holds true. I would invite young adults to consider what is it that they are drawn to? Where do they feel energized? If what they feel drawn to doesn't require a college degree, then perhaps get more experience in the world through a gap year, internships, apprenticeships and other ways to "test the waters" before committing four years to an undergraduate degree. There are many paths to a joyful life. What matters most is that the one you choose is a fit for who you are.
Despite the doom and gloom predictions, there are, and likely still will be, jobs available. How do you see job seekers having to change their approaches to finding not only employment, but employment that fits their talents and interests?
Right now, we are in an employee market. Employers are finding themselves in a war for talent. The very structure and nature of work has been upended. Job seekers are looking for places to work where there is care and concern for the employees' well-being, and also a bigger care for our society and for the planet. I think seekers need to be curious as to how to bring about the changes they wish to see in employment by asking thought-provoking questions in their interviews, and by reading and thinking deeply about where to focus their time and talent.
The statistics of artificial intelligence and automation eliminating millions of jobs, appears frightening to some. For example, Walmart aims to eliminate cashiers altogether and Dominos is instituting pizza delivery via driverless vehicles. How should people plan their careers such that they can hedge their bets against being replaced by automation or robots?
The statistics are indeed daunting. Within the next decade there will be a massive shift to automation and AI, causing many to lose their current jobs. However, there will also be many more jobs in different fields, including clean energy, healthcare, research and more. The trick is matching the needed work with worker skills. Many people will need to be open to re-training and learning, and we need to have the programs and processes in place to create robust train-to-work momentum.
Technological advances and pandemic restrictions hastened the move to working from home. Do you see this trend continuing? Why or why not?
As a work-from-homer since 2009, I do believe and hope the trend will continue. Many people did appreciate the flexibility that work from home allows them. However, to make it successful requires robust systems on the part of the employer as well as strong employee self-management. As we continue to navigate through the pandemic, I see that employers want employees back in office at least part-time, so I think a hybrid model will be popular. I do not think we're ever going back to a strict "five days/week on-site in office for everyone" policy, in part because employees won't agree.
What societal changes do you foresee as necessary to support the fundamental changes to work?
For there to be fundamental changes to work, society will need to come to grips with the needs of working families, including quality, affordable childcare, parental leave, and real attention paid to the importance of physical and mental well-being. A sick society is expensive, not just in the amount of money spent on healthcare, but also in lost human potential. We can do better.
What changes do you think will be the most difficult for employers to accept? What changes do you think will be the most difficult for employees to accept?
I think employers will find it most difficult to accept that employees don't want to be required to sit at desks for 8–9 hours/day, five days a week. We were told that work from home would never work, and yet within one week in March, 2020, much of the world was doing it, and quite effectively. Technology has changed the equation permanently.
I think employees will find it most difficult to accept that institutional change isn't often fast enough to keep up with the real-time needs they have. It takes time to turn large organizations around and to modify their assumptions, systems, and even cultures to the new reality.
The COVID-19 pandemic helped highlight the inadequate social safety net that many workers at all pay levels have. Is this something that you think should be addressed? In your opinion how should this be addressed?
Yes, I do believe that the pandemic shone a spotlight on the lack of a social safety net in the American workforce. We need to address it if we wish to thrive in the 21st century as a society. The happiest countries tend to be those that have strong social safety nets. My hope is that there are parts of that we can all agree make for a stronger, more prosperous society for everyone.
Despite all that we have said earlier, what is your greatest source of optimism about the future of work?
My greatest source of optimism about the future of work is that we are living in a time in which so much is being disrupted so quickly, we have no choice but to ask big questions and change the quality of our thinking around the status quo. Humanity has demonstrated tremendous resilience in times of war, famine, and pestilence. We can do this if we are committed to working together towards a common vision. It's agreeing to what we want that will be tricky.
Historically, major disruptions to the status quo in employment, particularly disruptions that result in fewer jobs, are temporary with new jobs replacing the jobs lost. Unfortunately, there has often been a gap between the job losses and the growth of new jobs. What do you think we can do to reduce the length of this gap?
Focus on the needed skills and leverage existing education and training to close those caps quickly and effectively. We should look to existing models both here and abroad where this has been done, and see how we might apply some of these successes.
Okay, wonderful. Here is the main question of our interview. What are your "Top 5 Trends to Watch in the Future of Work?" (Please share a story or example for each.)
Top 5 Trends to Watch in the Future of Work:
Thank you for these fantastic insights. We greatly appreciate the time you spent on this. We wish you continued success and good health.
Ready to Start Your Journey to Leadership Success?
SCHEDULE A COMPLIMENTARY LEADERSHIP DISCOVERY SESSION WITH SUSAN
Sign Up for Weekly Step UP Leader Tips
Copyright 2022 © Susan S. Freeman All Rights Reserved.


Reed Construction Data's Chief Economist Jim Haughey reports commercial (built to lease) market drivers turned almost universally positive during the spring. The only negative drivers are office and retail rents measured on a year over year basis. Both of these rental indexes have shown quarter to quarter rises recently so they are imposing little restraint on commercial project starts.
However, very weak economic growth gains early in 2011 may stall or even briefly reverse the improvement in the starts environment for commercial properties unless economic growth is clearly stronger in the second half of the year.
The build vs. buy indicator still points at buy for real estate investors in many but not all markets. Asset prices of existing building have risen significantly from the cyclical low level and continue to improve. This metric is progressively moving to the build side as the value of existing buildings rises and improving occupancy and rental rates boost net operating income prospects. However, this is a long process with the indicator tilting to build market by market. The process will take 2-3 years in the weakest markets but has already happened in a few markets.
The market drivers for institutional construction are rapidly deteriorating. Federal stimulus funding is largely spent of committed. Congress had refocused on spending cuts which will inevitably include federal buildings and grants to lower levels of government and nonprofit institutions. Rising tax revenues will eventually offset this constraint at the state level in 2013 but funding constraints will remain at the federal and local level and with nonprofit institutions longer. Largely self funded institutions, such as hospitals and private education, will fell relatively constraints from government spending cuts.


Page 87 - Gallery Design of Coloring Pages | Connecticuttrafficcourts Spain Flag To Color. Printable Pictures Of Insects. Dinosaur Pictures For Kids To Color.
collection of galleries from Coloring Pages like Spain Flag To Color. Printable Pictures Of Insects. Dinosaur Pictures For Kids To Color. and other designs you might like Color Drawing Pictures. Images Of Moses And The Ten Commandments. Beautiful Girl Book. Hot Wheels Coloring Pages Printable. Printing Monkey. Barbies Coloring Pages. Coloring Pages With Words.


text:
Claude Détraz, né le  à Albi  et mort le  à Challex est un physicien et homme politique français, spécialiste  des noyaux d'atomes exotiques, directeur de recherche au CNRS et directeur de l'Institut national de physique nucléaire et de physique des particules.

Biographie
Claude Détraz appartient à la promotion de 1958 de l'École Normale Supérieure.   Docteur en sciences physiques il rejoint, en 1962,  l'Institut de physique nucléaire d'Orsay, laboratoire créé par Irène et Fréderic Joliot-Curie. Il  participe aux travaux scientifiques aux États-Unis des laboratoire de Berkeley,  de l'université du Colorado) et de l'Institut Max Planck en Allemagne.

De 1982 à 1990, il dirige le GANIL, grand accélérateur national d'ions lourds,  à Caen.

Depuis sa fondation, en 1988, il préside le Comité européen de coopération en physique nucléaire, organisme associé de la Fondation européenne de la science.

En 1991, il est conseiller technique au cabinet du ministre de la Recherche, Hubert Curien.

Il est nommé directeur de l'Institut national de physique nucléaire et de physique des particules (IN2P3) en 1992   (mandat renouvelé en 1995 et 1998). Il précise les orientations qu'il veut donner à l'Institut en  déclarant " désormais, nous inscrivons nos recherches dans quatre domaines: matière nucléaire (dans tous ses états), quarks et leptons, quarks et hadrons dans la matière, noyaux et particules dans l'univers.."

Il est membre du Conseil supérieur de la recherche et de la technologie.

Il est directeur de recherche du CNRS, directeur du département de physique nucléaire et corpusculaire  en octobre 1997.

Claude Détraz est nommé Directeur des programmes avec cibles fixes et des programmes futurs du CERN en 1999  fonction qu'il exerce jusqu'au .

Il préside le conseil scientifique de l'Agence nationale pour la gestion des déchets radioactifs (ANDRA) en 1999, son mandat est renouvelé en 2005.

Claude Détraz est maire de Maison-Maugis de 2008 à 2014.

Il est membre du Comité économique et social de Basse-Normandie en 1991.

Décorations et récompenses
Claude Détraz reçoit en  1974 le Prix Joliot-Curie, de la  Société française de physique.

En 1978 la Médaille d'argent du CNRS lui est décderné.

Il est promu commandeur dans l'Ordre du mérite en 2006.

Publications
Protons, deutons, tritons et noyaux d'hélium émis par 197Au sous l'action de protons de 154 Mev [co-auteurs Jeannine Génin Pierre Radvanyi Ivan Brissaud], Journal de physique, janvier 1961

Contribution à l'étude de la structure des noyaux légers à couches ou sous-couches fermées par la réaction de pick-up à 155 Mev / Paris : Ed. de la Revue d'optique théorique et instrumentale , 1965

La matière aujourd'hui / R. Collongues, J.-F. Delpech, C. Détraz... [et al.] ; [introduction par Émile Noël] / Paris : Éditions du Seuil , DL 1981

La recherche en physique nucléaire   [articles de J. Arvieux, R. Bertini, M. Buenerd... [et al.] ; choisis et présentés par Claude Détraz et Didier Isabelle,  Paris, 1983,  Éditions du Seuil, coll.: Points-Sciences,  320 pages

Notes et références

Liens externes

Naissance en mars 1938
Naissance à Albi
Élève de l'École normale supérieure
Physicien français du XXe siècle
Physicien français du XXIe siècle
Personnalité du CERN
Maire de l'Orne
Décès dans l'Ain
Décès à 82 ans
Décès en juin 2020

redpajama_set_name: RedPajamaWikipedia


The interior of each baking pan has interlocking non-stick layers that are designed to deliver long-lasting, high-performance release. Releases up to 2X better than Calphalon Classic Bakeware.
Large baking surface and raised edges let you use this jelly roll pan to bake everything from cookies to sheet cakes. Edges mean baked goods won't slide off and juices won't spill over the sides.


text:
The Ultimate Bucket List of Things to Do in New York
USA New York Travel Info
#New York #USA
New York City is one of the most famous cities in the world and with good reason. It is a leader in the field of art, fashion, theatre and food. Celebrity sightings and glittery star-studded events like the Met Gala are a common occurrence. And most importantly, there is something for everyone to be found in this sprawling cosmopolitan city.
If you are thinking of, or planning a trip to the Big Apple, here is our ultimate bucket list of things to do in New York City.
Visit New York's World-Class Museums and Galleries
Image by Robert Bye
No New York travel guide would be complete without a reference to its museums, which rank among the best in the world.
Some of the museums we would recommend include:
(From L-R, Top-Bottom: Images by Ramon M, WikimediaImages, enriquelopezgarre, gothamjoe, Time Out, Andrea Rosen, NYTimes)
The Metropolitan Museum of Art: One of the most famous art museums in the world, The Met houses collections spanning the entire globe and over the past six millenia. Come here for a dive into global history, and will the day away as you explore galleries full of glorious Renaissance paintings, Japanese armouries, Egyptian tombs and Roman sculptures.
American Museum of Natural History: A museum dedicated to exploring human cultures and the natural world. It is also the film site of many popular Hollywood movies, most notably Night at the Museum.
The Guggenheim: Located on 1071 Fifth Avenue, the world-famous Guggenheim has some of the most famous paintings in the world. Come here for the Picassos, Bollards, and Gauguins, and take a photo against the famous spiral ramps of the Guggenheim's rotunda.
Louis Armstrong House Museum: This used to be the home of Louis Armstrong and since his death, has been converted into a famous museum. Come here to learn all about Louis - one of the most famous jazz players in the world - and see what it was to live as he did. The museum also offers guided tours as part of the admission price.
Intrepid Sea, Air & Space Museum: For a glimpse of world war history.
Art galleries in Chelsea: Did you know that Chelsea has a huge offering of art galleries featuring everything from 3D polychrome object paintings to tondo oil paintings? For a full list, use the Chelsea Gallery Map.
Museum of Sex: In case you want a break from all those paintings, how about a visit to a museum dedicated to the art of love?
Visit Broadway
Image by Sudan Ouyang
In case you didn't know, West End is to London what Broadway is to New York.
This 21 km street cuts from south of Manhattan, through the Bronz and into Inwood. There are 41 mainstream theatres featuring long-term classics such as The Lion King and the Phantom of the Opera, to the viral Hamilton, Aladdin, Mean Girls and Frozen.
Even if you have been to the West End before, Broadway will be a different experience. The type of shows available are different and the cast, who give each performance its unique flavour, is also different. For a full list of Broadway shows available, click here.
Admittedly, the theatre ticket prices might be a little steep but the experience of an amazing night out is something that all New Yorkers (and visitors) love. Nowhere else (maybe except in London?) will you get live theatre or musicals of this quality and scope so if this is a must-do on your list, make sure you book your tickets in advance!
Walk along the Highline
Image by NATA Trip from Pixabay
Located on the west side of Manhattan, the Highline is one of New York's (scratch that, the world's!) most famous green spaces. The Highline is 1.45 miles (over 2 km) long and built upon the former New York Central Railway.
Come here for a free stroll in the morning or late evening. Check out the public art, the varied indie food and art stalls, or sit on one of its benches to catch some sun rays while snacking away. To read up on the latest events happening at the Highline, you can visit the Highline's official website here.
And did we mention just how Instagrammable this gorgeous open space is?
Wander Around the Neighbourhood
Image by Adita Vyas
Every neighbourhood in New York is different and with the city being such a pedestrian-friendly place, it is time to take out your best walking shoes!
Some of our recommended neighbourhoods include:
Wall Street: The financial heart of the American economy, this is one place you should definitely visit if only to gap in awe at the towering skyscrapers and sharp suited men and women walking briskly all around. Wall Street also has plenty of touristy things to do, including taking a picture with the Charging Bull Statue on Bowling Green and the Fearless Girl Statue, the New York City Hall, the New York Stock Exchange, Federal Hall Building, and 9/11 Memorial & Museum.
Park Slope in Brooklyn: Park Slope is a historic place well-known for its pricey brownstones and tree-lined streets. Known as one of the most desired locations in New York, this is the perfect place to find your new dream home (or take a photo against!). There are plenty of other attractions nearby to supplement your visit, including the Old Stone House, Central Library, Prospect Park, Brooklyn Museum and a number of local boutique stores, cafes and restaurants to pop by.
Tribeca: Tribe is a hipster area in Lower Manhattan and the site of the Tribeca Film Festival around April or May. Tribeca is gentrified, full of cobblestone streets and refurbished industrial buildings that many celebrities call home. Some of the things to do here include visiting the Ghostbusters' Fire Station, playing with your children at Pier 25 or the Escapes Games NYC and dining at Tribeca Grill (co-owned by Robert de Niro!). There are also plenty of art museums and boutique shops to cater to every person's interests.
Try the New York Cookies
Image by Levain Bakery
New York City is a haven for all the Cookie Monsters out there. And at the head of all the cookie joints in the Big Apple, there is the Levain Bakery. The Levain Bakery is known for its chunky and gooey cookies, and is so amazing that it has influenced hordes of Levain-inspired / copycat cookie recipes.
If you are looking for a quintessentially NYC treat, try the Black & White Cookies. You can find in places like Russ & Daughters and Zabar's. Otherwise, head over to Insomnia Cookies (who deliver cookies to your doorstep up till 3am!), Jacques Torres Chocolate and Schmackery's.
If you are one with a sweet tooth, then this is a definite bucket list item to put on your food itinerary.
Try Some of the Most Instagrammable Food in Town
Source: Black Tap Craft Burgers & Beer
Speaking of food.. If there ain't a picture to show it, then it never happened.
New York gets it. It has perfected the art of creating Instagram-perfect delicacies that look good and taste amazing.
Some of our top tips include:
Dana's Bakery for some crazy rainbow-coloured cookies and macaroons;
Sweet Moments for some of the most photogenic, "Creamart" lattes and Korean shaved ice (otherwise known as bingsoos);
Emack & Bolio's for some insane crunchy, sprinkle, popcorn-laden ice-creams; and
Black Tap for the beer, burgers and crazy Instagrammable milkshakes (which come laden with cookies, M&Ms, bubble gum, caramel, Reese butter cups, brownies, candy floss and so much more).
Our list barely scratches the surface of what New York has to offer but it is a solid start. In fact, you might find yourself wanting to book a return trip after a taste of everything that the Big Apple has to offer!
Looking to travel? Don't forget to check out www.mayflower.com.my for some of the best flight deals in town. Follow Mayflower Facebook Page for the latest travel deals.

redpajama_set_name: RedPajamaCommonCrawl


text:
New form of badminton: AirBadminton (Credit: BWF)
AirBadminton – The new Outdoor Game
Published by : Badminton Europe
The Badminton World Federation (BWF) and HSBC jointly launched a new outdoor game, AirBadminton, and a new outdoor shuttlecock, the AirShuttle, at a global launch ceremony outside the Tianhe Gymnasium in Guangzhou, China, earlier today.
AirBadminton is an ambitious new development project in partnership with HSBC, BWF's Global Development Partner, designed to create opportunities for people of all ages and ability to play badminton on hard, grass and sand surfaces in parks, gardens, streets, playgrounds and beaches around the world.
The project has been five years in the making, starting with a vision to develop a new outdoor shuttlecock with increased durability, stability and wind resistance to allow people to have a more positive experience of badminton outdoors.
BWF President and Atlanta 1996 Olympic Games Men's Singles gold medallist, Poul-Erik Høyer, officiated the launch and said AirBadminton provides a pathway for increased global participation in the sport, while opening up doors for a new, highly attractive form of competitive badminton.
- This is a momentous occasion for badminton. Strategically, AirBadminton will allow us to fulfill our overall objective of putting a badminton racket in the hands of as many people as possible, he said.
- Given that most people's first experience with badminton comes in an outdoor environment, we are now making it easier for everyone to access the sport through a new outdoor game and new shuttlecock, the AirShuttle, he added.
- In the future, we see AirBadminton as an exciting, new, energetic version of the sport. Overall, the aim is to inspire more people to play more badminton in more places.
World Champions and Olymic gold medallists play AirBadminton
Joining Høyer at the launch was Global Head of Brand Partnerships, HSBC, Jonathan Castleman; Atlanta 1996 Men's Singles silver medallist, and now Chinese national Para badminton head coach, Dong Jiong; Athens 2004 Olympic Games gold medallist, Yang Wei; 2009 Women's Singles World Champion, Lu Lan; and 2010 Men's Singles World Champion, Chen Jin.
Castleman said HSBC was delighted to partner with the BWF for this momentous occasion.
- HSBC is delighted that more people will be able to enjoy the sport as a result of AirBadminton. We have seen, time and time again, how participating in sport at any age contributes to better health, well-being, greater inclusivity and accessibility, he said.
- AirBadminton will play a crucial role in this. We look forward to seeing ongoing benefits this programme will bring to communities in this region, and beyond.
Dong Jiong, who joined his Atlanta 1996 opponent Høyer in unveiling the AirShuttle, said:
- This is very exciting for the sport. I think this has tremendous potential here in China and around the world. It's also great that it is an inclusive game that can be enjoyed by people with disabilities.
Badminton can extend longevity
The launch was held in partnership with the Chinese Badminton Association (CBA) and the Guangzhou Sports Bureau (GSB). Badminton is a popular, fun and inclusive sport with more than 300 million active players globally.
In fact, just one hour of badminton can burn more than 450 calories. New research even shows that racket sports with a strong and enriched social connectedness such as badminton can extend longevity by as much as nine years.
An underlying objective of AirBadminton is to promote these health and social benefits of badminton by providing more positive badminton experiences to more people in more places.
As such, the BWF is currently working with leading sports manufacturers to mass produce and rollout the AirShuttle. The eventual aim is for the shuttlecock to be played in winds up to 12km per hour.
The new outdoor game is also played on new court dimensions, making it more suitable to play in outdoor environments; on hard, grass and sand surfaces around the world.


text:
FeatureXbox One
PUBG Xbox One Preview – First Impressions
December 21, 2017 — by Shrey "Twinkle Fairy" Pacheco — 0
After months in alpha on PC, 2017's surprise hit, PlayerUnknown's Battlegrounds has made its way to home consoles as an early preview. The PUBG Xbox One preview was available starting December 12, but right out of the bag there were issues. PUBG is a resource hog, to say the least, and asks for a decently powerful rig to run, so we were pretty curious to see how a console would handle it. We gave it a shot on the original Xbox One to see if the console could handle what PUBG could throw at it.
We're assuming that you know what PUBG is all about. However, if you've been avoiding any gaming related news for the past few months, here's a quick recap. PUBG takes you and 99 other players and drops you all into a huge sprawling map with nothing but the clothes (also optional) on your back. The premise is simple, the last one standing is the winner. Once you land on the map, it's up to you to scavenge and stock up with guns, ammunition and other supplies spread across the map. Over the course of the next few minutes, the play area will continue to shrink, increasing the chances of you coming across other players. It's simple, straightforward, and quite addictive.
PUBG Xbox One: Gameplay
So what's new about the gameplay on console? To put it bluntly, the game in its current state is a mess. Lag, poor textures, and framerate drops will be your constant companion through the game. When you're first dropped into the lobby, you'll be greeted with low poly textures and laggy gameplay. This continues when you are loaded onto the transport plane to be dropped into the game itself. It was pretty clear that we were going to be battling frame rate drops as much as the other players when we started playing the PUBG Xbox One preview.
As for controls, it's quite surprising that so many different actions available to PC players have been squeezed into a controller. Double taps, holding a button down and more have been implemented to bring the complex set of controls to the console's controller. For example, simply pressing the left trigger lets your player hold up the gun and aim. However, double tapping it brings up the iron sights/scope. Getting used to these controls can take a while. Inventory management is a pain and can take some getting used to. In a game where you can be shot at any time, this isn't welcome. Your best bet is finding a nice quiet spot in a building before you even think about managing your inventory. Also, there doesn't seem to be any aim assist for console players. So you're on your own when trying to shoot other players.
We feel like we should mention here that this author (for god knows what reason) kept himself away from the PC-version of PUBG for as long as possible. You might ask why, but we don't know. You'll just have to pester him and find out for yourself. In his defence, he did give the game a go on PC afterwards.
All you 'Master Racists' can rest assured that the controls are better on the PC. Especially when you consider the fact that there is no auto aim for console users. Switching to the scope is a pain and getting accurate headshots is close to impossible. But all controls such as leaning out from cover, different firing modes etc are all present. On the flip side, driving controls are better and you have the ability to creep your vehicle forward, making slightly lesser noise as compared to making an infernal racket. Another handy addition is the ability to vault over obstacles and through windows, which is currently live on the test servers for the PC version and should come to the game soon.
There are also a few other notable omissions. For example, the new Miramar map isn't available on Xbox yet, although it should be added soon. Weather effects like fog and rain also seem to be missing. We didn't come across any different weather in the limited time we played the game. We didn't have the chance to mess around with the loot crates either, but we're sure that it will remain just cosmetic.
Verdict: PUBG Xbox One Preview
All that being said, the essence of PUBG is still there in the Xbox version and that is why the game may still be worth your money. The tense atmosphere generated when you hear footsteps or a vehicle coming up to your hiding spot is still there. So is the sense of urgency when rushing across the map to a safe zone and the minuscule sense of accomplishment you feel when you've managed to outlive over half the players. Make sure you have a pair of headphones with you while playing; spatial awareness is a must with this game.
PUBG is currently available as a preview and it's pretty clear that's all it is. The game in its current state is a mess, but that doesn't mean that the game itself is bad. The gameplay is still exciting we can't wait for a time when the game's bugs are finally fixed and everything starts working as it should.
At Rs. 2000 the game is pretty expensive but if you are a fan of online shooters and are looking for something refreshing, then you can jump on the PUBG Xbox One bandwagon. However, if you are looking for a refined experience, then you may want to hold off till all the issues and bugs are ironed out.
Shrey "Twinkle Fairy" Pacheco
He sparkles ... like a fairy
PlayerUnknown
Previous PostImproving and bettering your life through gaming
Next PostTyler1 REFORMED - finally unbanned from League of Legends
ReviewXbox One Red Dead Redemption Xbox One Review - Welcome to the Wild Wild WestNovember 21, 2018 — by Sameer "Psycho Mantis" Mitha
ReviewXbox One Forza Horizon 4 Review: Welcome to the festival that never endsSeptember 27, 2018 — by Swapnil "flyingphoenixx" Mathur
ReviewXbox One State of Decay 2 - Not the exclusive the Xbox One needsMay 28, 2018 — by Sameer "Psycho Mantis" Mitha
FeatureXbox One Hellblade: Senua's Sacrifice is a lesson in realityApril 27, 2018 — by Swapnil "flyingphoenixx" Mathur
ReviewXbox One Far Cry 5 Xbox One Review: Far Cry refinedApril 4, 2018 — by Sameer "Psycho Mantis" Mitha
ReviewXbox One Sea of Thieves Review - Beautiful, but lacks depthMarch 30, 2018 — by Sameer "Psycho Mantis" Mitha
ReviewXbox One Super Lucky's Tale Review - What does the fox say?November 10, 2017 — by Shrey "Twinkle Fairy" Pacheco
ReviewXbox One Cuphead review - The Dark Souls of 2D platformersOctober 12, 2017 — by Sameer "Psycho Mantis" Mitha
FeaturePS4 The Uncharted franchise - Gaming's Indiana JonesOctober 9, 2017 — by Manish "Trigger-Happy" Rajesh
PCReviewXbox One Cities: Skylines ReviewJune 8, 2017 — by Abhijit "BabuMoshaaye" Dey
PCPS4ReviewXbox One Mass Effect: Andromeda ReviewMay 1, 2017 — by Siddharth "Staticsid" Parwatay
PCPS4ReviewXbox One Lego Worlds: ReviewApril 27, 2017 — by Sameer "Psycho Mantis" Mitha


text:
import 'normalize.css';
import '@babel/polyfill';
import FastClick from 'fastclick';
import PromiseFinally from 'promise.prototype.finally';
import { isDevelop, isInMobileDevice } from '@/utils/environment';
import flexible from './global/flexible';
import mountVue from './global/mount-vue';
import '@/styles/index.scss';

if (isInMobileDevice()) {
  flexible();
  document.body.className = 'mobile';
  document.documentElement.className = 'mobile';
} else {
  document.body.className = 'pc';
  document.documentElement.className = 'pc';
}

if (window.navigator.userAgent.indexOf('iPad') > -1) {
  FastClick.attach(document.body);
}
PromiseFinally.shim();

if (window.location.protocol === 'https:' && navigator.serviceWorker) {
  // window.navigator.serviceWorker.getRegistrations().then((registrations) => {
  //   registrations.forEach((registration) => {
  //     registration.unregister();
  //   });
  // });
  navigator.serviceWorker.register(`${process.env.PUBLIC_PATH}service-worker.js`);
}

if (isDevelop(true)) {
  const el = document.createElement('div');
  import('eruda').then(({ default: eruda }) => {
    eruda.init({
      container: el,
      tool: [
        'console',
        'elements',
        'network',
        'resource',
        'info',
        'snippets',
        'sources',
        'feature',
      ],
    });
    mountVue();
  });
  document.body.appendChild(el);
} else {
  mountVue();
}

redpajama_set_name: RedPajamaGithub


Secure yard with on-site owner/ manager for your convenience and safety. Please come down and see our facility and how we can help you.
If possible, use same size boxes for easy stacking.
Drain all gas from lawn mowers, snow blowers, autos, boats, motorcycles etc.


text:
Machine Learning Interview Questions Algorithms Supervised &Unsupervised Machine Learning Data Sets K-Clustering Probability Validation
List of Popular Questions To Crack Machine Learning Interview
By Jyoti Nigania |Email | Oct 23, 2018 | 4146 Views
Now every second person want to start his/her career in Machine Learning, Data Science as all are the highest paid fields. Machine Learning interview questions are the subpart of data science interview and this is the one way to gear up your career as data scientist, machine learning engineer or data engineer and many more.
Machine Learning Interview Questions: Categories
Traditionally seen machine learning interview questions pop up in several categories. The first really has to do with the algorithms and theory behind machine learning. You'll have to show an understanding of how algorithms compare with one another and how to measure their efficacy and accuracy in the right way.
The second category has to do with your programming skills and your ability to execute on top of those algorithms and the theory. The third has to do with your general interest in machine learning: you'll be asked about what's going on in the industry and how you keep up with the latest machine learning trends. Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward.
Here these interview questions are divided into two parts as mentioned below so that everybosy can easily crack their interview proces:
Machine Learning Interview Questions: Algorithms/Theory:
Q1- What's the trade-off between bias and variance?
More reading: Bias-Variance Tradeoff (Wikipedia)
Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm you're using. This can lead to the model underfitting your data, making it hard for it to have high predictive accuracy and for you to generalize your knowledge from the training set to the test set.
Variance is error due to too much complexity in the learning algorithm you're using. This leads to the algorithm being highly sensitive to high degrees of variation in your training data, which can lead your model to overfit the data. You'll be carrying too much noise from your training data for your model to be very useful for your test data. The bias-variance decomposition essentially decomposes the learning error from any algorithm by adding the bias, the variance and a bit of irreducible error due to noise in the underlying dataset. Essentially, if you make the model more complex and add more variables, you'll lose bias but gain some variance in order to get the optimally reduced amount of error, you'll have to tradeoff bias and variance. You don't want either high bias or high variance in your model.
Q2- What is the difference between supervised and unsupervised machine learning?
More reading: What is the difference between supervised and unsupervised machine learning? (Quora)
Supervised learning requires training labeled data. For example, in order to do classification (a supervised learning task), you'll need to first label the data you'll use to train the model to classify data into your labeled groups. Unsupervised learning, in contrast, does not require labeling data explicitly.
Q3- How is KNN different from k-means clustering?
More reading: How is the k-nearest neighbor algorithm different from k-means clustering? (Quora)
K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points. The critical difference here is that KNN needs labeled points and is thus supervised learning, while k-means doesn't and is thus unsupervised learning.
Q4- Explain how a ROC curve works?
More reading: Receiver operating characteristic (Wikipedia)
The ROC curve is a graphical representation of the contrast between true positive rates and the false positive rate at various thresholds. It's often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).
Q5- Define precision and recall?
More reading: Precision and recall (Wikipedia)
Recall is also known as the true positive rate: the amount of positives your model claims compared to the actual number of positives there are throughout the data. Precision is also known as the positive predictive value, and it is a measure of the amount of accurate positives your model claims compared to the number of positives it actually claims. It can be easier to think of recall and precision in the context of a case where you've predicted that there were 10 apples and 5 oranges in a case of 10 apples. You'd have perfect recall (there are actually 10 apples, and you predicted there would be 10) but 66.7% precision because out of the 15 events you predicted, only 10 (the apples) are correct.
Q6- What is Bayes' Theorem and how is it useful in a machine learning context?
More reading: An Intuitive (and Short) Explanation of Bayes' Theorem (BetterExplained)
Bayes' Theorem gives you the posterior probability of an event given what is known as prior knowledge. Mathematically, it's expressed as the true positive rate of a condition sample divided by the sum of the false positive rate of the population and the true positive rate of a condition. Say you had a 60% chance of actually having the flu after a flu test, but out of people who had the flu, the test will be false 50% of the time, and the overall population only has a 5% chance of having the flu. Would you actually have a 60% chance of having the flu after having a positive test?
Bayes' Theorem says no. It says that you have a (.6 * 0.05) (True Positive Rate of a Condition Sample) / (.6*0.05)(True Positive Rate of a Condition Sample) + (.5*0.95) (False Positive Rate of a Population) = 0.0594 or 5.94% chance of getting a flu.
Bayes' Theorem is the basis behind a branch of machine learning that most notably includes the Naive Bayes classifier. That's something important to consider when you're faced with machine learning interview questions.
Q7- Why is â??Naiveâ?? Bayes naive?
More reading: Why is â??naive Bayesâ?? naive? (Quora)
Despite its practical applications, especially in text mining, Naive Bayes is considered â??Naiveâ?? because it makes an assumption that is virtually impossible to see in real-life data: the conditional probability is calculated as the pure product of the individual probabilities of components. This implies the absolute independence of features a condition probably never met in real life. As a Quora commenter put it whimsically, a Naive Bayes classifier that figured out that you liked pickles and ice cream would probably naively recommend you a pickle ice cream.
Q8- Explain the difference between L1 and L2 regularization?
More reading: What is the difference between L1 and L2 regularization? (Quora)
L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.
Q9- What's your favorite algorithm, and can you explain it to me in less than a minute?
This type of question tests your understanding of how to communicate complex and technical nuances with poise and the ability to summarize quickly and efficiently. Make sure you have a choice and make sure you can explain different algorithms so simply and effectively that a five-year-old could grasp the basics!
Q10- What's the difference between Type I and Type II error?
More reading: Type I and type II errors (Wikipedia)
Don't think that this is a trick question! Many machine learning interview questions will be an attempt to lob basic questions at you just to make sure you're on top of your game and you've prepared all of your bases.
Type I error is a false positive, while Type II error is a false negative. Briefly stated, Type I error means claiming something has happened when it hasn't, while Type II error means that you claim nothing is happening when in fact something is. A clever way to think about this is to think of Type I error as telling a man he is pregnant, while Type II error means you tell a pregnant woman she isn't carrying a baby.
Q11- What's a Fourier transform?
More reading: Fourier transform (Wikipedia)
A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it's how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain it's a very common way to extract features from audio signals or other time series such as sensor data.
Q12- What's the difference between probability and likelihood?
More reading: What is the difference between â??likelihoodâ?? and â??probabilityâ??? (Cross Validated)
Q13- What is deep learning, and how does it contrast with other machine learning algorithms?
More reading: Deep learning (Wikipedia)
Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.
Q14- What's the difference between a generative and discriminative model?
More reading: What is the difference between a Generative and Discriminative Algorithm? (Stack Overflow)
A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.
Q15- What cross-validation technique would you use on a time series dataset?
More reading: Using k-fold cross-validation for time-series model selection (CrossValidated)
Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data it is inherently ordered by chronological order. If a pattern emerges in later time periods for example, your model may still pick up on it even if that effect doesn't hold in earlier years.
You'll want to do something like forward chaining where you'll be able to model on past data then look at forward-facing data.
fold 1 : training [1], test [2]
fold 2 : training [1 2], test [3]
fold 3 : training [1 2 3], test [4]
fold 4 : training [1 2 3 4], test [5]
fold 5 : training [1 2 3 4 5], test [6]
Q16- How is a decision tree pruned?
More reading: Pruning (decision trees)
Pruning is what happens in decision trees when branches that have weak predictive power are removed in order to reduce the complexity of the model and increase the predictive accuracy of a decision tree model. Pruning can happen bottom-up and top-down, with approaches such as reduced error pruning and cost complexity pruning.
Reduced error pruning is perhaps the simplest version: replace each node. If it doesn't decrease predictive accuracy, keep it pruned. While simple, this heuristic actually comes pretty close to an approach that would optimize for maximum accuracy.
Q17- Which is more important to you model accuracy, or model performance?
More reading: Accuracy paradox (Wikipedia)
This question tests your grasp of the nuances of machine learning model performance! Machine learning interview questions often look towards the details. There are models with higher accuracy that can perform worse in predictive power how does that make sense?
Well, it has everything to do with how model accuracy is only a subset of model performance, and at that, a sometimes misleading one. For example, if you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely predict no fraud at all if only a vast minority of cases were fraud. However, this would be useless for a predictive model a model designed to find fraud that asserted there was no fraud at all! Questions like this help you demonstrate that you understand model accuracy isn't the be-all and end-all of model performance.
Q18- What's the F1 score? How would you use it?
More reading: F1 score (Wikipedia)
The F1 score is a measure of a model's performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don't matter much.
Q19- How would you handle an imbalanced dataset?
More reading: 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset (Machine Learning Mastery)
An imbalanced dataset is when you have, for example, a classification test and 90% of the data is in one class. That leads to problems: an accuracy of 90% can be skewed if you have no predictive power on the other category of data! Here are a few tactics to get over the hump:
Collect more data to even the imbalances in the dataset.
Resample the dataset to correct for imbalances.
Try a different algorithm altogether on your dataset.
What's important here is that you have a keen sense for what damage an unbalanced dataset can cause, and how to balance that.
Q20- When should you use classification over regression?
More reading: Regression vs Classification (Math StackExchange)
Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points. You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.)
Q21- Name an example where ensemble techniques might be useful.
More reading: Ensemble learning (Wikipedia)
Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods, from bagging to boosting to a â??bucket of modelsâ?? method and demonstrate how they could increase predictive power.
Q22- How do you ensure you're not overfitting with a model?
More reading: How can I avoid overfitting? (Quora)
This is a simple restatement of a fundamental problem in machine learning: the possibility of overfitting training data and carrying the noise of that data through to the test set, thereby providing inaccurate generalizations. There are three main methods to avoid overfitting:
Keep the model simpler: reduce variance by taking into account fewer variables and parameters, thereby removing some of the noise in the training data.
Use cross-validation techniques such as k-folds cross-validation.
Use regularization techniques such as LASSO that penalize certain model parameters if they're likely to cause overfitting.
Q23- What evaluation approaches would you work to gauge the effectiveness of a machine learning model?
More reading: How to Evaluate Machine Learning Algorithms (Machine Learning Mastery)
You would first split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data. You should then implement a choice selection of performance metrics: here is a fairly comprehensive list. You could use measures such as the F1 score, the accuracy, and the confusion matrix. What's important here is to demonstrate that you understand the nuances of how a model is measured and how to choose the right performance measures for the right situations.
Q24- How would you evaluate a logistic regression model?
More reading: Evaluating a logistic regression (CrossValidated)
A subsection of the question above. You have to demonstrate an understanding of what the typical goals of a logistic regression are (classification, prediction etc.) and bring up a few examples and use cases.
Q25- What's the â??kernel trickâ?? and how is it useful?
More reading: Kernel method (Wikipedia)
The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space. This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates. Many algorithms can be expressed in terms of inner products. Using the kernel trick enables us effectively run algorithms in a high-dimensional space with lower-dimensional data.
Machine Learning Interview Questions: Programming:
These machine learning interview questions test your knowledge of programming principles you need to implement machine learning principles in practice. Machine learning interview questions tend to be technical questions that test your logic and programming skills: this section focuses more on the latter.
Q26- How do you handle missing or corrupted data in a dataset?
More reading: Handling missing data (O'Reilly)
You could find missing/corrupted data in a dataset and either drop those rows or columns, or decide to replace them with another value. In Pandas, there are two very useful methods: isnull() and dropna() that will help you find columns of data with missing or corrupted data and drop those values. If you want to fill the invalid values with a placeholder value (for example, 0), you could use the fillna() method.
Q27- Do you have experience with Spark or big data tools for machine learning?
More reading: 50 Top Open Source Tools for Big Data (Datamation)
You'll want to get familiar with the meaning of big data for different companies and the different tools they'll want. Spark is the big data tool most in demand now, able to handle immense datasets with speed. Be honest if you don't have experience with the tools demanded, but also take a look at job descriptions and see what tools pop up: you'll want to invest in familiarizing yourself with them.
Q28- Pick an algorithm. Write the psuedo-code for a parallel implementation?
More reading: Writing pseudocode for parallel programming (Stack Overflow)
This kind of question demonstrates your ability to think in parallelism and how you could handle concurrency in programming implementations dealing with big data. Take a look at pseudocode frameworks such as Peril-L and visualization tools such as Web Sequence Diagrams to help you demonstrate your ability to write code that reflects parallelism.
Q29- What are some differences between a linked list and an array?
More reading: Array versus linked list (Stack Overflow)
An array is an ordered collection of objects. A linked list is a series of objects with pointers that direct how to process them sequentially. An array assumes that every element has the same size, unlike the linked list. A linked list can more easily grow organically: an array has to be pre-defined or re-defined for organic growth. Shuffling a linked list involves changing which points direct where â?? meanwhile, shuffling an array is more complex and takes more memory.
Q30- Describe a hash table?
More reading: Hash table (Wikipedia)
A hash table is a data structure that produces an associative array. A key is mapped to certain values through the use of a hash function. They are often used for tasks such as database indexing.
Q31- Which data visualization libraries do you use? What are your thoughts on the best data visualization tools?
31 Free Data Visualization Tools (Springboard)
What's important here is to define your views on how to properly visualize data and your personal preferences when it comes to tools. Popular tools include R's ggplot, Python's seaborn and matplotlib, and tools such as Plot.ly and Tableau.
Machine Learning Interview Questions: Company/Industry Specific:
These machine learning interview questions deal with how to implement your general machine learning knowledge to a specific company's requirements. You'll be asked to create case studies and extend your knowledge of the company and industry you're applying for with your machine learning skills.
Q32- How would you implement a recommendation system for our company's users?
More reading: How to Implement A Recommendation System? (Stack Overflow)
A lot of machine learning interview questions of this type will involve implementation of machine learning models to a company's problems. You'll have to research the company and its industry in-depth, especially the revenue drivers the company has, and the types of users the company takes on in the context of the industry it's in.
Q33- How can we use your machine learning skills to generate revenue?
More reading: Startup Metrics for Startups (500 Startups)
This is a tricky question. The ideal answer would demonstrate knowledge of what drives the business and how your skills could relate. For example, if you were interviewing for music-streaming startup Spotify, you could remark that your skills at developing a better recommendation model would increase user retention, which would then increase revenue in the long run.
The startup metrics Slideshare linked above will help you understand exactly what performance indicators are important for startups and tech companies as they think about revenue and growth.
Q34- What do you think of our current data process?
More reading: The Data Science Process Email Course-Springboard
This kind of question requires you to listen carefully and impart feedback in a manner that is constructive and insightful. Your interviewer is trying to gauge if you'd be a valuable member of their team and whether you grasp the nuances of why certain things are set the way they are in the company's data process based on company- or industry-specific conditions. They're trying to see if you can be an intellectual peer. Act accordingly. Machine Learning Interview Questions: General Machine Learning Interest
This series of machine learning interview questions attempts to gauge your passion and interest in machine learning. The right answers will serve as a testament for your commitment to being a lifelong learner in machine learning.
Q35- What are the last machine learning papers you've read?
More reading: What are some of the best research papers/books for machine learning?
Keeping up with the latest scientific literature on machine learning is a must if you want to demonstrate interest in a machine learning position. This overview of deep learning in Nature by the scions of deep learning themselves (from Hinton to Bengio to LeCun) can be a good reference paper and an overview of what's happening in deep learning and the kind of paper you might want to cite.
Q36- Do you have research experience in machine learning?
Related to the last point, most organizations hiring for machine learning positions will look for your formal experience in the field. Research papers, co-authored or supervised by leaders in the field, can make the difference between you being hired and not. Make sure you have a summary of your research experience and papers ready and an explanation for your background and lack of formal research experience if you don't.
Q37- What are your favorite use cases of machine learning models?
What are the typical use cases for different machine learning algorithms? (Quora)
The Quora thread above contains some examples, such as decision trees that categorize people into different tiers of intelligence based on IQ scores. Make sure that you have a few examples in mind and describe what resonated with you. It's important that you demonstrate an interest in how machine learning is implemented.
Q38- How would you approach the â??Netflix Prizeâ?? competition?
More reading: Netflix Prize (Wikipedia)
The Netflix Prize was a famed competition where Netflix offered $1,000,000 for a better collaborative filtering algorithm. The team that won called BellKor had a 10% improvement and used an ensemble of different methods to win. Some familiarity with the case and its solution will help demonstrate you've paid attention to machine learning for a while.
Q39- Where do you usually source datasets?
More reading: 19 Free Public Data Sets For Your First Data Science Project (Springboard)
Machine learning interview questions like these try to get at the heart of your machine learning interest. Somebody who is truly passionate about machine learning will have gone off and done side projects on their own, and have a good idea of what great datasets are out there. If you're missing any, check out Quandl for economic and financial data, and Kaggle's Datasets collection for another great list.
Q40- How do you think Google is training data for self-driving cars?
More reading: Waymo Tech
Machine learning interview questions like this one really test your knowledge of different machine learning methods, and your inventiveness if you don't know the answer. Google is currently using recaptcha to source labelled data on storefronts and traffic signs. They are also building on training data collected by Sebastian Thrun at GoogleX some of which was obtained by his grad students driving buggies on desert dunes.
Welcome to this free online class on machine learning. Machine learning is one of the most exciting recent technologies.
Top IoT Trends need to be focused ON in 2019
Internet of Things was probably one of the obscure technologies at some point, when people were apprehensive about it, as they didn't know much about it.
How TensorFlow can assist you to create Chatbots?
Here is the video of Chatbots using TensorFlow which will give you an idea about what are chatbots and how did they come into existence. It provides a brief introduction about all the layers involved in creating a chatbot using TensorFlow and Machine Learning.


eTickets.ca has the best selection of Paul Taylor Dance Company tickets. Whether you are looking to buy the standard ticket or a vip ticket for Paul Taylor Dance Company upcoming events we have it all. If you are a fan of Paul Taylor Dance Company we have the right Paul Taylor Dance Company tickets for you. You can check a list of all Paul Taylor Dance Company upcoming shows and Paul Taylor Dance Company entire schedule here. Buy your Paul Taylor Dance Company Tickets from eTickets.ca and rest assured that you're getting the cheapest ticket deals on the best seats.
Currently we don't have any tickets for Paul Taylor Dance Company.
Oops! Paul Taylor Dance Company tickets aren't available. Do check out our array of other equally exciting and intriguing upcoming events.


The Dear Hunter "Act V: Hymns With The Devil In Confessional"
Group The Dear Hunter presented its new album titled Act V: Hymns With The Devil In Confessional, written in the style of Indie Rock, Progressive Rock and released on the label Equal Vision Records. You can order and download disk The Dear Hunter "Act V: Hymns With The Devil In Confessional" in any format MP3 320 kbps or lossless (FLAC, WAV, APE) online Amazon or eBay. View the entire discography of the group The Dear Hunter, we can, links to social networking group arranged below.


text:
Q: How to use svn tortoise by command in windows? I installed Tortoise-svn in my system(windows-7). i can use repository via Tortoise-svn application. but i want to use repository via command line how to do that?

A: TortoiseSVN 1.8 includes svn.exe command-line client. You can choose to install or not to install it in TortoiseSVN's installer.

redpajama_set_name: RedPajamaStackExchange


text:
Storey's Field Centre
Yoga Workshop with Howard Daly
For more details or to book please contact howarddaly@mac.com
£14 students
©2019 Storey's Field Centre – Privacy Policy
Subscribe to our newsletter Email address
Design by The District
Enter your search term and the results will be displayed before the form.
All visitors to Eddington are encouraged to cycle, walk or to use public transport.
Eddington is a 15 minute cycle from the centre of Cambridge and has plenty of cycle parking. The Ridgeway is a cycle and pedestrian path running through Eddington connecting to Storey's Way and Huntingdon Road and is supported by an extensive network of cycle and pedestrian routes. You can plan your cycle route on Cycle Streets, choosing the quietest or quickest route to suit you, our postcode is CB3 1AA.
Eddington is on the Universal Bus route, with the last bus departing from outside the Centre at 9.33pm Mondays-Fridays, 9.46pm Saturdays, and 6.45pm Sundays. More info on the Universal Bus (including fare information and the timetable) can be found on the Whippet website.
You can also use the Citi 4, X3 buses from Madingley Road, the Madingley Road Park and Ride and the Citi 5 and Citi 6 buses from Huntingdon Road. More info can be found on the Stagecoach website.
Eddington has been designed with sustainability in mind and as a result there is limited parking on site for residents and visitors at Eddington. A Travel Plan has been agreed and approved by local authorities and the University in consultation with travel groups and the wider community.
Sustainable travel is a large part of the Eddington Travel Plan so there is the dedicated U Bus (subsidised for University staff), extensive cycling and walking routes and provisions, and car club vehicles available. The final part of Eddington's Travel Plan is the Car Parking Management Plan, which lays out the parking policies and procedures at Eddington. These apply to all drivers and are not targeted at any particular group. They are essential for managing traffic at Eddington and ensuring the safety of cyclists and pedestrians, including residents, school pupils, visitors, and other members of the public.
We encourage visitors to avoid travelling by car to Storey's Field Centre where possible as there is limited parking on site. We advise anyone driving to the Centre to park at Madingley Road Park and Ride, which is free (for up to 18 hours) and only a 5-10 minute walk away.
Parking charges in Eddington apply 8am-6pm Monday to Friday (including public holidays) and 10am-5pm Saturday to Sunday (including public holidays). The majority of spaces have a maximum stay of 2 hours and no return within 1 hour, there are some spaces on site with a maximum stay of 4 hours. There is free disabled parking on site for Blue Badge holders (please note time restraints still apply). Drivers must register for parking using on-street machines, the Redde app, text or phone service, or at a dedicated kiosk inside the Storey's Field Centre reception.
Visitor parking information (updated July 2019)
Maps available to download:
Eddington map
Eddington Getting Here Map (Dec 17)
Universal Bus Timetable (Oct 18)
Visitors with Disabilities
There are three dedicated parking bays for people with disabilities very close to the Centre. The Centre is all on one level and accessible throughout. Please contact the Centre on 01223 656696 or email for information and advice if you have any questions or concerns.
The centre is open for community activities Monday to Wednesday from 09:00 until the last activity ends in the evening. Please see the event page for details.
We are open Thursday to Sunday for bookings and events, please see the events page for details and timings.
The office is staffed Monday to Friday from 09:00 until the last activity finishes.
Storey's Field Centre
Eddington Avenue
Cambridge CB3 1AA
Bookings and enquires: info@storeysfieldcentre.org.uk


Welcome to ST&T Research Intl.
Science, Toxicology & Technology (ST&T Research, Intl.) is comprised of a group of prominent M.D. & Ph.D. pharmacologists / toxicologists and other product development oriented physicians, scientists, researchers, regulatory compliance specialists. Many of these ST&T experts have been working together for more than 30 years.
ST&T specialists work in concert with the world's finest universities, research institutions, product manufacturers, legal and regulatory organizations (FDA-FTC) and customs / transportation entities.
Our physicians and clinical research teams are highly trained in state-of-the-art pharmacology, chemical and natural ingredients and cosmetic compounds. Some have extensive government regulatory standards and product development background.
The ST&T teams posses the product oriented talent and experience to provide specialized consultation, targeted consumer research, new product and brand development, packaging / label compliance, and environmental review. ST&T also provides education and training programs for Client QA/QC teams.
For its foreign Clients, ST&T provides US Agent services to assist the Client with FDA registrations and compliance, and specialists to advise on international shipping, contract manufacturing and customs import/export objectives.