Answers.Body: <p>There are actually quite a few. Personally I would say these courses have high quality and strong  focus on practice:</p>
  
  <ul>
  <li>Standford computer vision cs231. Check the assignments materials on <a href="http://cs231n.stanford.edu/2019/syllabus.html" rel="noreferrer">this page</a>. This course has good explanation/exercises of how generally neural nets and backprop works.</li>
  <li><a href="https://github.com/fastai/course-v3/tree/master/nbs/dl1/" rel="noreferrer">Fastai course notebooks</a>. You can listen to the lectures as well, but notebooks are quite self-containing</li>
  <li><a href="https://github.com/yandexdataschool/Practical_RL" rel="noreferrer">Practical reinforcement learning</a> course, if you interested in NN application for RL</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-05-04T22:44:40.510Z
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Answers.LastEditDate: 2020-05-04T22:49:57.993Z
Answers.UserDisplayName: Kirill Fedyanin
Answers.Body: <p>One of the most famous books dedicated to neural networks is <a href="http://page.mi.fu-berlin.de/rojas/neural/" rel="nofollow noreferrer">Neural Networks - A Systematic Introduction</a> (1996) by <a href="https://en.wikipedia.org/wiki/Ra%C3%BAl_Rojas" rel="nofollow noreferrer">Raul Rojas</a>. <a href="http://page.mi.fu-berlin.de/rojas/neural/chapter/forword.pdf" rel="nofollow noreferrer">Most chapters end with a series of exercises</a> that test your understanding of the material. For example, in chapter <a href="http://page.mi.fu-berlin.de/rojas/neural/chapter/K14.pdf" rel="nofollow noreferrer">14 Stochastic Networks</a>, one of the exercises is</p>
  
  <blockquote>
    <p>Solve the eight queens problem using a Boltzmann machine. Define the
    network's weights by hand.</p>
  </blockquote>
  
  <p>This should give you a sense of the type of exercise that you will find in this book.</p>
  
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Answers.CreationDate: 2020-05-04T23:11:29.243Z
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Answers.UserDisplayName: nbro
Answers.Body: <p>The book <a href="http://www.hdip-data-analytics.com/_media/resources/pdf/s4/grokking_deep_learning.pdf" rel="nofollow noreferrer">Grokking Deep Learning</a>, by Andrew Trask (a PhD student at Oxford University and a research scientist at DeepMind), a wonderful, clean, and plain-English discussion of the basic mechanics that go on under the hood of neural networks - from data flow to updating of weights.  It is written without a slant on normally-wonky math, the concepts are presented and then advanced at a digestible pace for anyone. </p>
  
  <p>Here are a few more possibly useful resources.</p>
  
  <ol>
  <li><p><a href="https://developers.google.com/machine-learning/crash-course/introduction-to-neural-networks/playground-exercises" rel="nofollow noreferrer">Neural Networks: Playground Exercises</a> </p></li>
  <li><p><a href="https://cloudacademy.com/course/convolutional-neural-networks/exercise-1-solution/" rel="nofollow noreferrer">Getting Started With Deep Learning: Convolutional Neural Networks</a></p></li>
  <li><p><a href="https://tensorchiefs.github.io/dl_course/" rel="nofollow noreferrer">Deep learning focuses on practical aspects of deep learning</a></p></li>
  <li><p><a href="http://www.zemris.fer.hr/~ssegvic/du/lab1en.shtml" rel="nofollow noreferrer">First lab assignment in Deep learning</a></p></li>
  <li><p><a href="http://deeplearning.stanford.edu/tutorial/supervised/ExerciseConvolutionalNeuralNetwork/" rel="nofollow noreferrer">Deeplearning stanford</a></p></li>
  </ol>
  
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Answers.CreationDate: 2020-05-05T13:58:32.923Z
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Answers.UserDisplayName: Faizy
Body: <p>I am asking for a book (or any other online resource) where we can solve exercises related to neural networks, similar to the books or online resources dedicated to mathematics where we can solve mathematical exercises.</p>
  
Tags: <neural-networks><deep-learning><reference-request>
Title: What are some resources with exercises related to neural networks?
UserDisplayName: DSarkar
ThreadText: # What are some resources with exercises related to neural networks?
  
  (Asked by: username_0 on 2020-05-04)
  
  I am asking for a book (or any other online resource) where we can solve exercises related to neural networks, similar to the books or online resources dedicated to mathematics where we can solve mathematical exercises.
  
  **Tags:** neural-networks, deep-learning, reference-request
  
  ---
  
  ## 3 answers
  
  ### Answer by username_3 on 2020-05-05. Score: 5 | Accepted answer
  
  The book [Grokking Deep Learning](http://www.hdip-data-analytics.com/_media/resources/pdf/s4/grokking_deep_learning.pdf), by Andrew Trask (a PhD student at Oxford University and a research scientist at DeepMind), a wonderful, clean, and plain-English discussion of the basic mechanics that go on under the hood of neural networks - from data flow to updating of weights. It is written without a slant on normally-wonky math, the concepts are presented and then advanced at a digestible pace for anyone.
  
  Here are a few more possibly useful resources.
  
  1. [Neural Networks: Playground Exercises](https://developers.google.com/machine-learning/crash-course/introduction-to-neural-networks/playground-exercises)
  2. [Getting Started With Deep Learning: Convolutional Neural Networks](https://cloudacademy.com/course/convolutional-neural-networks/exercise-1-solution/)
  3. [Deep learning focuses on practical aspects of deep learning](https://tensorchiefs.github.io/dl_course/)
  4. [First lab assignment in Deep learning](http://www.zemris.fer.hr/~ssegvic/du/lab1en.shtml)
  5. [Deeplearning stanford](http://deeplearning.stanford.edu/tutorial/supervised/ExerciseConvolutionalNeuralNetwork/)
  
  
  ---
  
  ### Answer by username_1 on 2020-05-04. Score: 5
  
  There are actually quite a few. Personally I would say these courses have high quality and strong focus on practice:
  
  * Standford computer vision cs231. Check the assignments materials on [this page](http://cs231n.stanford.edu/2019/syllabus.html). This course has good explanation/exercises of how generally neural nets and backprop works.
  * [Fastai course notebooks](https://github.com/fastai/course-v3/tree/master/nbs/dl1/). You can listen to the lectures as well, but notebooks are quite self-containing
  * [Practical reinforcement learning](https://github.com/yandexdataschool/Practical_RL) course, if you interested in NN application for RL
  
  
  ---
  
  ### Answer by username_2 on 2020-05-04. Score: 3
  
  One of the most famous books dedicated to neural networks is [Neural Networks - A Systematic Introduction](http://page.mi.fu-berlin.de/rojas/neural/) (1996) by [Raul Rojas](https://en.wikipedia.org/wiki/Ra%C3%BAl_Rojas). [Most chapters end with a series of exercises](http://page.mi.fu-berlin.de/rojas/neural/chapter/forword.pdf) that test your understanding of the material. For example, in chapter [14 Stochastic Networks](http://page.mi.fu-berlin.de/rojas/neural/chapter/K14.pdf), one of the exercises is
  
  > Solve the eight queens problem using a Boltzmann machine. Define the
  > network's weights by hand.
  
  This should give you a sense of the type of exercise that you will find in this book.
  
  
  ---
  
  