Title: I have been a Java Developer then full stack developer all my career. I want to get started on AI. I am not sure where to start as it is overwhelming to look at the rapid changes coming in. Are there a list of books i can start with? My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it. So i asked ChatGPT to give me a detailed plan  and here is what it gave me. Could you please let me know if this is a way to go or if there is a better way<p>Transitioning from a full-stack engineer to the AI field can be a fulfilling and exciting journey. To make the most of this transition, you should focus on building a strong foundation in mathematics, programming, and machine learning concepts. Here&#x27;s a detailed learning plan, broken down into four stages:<p>Stage 1: Building a Strong Foundation<p>Mathematics:<p>Linear Algebra: &quot;Linear Algebra and Its Applications&quot; by Gilbert Strang<p>Calculus: &quot;Calculus: Early Transcendentals&quot; by James Stewart<p>Probability &amp; Statistics: &quot;Probability and Statistics for 
 Engineers and Scientists&quot; by Ronald E. Walpole, Raymond H. 
 Myers, Sharon L. Myers, and Keying E. Ye<p>Optimization: &quot;Convex Optimization&quot; by Stephen Boyd and 
 Lieven Vandenberghe<p>Programming:<p>Python: &quot;Python Crash Course&quot; by Eric Matthes<p>Git: &quot;Pro Git&quot; by Scott Chacon and Ben Straub<p>Machine Learning &amp; Data Science Basics:<p>&quot;Introduction to Data Science&quot; by Laura Igual and Santi Seguí
&quot;Python Data Science Handbook&quot; by Jake VanderPlas<p>Stage 2: Learning Machine Learning and Deep Learning<p>Online Courses:<p>Coursera: &quot;Machine Learning&quot; by Andrew Ng<p>Coursera: &quot;Deep Learning Specialization&quot; by Andrew Ng<p>Fast.ai: &quot;Practical Deep Learning for Coders&quot;<p>Books:<p>&quot;Pattern Recognition and Machine Learning&quot; by Christopher M. Bishop
&quot;Deep Learning&quot; by Ian Goodfellow, Yoshua Bengio, and Aaron Courville<p>Stage 3: Expanding Knowledge of AI Subfields<p>Natural Language Processing (NLP):<p>&quot;Speech and Language Processing&quot; by Daniel Jurafsky and James H. Martin<p>&quot;Natural Language Processing with Python&quot; by Steven Bird, Ewan Klein, and Edward Loper<p>Computer Vision:<p>&quot;Computer Vision: Algorithms and Applications&quot; by Richard Szeliski<p>&quot;Deep Learning for Computer Vision&quot; by Adrian Rosebrock
Reinforcement Learning:<p>&quot;Reinforcement Learning: An Introduction&quot; by Richard S. Sutton and Andrew G. Barto<p>&quot;Deep Reinforcement Learning Hands-On&quot; by Maxim Lapan<p>Stage 4: Staying Current and Gaining Practical Experience<p>Read research papers:<p>Subscribe to the arXiv mailing list in your areas of interest
Regularly read papers from conferences like NeurIPS, ICML, and ACL
Participate in online competitions:<p>Kaggle: Participate in machine learning competitions to improve your skills and build your portfolio
AIcrowd: Another platform for AI competitions
Contribute to open-source projects:<p>TensorFlow, PyTorch, or other popular AI frameworks
Look for projects on GitHub related to your interests and contribute by fixing bugs, implementing new features, or improving documentation
Network with AI professionals:<p>Attend AI conferences, workshops, and local meetups
Join AI-related forums, LinkedIn groups, or online communities like Reddit&#x27;s r&#x2F;MachineLearning
Remember that the learning plan can be adjusted based on your pace and interests. The key is to stay consistent and always be curious to learn more. Good luck on your journey into the AI field!
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