Deep Reinforcement Learning Hands-On

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1. FullStackDev1Apr 2023
   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 My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it.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

   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's a detailed learning plan, broken down into four stages:

   Stage 1: Building a Strong Foundation

   Mathematics:

   Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang

   Calculus: "Calculus: Early Transcendentals" by James Stewart

   Probability & Statistics: "Probability and Statistics for Engineers and Scientists" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye

   Optimization: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe

   Programming:

   Python: "Python Crash Course" by Eric Matthes

   Git: "Pro Git" by Scott Chacon and Ben Straub

   Machine Learning & Data Science Basics:

   "Introduction to Data Science" by Laura Igual and Santi Seguí "Python Data Science Handbook" by Jake VanderPlas

   Stage 2: Learning Machine Learning and Deep Learning

   Online Courses:

   Coursera: "Machine Learning" by Andrew Ng

   Coursera: "Deep Learning Specialization" by Andrew Ng

   Fast.ai: "Practical Deep Learning for Coders"

   Books:

   "Pattern Recognition and Machine Learning" by Christopher M. Bishop "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

   Stage 3: Expanding Knowledge of AI Subfields

   Natural Language Processing (NLP):

   "Speech and Language Processing" by Daniel Jurafsky and James H. Martin

   "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper

   Computer Vision:

   "Computer Vision: Algorithms and Applications" by Richard Szeliski

   "Deep Learning for Computer Vision" by Adrian Rosebrock Reinforcement Learning:

   "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto

   "Deep Reinforcement Learning Hands-On" by Maxim Lapan

   Stage 4: Staying Current and Gaining Practical Experience

   Read research papers:

   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:

   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:

   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:

   Attend AI conferences, workshops, and local meetups Join AI-related forums, LinkedIn groups, or online communities like Reddit's r/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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2. fhfhhf1233Jan 2020
   The Melanie Mitchell's book is mostly a high level overview. It's a really good introduction to the field but you will not become a good practitioner by just reading the book. If you want the mathematical details then d2l.ai so far seems pretty good but they use MXNet for the code. fast.ai uses PyTorch and in the later lessons introduces Swift4Tensorflow which bakes differentiable programming into the compiler. PyTorch is more popular so more jobs are likely to have code bases that use PyTorch.

   So I'd say it depends on your use cases. If you want high level understanding then go with Melanie Mitchell's book. If you want more technical details then d2l.ai or fast.ai are good choices.

   There's also "Deep Reinforcement Learning Hands-On" by Maxim Lapan. That one is also pretty good but I'm not far enough along to give a review one way or another.

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3. knicholesOct 2018
   I need to figure out how to turn this into an openAI gym so I can train my DQN on it. I just finished running the code from Deep Reinforcement Learning - Hands on for Pong, and it's pretty cool...
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