Learning Material
To successfully build AI agents, it's crucial to equip yourself with the right knowledge and resources. Here's a curated list of popular and effective resources you can leverage:
Online Courses and Tutorials
Deep Reinforcement Learning Nanodegree (Udacity): https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893
Artificial Intelligence A-Z™: Learn How To Build An AI (Udemy): https://www.udemy.com/course/artificial-intelligence-az/
Stanford CS221: Artificial Intelligence: Principles and Techniques: https://stanford-cs221.github.io/autumn2023/
Introduction to Artificial Intelligence with Python (HarvardX): https://www.edx.org/course/cs50s-introduction-to-artificial-intelligence-with-python
Books
Artificial Intelligence: A Modern Approach (Russell and Norvig): http://aima.cs.berkeley.edu/
Reinforcement Learning: An Introduction (Sutton and Barto): http://incompleteideas.net/book/the-book-2nd.html
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Géron): https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
Libraries and Frameworks
TensorFlow: https://www.tensorflow.org/
PyTorch: https://pytorch.org/
Scikit-learn: https://scikit-learn.org/stable/
Rasa: https://rasa.com/
Online Communities and Forums
Reddit: r/MachineLearning: https://www.reddit.com/r/MachineLearning/
Reddit: r/artificialintelligence: [invalid URL removed]
Hugging Face Forums: https://discuss.huggingface.co/
Additional Resources
Research Papers: You can find research papers on platforms like arXiv (https://arxiv.org/) and Google Scholar (https://scholar.google.com/)
Blogs and Websites: Some popular AI blogs include OpenAI Blog (https://openai.com/blog/), Towards Data Science (https://towardsdatascience.com/), and Machine Learning Mastery (https://machinelearningmastery.com/)
GitHub Repositories: Search for AI-related projects on GitHub (https://github.com/)
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