In this free PyTorch course learn the key concepts behind deep learning and how to apply the concepts to a real-life project using PyTorch and Python.
The course covers the following:
In this video, Joshua Achiam explores reinforcement learning and exploring the OpenAI environment.
Here’s a great introductory tutorial on deep learning with Python, TensorFlow, and Keras by sentdex.
Lex Fridman has just posted this video of the first lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL.
For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
has just posted another excellent lecture — this time on the recent developments in deep learning that are defining the state of the art in the field
Lex Fridman posted a video an introductory lecture overviewing the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers.
For more lecture videos visit this website or follow code tutorials on GitHub.
In this video, Siraj Raval reviews the top AI innovations of 2018 and a few predictions for 2019.
Lex Fridman interviews Pieter Abbeel, a professor at UC Berkeley and director of the Berkeley Robot Learning Lab. He is one of the top researchers in the world working on how to make robots understand and interact with the world around them, especially through imitation and deep reinforcement learning.
This interview is part of the Artificial Intelligence podcast and the MIT course 6.S099: Artificial General Intelligence. The conversation and lectures are free and open to everyone. Audio podcast version is available on https://lexfridman.com/ai/
Kaggle has a great video on how to do Deep Learning from scratch.
Siraj Raval explores one of the newest frontiers of deep learning: Geometric Deep Learning and why non-Euclidian geometry requires new kinds of deep learning approaches.
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