Let me explain PyTorch in 7 Concepts

PyTorch is THE essential deep learning library for both research and industrial projects. This comprehensive PyTorch tutorial provides a complete guide to all its key concepts and features, from the basics of tensors and automatic differentiation to building and training advanced deep neural networks for computer vision and natural language processing tasks. There is also mention of customization with different forms of loss functions, optimizers, and guidance for further studies. Follow me on Twitter: https://x.com/neural_avb Code, slides, and additional materials from all my YouTube videos are available on my Patreon. Check our Patreon page at: https://www.patreon.com/NeuralBreakdownwithAVB This video will start with the fundamentals and gradually work our way up to more advanced topics like backpropagation, gradient accumulation, loss functions, and SGD. We will walk through the implementation ideas behind a lot of popular neural architectures (CNNs, ResNets, AutoEncoders, GRUs, Seq2Seq, Attention, Bayesian Nets)! Videos to watch: Playlist of all Projects: https://www.youtube.com/playlist?list=PLGXWtN1HUjPe7Y6EqwDpW4rrc-7sv_BjZ Playlist of all Computer Vision Videos: https://www.youtube.com/playlist?list=PLGXWtN1HUjPf_QYS4DnOxvotlOgiODZpD Playlist of all NLP / LLM Videos: https://www.youtube.com/playlist?list=PLGXWtN1HUjPfK_n9j5tPZ_a6Rx3yceZ_B Timestamps: 0:00 - Intro 1:40 - Tensors 5:36 - Automatic Differentiation 13:16 - Linear Models & MLP 21:08 - Working with Images & Convolution 26:51 - Working with Text, RNNs, Attention 30:08 - Customization - Losses, Optimizers, Torch Distributions 35:44 - Logging & Deployment 39:56 - Outro & Additional Libraries