Embark on a comprehensive journey through PyTorch with this 7-hour tutorial series. Begin with installation and tensor basics, then progress to advanced concepts like gradient calculation, backpropagation, and autograd. Master essential components of the training pipeline including models, loss functions, and optimizers. Dive into practical applications with linear and logistic regression, dataset handling, and batch training. Explore crucial deep learning topics such as activation functions, feed-forward neural networks, and convolutional neural networks (CNNs). Advance your skills with transfer learning, TensorBoard usage, and model saving/loading techniques. Gain hands-on experience by creating and deploying a deep learning app using Flask and Heroku. Delve into recurrent neural networks (RNNs) with tutorials on LSTM and GRU. Discover PyTorch Lightning for streamlined research workflows and learn to optimize your models with learning rate schedulers.
Overview
Syllabus
PyTorch Tutorial 01 - Installation.
PyTorch Tutorial 02 - Tensor Basics.
PyTorch Tutorial 03 - Gradient Calculation With Autograd.
PyTorch Tutorial 04 - Backpropagation - Theory With Example.
PyTorch Tutorial 05 - Gradient Descent with Autograd and Backpropagation.
PyTorch Tutorial 06 - Training Pipeline: Model, Loss, and Optimizer.
PyTorch Tutorial 07 - Linear Regression.
PyTorch Tutorial 08 - Logistic Regression.
PyTorch Tutorial 09 - Dataset and DataLoader - Batch Training.
PyTorch Tutorial 10 - Dataset Transforms.
PyTorch Tutorial 11 - Softmax and Cross Entropy.
PyTorch Tutorial 12 - Activation Functions.
PyTorch Tutorial 13 - Feed-Forward Neural Network.
PyTorch Tutorial 14 - Convolutional Neural Network (CNN).
PyTorch Tutorial 15 - Transfer Learning.
PyTorch Tutorial 16 - How To Use The TensorBoard.
PyTorch Tutorial 17 - Saving and Loading Models.
Create & Deploy A Deep Learning App - PyTorch Model Deployment With Flask & Heroku.
PyTorch RNN Tutorial - Name Classification Using A Recurrent Neural Net.
PyTorch Tutorial - RNN & LSTM & GRU - Recurrent Neural Nets.
PyTorch Lightning Tutorial - Lightweight PyTorch Wrapper For ML Researchers.
PyTorch LR Scheduler - Adjust The Learning Rate For Better Results.
Taught by
Python Engineer
