Deep Learning With PyTorch - Full Course

Dive into a comprehensive 4.5-hour course on deep learning with PyTorch, covering fundamental concepts and practical implementations. Begin with PyTorch installation and tensor basics, then progress through autograd, backpropagation, and gradient descent. Explore the training pipeline, linear and logistic regression, and learn to work with datasets and dataloaders. Delve into advanced topics like softmax, cross-entropy, activation functions, and feed-forward networks. Master convolutional neural networks (CNNs) and transfer learning techniques. Conclude by learning to use TensorBoard for visualization and how to save and load models. Gain hands-on experience with code examples and follow along with the provided GitHub repository to enhance your deep learning skills using PyTorch.

- Intro
- 1 Installation
- 2 Tensor Basics
- 3 Autograd
- 4 Backpropagation
- 5 Gradient Descent
- 6 Training Pipeline
- 7 Linear Regression
- 8 Logistic Regression
- 9 Dataset and Dataloader
- 10 Dataset Transforms
- 11 Softmax and Crossentropy
- 12 Activation Functions
- 13 Feed Forward Net
- 14 CNN
- 15 Transfer Learning
- 16 Tensorboard
- 17 Save & Load Models