What You will Learn?

• PyTorch Basics - Tensors & Gradients
• Introduction to Jupyter notebooks & Data Science in Python
• Creating vectors, matrices & Tensors in PyTorch
• Tensor operations and gradient computations
• Interoperability of PyTorch with Numpy
• Linear Regression & Gradient Descent
• Linear Regression from scratch using Tensor operations
• Weights, biases and the mean squared error loss function
• Gradient descent and model training with PyTorch Autograd
• Linear Regression using PyTorch built-ins (nn.Linear, nn.functional etc.)
• Logistic Regression for Image Classification
• Working with images from the MNIST dataset
• Training and validation dataset creation
• Softmax function and categorical cross entropy loss
• Model training, evaluation and sample predictions
• Feedforward Neural Networks & GPUs
• Working with cloud GPU platforms like Kaggle & Colab
• Creating a multilayer neural network using nn.Module
• Activation function, non-linearity and universal approximation theorem
• Moving with datasets and models to the GPU for faster training
• Image Classification using Convolutional Neural Networks
• Working with the 3-channel RGB images from the CIFAR10 dataset
• Introduction to Convolutions, kernels & features maps
• Underfitting, overfitting and techniques to improve model performance
• Data Augmentation, Regularization and Residual Networks
• Improving the dataset using data normalization and data augmentation
• Improving the model using residual connections and batch normalization
• Improving the training loop using learning rate annealing, weight decay and gradient clip
• Training a state of the art image classifier from scratch in 10 minutes
• Image Generation using Generative Adverserial Networks (GANs)
• Introduction to generative modeling and application of GANs
• Creating generator and discriminator neural networks
• Generating and evaluating fake images of handwritten digits
• Training the generator and discriminator in tandem and visualizing results