Mastering Generative AI with PyTorch: Hands-on Experience

Explore how gradient descent minimizes the cost and reaches a local minimum, tune learning rates, compare batch and stochastic gradient descent, and review backpropagation in a simple feedforward neural network.

Explore how long short-term memory networks solve long-term dependency problems in recurrent neural networks, detailing forget gates, update gates, and cell state dynamics for time series modeling in PyTorch.

Install CUDA toolkit 11.7 locally for PyTorch on Windows or Linux, following the start locally guide, verify installation, and note that Mac cannot use CUDA in PyTorch outside Colab.

Learn to build a simple generative AI for image generation in Colab using PyTorch, torchvision, and MNIST data, converting images to tensors and preparing a train data loader.

Train the discriminator by generating fake data from random noise through the generator, compare real and fake data with labeled targets, and update the discriminator via backpropagation and its optimizer.

Learn to prepare time series temperature data for a PyTorch-based generative AI model by building 30-day sequences, converting to tensors, and creating a data loader.

Train the generator by sampling noise, producing fake data, evaluating it with the discriminator, and backpropagating BCE loss to update the generator via its optimizer.

Learn to evaluate a training process by computing and printing the average generator and discriminator losses per epoch, using a data loader, and preparing for synthetic data generation.

Train a generative AI model to produce synthetic temperature data by passing random noise to the generator, then compare actual and generated data and assess with MSE, MAE, and correlation.

Prepare data for conditional gan training by downloading, extracting, and organizing the 102-category Oxford flowers dataset, including image labels and PyTorch data pipelines in Colab.

Develop a conditional discriminator that mirrors the generator and uses a label embedding, flattening the generated image and concatenating the label before a leaky ReLU classifier.

Prepare data for the discriminator and generator, then run their training loops in PyTorch. Define latent time, label time, 102 classes, and 100 epochs.

Learn to generate and display images with a conditional convolutional GAN in PyTorch, from noise and label setup to evaluating convolutional versus linear outputs.

Explore ethics in generative AI across the model life cycle, focusing on privacy, security, fairness, accountability, and avoiding misinformation and deepfakes.