Mastering Generative AI with PyTorch: Hands-on Experience

Explore the definition and scope of generative AI, compare it with traditional AI, and highlight data generation, distribution, and key applications like image synthesis and text generation.

Explore the history of generative AI, from probabilistic models and Bayesian networks to neural networks, GANs, and transformers, with notes on robustness and ethics.

Explore the diverse applications of generative AI, including image and video generation, deepfake, text generation and chatbots, audio and music synthesis, content creation, data augmentation, across industries.

Explore the main generative architectural networks in generative AI, including GANs, VAEs, autoregressive and diffusion models, and transformer-based GPT, with a focus on why GANs produce high-quality, realistic outputs.

Explore the architecture of generative adversarial networks, pitting a generator against a discriminator in a real-world art-forger analogy, with iterative feedback to produce convincing synthetic data.

Explore how a generator turns a noise vector into synthetic data and a discriminator judges real vs fake, with learning via backpropagation and cross-entropy in PyTorch.

Explore Google Colab by opening the browser, creating a new notebook, and running code cells. Save, upload notebooks, and use keyboard shortcuts while troubleshooting with Stack Overflow.

Explore neuron structure, including dendrites, cell body, axon, and terminals, and learn how signals move from input to output through weights and bias with activation functions.

Explore activation functions such as threshold, sigmoid, tanh, and ReLU and how they pass signals, shape outputs, and guide learning in neural networks.

Explore how a neural network processes inputs through input, hidden, and output layers with realistic sparse connections, and learn to minimize the cost function via backpropagation across all samples.

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 recurrent neural networks use feedback loops to remember past inputs. Compare them with feedforward nets and learn about backpropagation through time.

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.

Explore how computers read images by converting pixels into grayscale 0–255 values and RGB channels into three matrices, building intuition for convolutional neural networks.

Learn how convolutional neural networks use three by three filters to extract image features, produce feature maps, and apply stride and padding with the size formula to control dimensions.

Explore how a convolutional layer yields a 5x5 feature map from a 7x7 image without padding, then max pool with a 2x2 window and stride 2 to downsample.

Learn how convolutional layers produce multiple pooled feature maps and how flattening reshapes these maps into a one-dimensional vector for fully connected layers in CNNs.

Learn how an input image passes through convolutional and pooling layers, activates non-linearity with ReLU, then flattens and connects to a fully connected layer.

Explore PyTorch tensors as n-dimensional data structures similar to numpy arrays, with CPU and GPU (CUDA) support, enabling efficient image and natural language processing computations for neural networks.

learn how to create NumPy arrays and PyTorch tensors, convert between them with torch.from_numpy and torch.tensor, and understand memory sharing versus copying 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.

Import libraries, load data into a 60,000-image train set, inspect a 28 by 28 grayscale tensor and label, then create a 64-batch, shuffled train loader for the generative AI model.

Learn to build a modular generative AI by defining a gen block in pytorch, then compose generator and discriminator blocks with reusable neural networks and forward passes.

Build a PyTorch generator from gen blocks, stacking sequential layers to transform a 100-value random noise vector into a 28 by 28 image, with tanh output.

Develop the DisBlock to power a GAN discriminator, reusing the gen block structure with a linear layer, leaky ReLU activation, and dropout to prevent overfitting.

Develop a discriminator class using torch nn.Module, mirroring the generator with sequential blocks, flattening the generator's 28x28 output to 784, and ending with a sigmoid for real vs fake.

Initialize a generator and discriminator in PyTorch, set up binary cross-entropy loss, and configure two optimizers with a 0.0002 learning rate for gan training.

Configure the training loop with epochs and fixed noise, prepare data via a trainloader, and train both discriminator and generator in a structured, sectioned GAN workflow.

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.

Train the generator by passing fake data to the discriminator, backpropagating through the generator, and updating with optimizer G toward the real-label loss.

Visualize and generate images with a PyTorch GAN, tracking epoch and batch discriminator and generator losses, and render fixed-noise image grids to monitor progress.

Explore time series synthetic data generation with generative ai and gans, learn how generator and discriminator create realistic data for data augmentation, privacy-preserving sharing, and rare-event testing.

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.

Develop a generator using a two-layer LSTM in PyTorch for time series, paired with a discriminator, featuring batch-first inputs and a final fully connected layer.

Develop the discriminator by adapting the generator with an LSTM to output a single real or fake score. Use the last LSTM hidden state through a fully connected layer.

Initialize the generator and discriminator in pytorch, set input, hidden, and output dimensions, define BCE with logit loss, configure adam optimizers with 0.0001 learning rate, and prepare training loop parameters.

Implement a training loop for time series data in PyTorch, including epoch loops, data loader integration, and preparing real and fake labels to train the discriminator and generator.

Train the discriminator by calculating real and fake BCE with logits losses from real and generated data, sum them, and update parameters via backpropagation.

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.

Learn conditional GANs, an extension of GANs that feed conditional information to both generator and discriminator, enabling text‑to‑image generation, image translation, and image resolution enhancement.

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 PyTorch flower data set pre-processing class to load images and labels from mat files, apply optional transforms, and return RGB images with label tensors for training.

Create a transform to resize flower images to 64 by 64, convert to tensors, and normalize to -1 to 1; then build shuffled 64-batch dataset for a conditional generative model.

Develop a conditional generator in PyTorch for a generative adversarial network by embedding flower labels and concatenating them with a latent vector to produce 64x64 color images conditioned on labels.

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.

Initialize generator and discriminator from the conditional classes, passing latent time and label times to the generator and label time to the discriminator, and define BCE loss and Adam optimizers.

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

Train the discriminator by computing real and fake losses, averaging them, and updating its parameters with the optimizer, using random noise, label embeddings, and the generator.

Train the generator in a generative adversarial network by producing images from a latent vector and labels, optimizing against the discriminator with adversarial loss across epochs.

Test a trained conditional generative AI model in PyTorch by saving/loading the generator and discriminator, setting eval mode, and generating and displaying images from noise and a label.

Replace linear layers with convolutional layers in the conditional GAN generator and discriminator to improve image generation. Use conv transpose 2d, batch norm, and tanh to upsample and stabilize training.

Revisit the conditional discriminator and training loop in a PyTorch GAN, replacing linear layers with convTranspose2d and conv2d, adding label embedding and batchnorm, and optimizing for GPU training.

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.