Programming Generative AI - Printable Version

Files Included :
1 1 Generative AI in the Wild (67.53 MB)
1 2 Defining Generative AI (23.61 MB)
1 3 Multitudes of Media (41.42 MB)
1 4 How Machines Create (49.17 MB)
1 5 Formalizing Generative Models (56.96 MB)
1 6 Generative versus Discriminative Models (42.33 MB)
1 7 The Generative Modeling Trilemma (31.2 MB)
1 8 Introduction to Google Colab (115.35 MB)
2 10 Working with Devices (53.56 MB)
2 11 Components of a Learning Algorithm (23.35 MB)
2 12 Introduction to Gradient Descent (24.2 MB)
2 13 Getting to Stochastic Gradient Descent (SGD) (15 MB)
2 14 Comparing Gradient Descent and SGD (29.22 MB)
2 15 Linear Regression with PyTorch (129.91 MB)
2 16 Perceptrons and Neurons (31.42 MB)
2 17 Layers and Activations with torch nn (62.29 MB)
2 18 Multi-layer Feedforward Neural Networks (MLP) (46.68 MB)
2 1 What Is PyTorch (17.78 MB)
2 2 The PyTorch Layer Cake (36.72 MB)
2 3 The Deep Learning Software Trilemma (24 MB)
2 4 What Are Tensors, Really (22.42 MB)
2 5 Tensors in PyTorch (38.73 MB)
2 6 Introduction to Computational Graphs (25.07 MB)
2 7 Backpropagation Is Just the Chain Rule (34.72 MB)
2 8 Effortless Backpropagation with torch autograd (55.79 MB)
2 9 PyTorch's Device Abstraction (i e , GPUs) (12.4 MB)
3 10 Setting up a Training Loop (33.95 MB)
3 11 Inference with an Autoencoder (18.12 MB)
3 12 Look Ma, No Features! (32.93 MB)
3 13 Adding Probability to Autoencoders (VAE) (17.57 MB)
3 14 Variational Inference Not Just for Autoencoders (28.92 MB)
3 15 Transforming an Autoencoder into a VAE (34.86 MB)
3 16 Training a VAE with PyTorch (35.49 MB)
3 17 Exploring Latent Space (40.63 MB)
3 18 Latent Space Interpolation and Attribute Vectors (37.49 MB)
3 1 Representing Images as Tensors (35.04 MB)
3 2 Desiderata for Computer Vision (22.46 MB)
3 3 Features of Convolutional Neural Networks (29.83 MB)
3 4 Working with Images in Python (51.03 MB)
3 5 The FashionMNIST Dataset (16.92 MB)
3 6 Convolutional Neural Networks in PyTorch (40.25 MB)
3 7 Components of a Latent Variable Model (LVM) (36.54 MB)
3 8 The Humble Autoencoder (19.92 MB)
3 9 Defining an Autoencoder with PyTorch (20.11 MB)
4 10 Image Restoration and Enhancement (38.06 MB)
4 1 Generation as a Reversible Process (17.29 MB)
4 2 Sampling as Iterative Denoising (19.96 MB)
4 3 Diffusers and the Hugging Face Ecosystem (34.64 MB)
4 4 Generating Images with Diffusers Pipelines (97.61 MB)
4 5 Deconstructing the Diffusion Process (81.29 MB)
4 6 Forward Process as Encoder (67.45 MB)
4 7 Reverse Process as Decoder (28.51 MB)
4 8 Interpolating Diffusion Models (49.31 MB)
4 9 Image-to-Image Translation with SDEdit (27.56 MB)
5 10 Embedding Sequences with Transformers (30.25 MB)
5 11 Computing the Similarity Between Embeddings (23.57 MB)
5 12 Semantic Search with Embeddings (23.3 MB)
5 13 Contrastive Embeddings with Sentence Transformers (20.23 MB)
5 1 The Natural Language Processing Pipeline (44.54 MB)
5 2 Generative Models of Language (39.8 MB)
5 3 Generating Text with Transformers Pipelines (48.1 MB)
5 4 Deconstructing Transformers Pipelines (30.54 MB)
5 5 Decoding Strategies (37.7 MB)
5 6 Transformers are Just Latent Variable Models for Sequences (42.94 MB)
5 7 Visualizing and Understanding Attention (56.29 MB)
5 8 Turning Words into Vectors (51.75 MB)
5 9 The Vector Space Model (24.15 MB)
6 10 Failure Modes and Additional Tools (29.2 MB)
6 11 Stable Diffusion Deconstructed (37.8 MB)
6 12 Writing Our Own Stable Diffusion Pipeline (31.76 MB)
6 13 Decoding Images from the Stable Diffusion Latent Space (14.03 MB)
6 14 Improving Generation with Guidance (26.07 MB)
6 15 Playing with Prompts (120.71 MB)
6 1 Components of a Multimodal Model (16.06 MB)
6 2 Vision-Language Understanding (38.14 MB)
6 3 Contrastive Language-Image Pretraining (20.81 MB)
6 4 Embedding Text and Images with CLIP (41.24 MB)
6 5 Zero-Shot Image Classification with CLIP (11.95 MB)
6 6 Semantic Image Search with CLIP (40.9 MB)
6 7 Conditional Generative Models (24.74 MB)
6 8 Introduction to Latent Diffusion Models (33.43 MB)
6 9 The Latent Diffusion Model Architecture (23.43 MB)
7 10 Conceptual Overview of Textual Inversion (33.09 MB)
7 11 Subject-Specific Personalization with Dreambooth (33.14 MB)
7 12 Dreambooth versus LoRA Fine-Tuning (22.83 MB)
7 13 Dreambooth Fine-Tuning with Hugging Face (47.62 MB)
7 14 Inference with Dreambooth to Create Personalized AI Avatars (51.16 MB)
7 15 Adding Conditional Control to Text-to-Image Diffusion Models (16.3 MB)
7 16 Creating Edge and Depth Maps for Conditioning (58.39 MB)
7 17 Depth and Edge-Guided Stable Diffusion with ControlNet (68.81 MB)
7 18 Understanding and Experimenting with ControlNet Parameters (35.82 MB)
7 19 Generative Text Effects with Font Depth Maps (7.07 MB)
7 1 Methods and Metrics for Evaluating Generative AI (22.35 MB)
7 20 Few Step Generation with Adversarial Diffusion Distillation (ADD) (33.79 MB)
7 21 Reasons to Distill (18.06 MB)
7 22 Comparing SDXL and SDXL Turbo (37.58 MB)
7 23 Text-Guided Image-to-Image Translation (72.66 MB)
7 24 Video-Driven Frame-by-Frame Generation with SDXL Turbo (78.73 MB)
7 25 Near Real-Time Inference with PyTorch Performance Optimizations (32.17 MB)
7 2 Manual Evaluation of Stable Diffusion with DrawBench (54.21 MB)
7 3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors (63.47 MB)
7 4 Overview of Methods for Fine-Tuning Diffusion Models (22.83 MB)
7 5 Sourcing and Preparing Image Datasets for Fine-Tuning (23.58 MB)
7 6 Generating Automatic Captions with BLIP-2 (21.46 MB)
7 7 Parameter Efficient Fine-Tuning with LoRA (45.43 MB)
7 8 Inspecting the Results of Fine-Tuning (16.02 MB)
7 9 Inference with LoRAs for Style-Specific Generation (42.53 MB)
Programming Generative AI Introduction (1) (24.87 MB)
Programming Generative AI Introduction (24.87 MB)
Programming Generative AI Summary (4.81 MB)
Topics (1) (4.27 MB)
Topics (2) (4.54 MB)
Topics (3) (4.52 MB)
Topics (4) (4.01 MB)
Topics (5) (4.22 MB)
Topics (6) (4.25 MB)
Topics (3.83 MB)]
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