Programming Generative AI

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Programming Generative AI

磁力链接/BT种子简介

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文件列表

  • Lesson 2 PyTorch for the Impatient/016. 2.15 Linear Regression with PyTorch.mp4 136.2 MB
  • Lesson 6 Connecting Text and Images/016. 6.15 Playing with Prompts.mp4 126.6 MB
  • Lesson 1 The What, Why, and How of Generative AI/009. 1.8 Introduction to Google Colab.mp4 121.0 MB
  • Lesson 4 Demystifying Diffusion/005. 4.4 Generating Images with Diffusers Pipelines.mp4 102.3 MB
  • Lesson 4 Demystifying Diffusion/006. 4.5 Deconstructing the Diffusion Process.mp4 85.2 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/025. 7.24 Video-Driven Frame-by-Frame Generation with SDXL Turbo.mp4 82.6 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/024. 7.23 Text-Guided Image-to-Image Translation.mp4 76.2 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/018. 7.17 Depth and Edge-Guided Stable Diffusion with ControlNet.mp4 72.2 MB
  • Lesson 1 The What, Why, and How of Generative AI/002. 1.1 Generative AI in the Wild.mp4 70.8 MB
  • Lesson 4 Demystifying Diffusion/007. 4.6 Forward Process as Encoder.mp4 70.7 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/004. 7.3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors.mp4 66.6 MB
  • Lesson 2 PyTorch for the Impatient/018. 2.17 Layers and Activations with torch.nn.mp4 65.3 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/017. 7.16 Creating Edge and Depth Maps for Conditioning.mp4 61.2 MB
  • Lesson 1 The What, Why, and How of Generative AI/006. 1.5 Formalizing Generative Models.mp4 59.7 MB
  • Lesson 5 Generating and Encoding Text with Transformers/008. 5.7 Visualizing and Understanding Attention.mp4 59.0 MB
  • Lesson 2 PyTorch for the Impatient/009. 2.8 Effortless Backpropagation with torch.autograd.mp4 58.5 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/003. 7.2 Manual Evaluation of Stable Diffusion with DrawBench.mp4 56.8 MB
  • Lesson 2 PyTorch for the Impatient/011. 2.10 Working with Devices.mp4 56.2 MB
  • Lesson 5 Generating and Encoding Text with Transformers/009. 5.8 Turning Words into Vectors.mp4 54.3 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/015. 7.14 Inference with Dreambooth to Create Personalized AI Avatars.mp4 53.6 MB
  • Lesson 3 Latent Space Rules Everything Around Me/005. 3.4 Working with Images in Python.mp4 53.5 MB
  • Lesson 4 Demystifying Diffusion/009. 4.8 Interpolating Diffusion Models.mp4 51.7 MB
  • Lesson 1 The What, Why, and How of Generative AI/005. 1.4 How Machines Create.mp4 51.6 MB
  • Lesson 5 Generating and Encoding Text with Transformers/004. 5.3 Generating Text with Transformers Pipelines.mp4 50.4 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/014. 7.13 Dreambooth Fine-Tuning with Hugging Face.mp4 49.9 MB
  • Lesson 2 PyTorch for the Impatient/019. 2.18 Multi-layer Feedforward Neural Networks (MLP).mp4 48.9 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/008. 7.7 Parameter Efficient Fine-Tuning with LoRA.mp4 47.6 MB
  • Lesson 5 Generating and Encoding Text with Transformers/002. 5.1 The Natural Language Processing Pipeline.mp4 46.7 MB
  • Lesson 5 Generating and Encoding Text with Transformers/007. 5.6 Transformers are Just Latent Variable Models for Sequences.mp4 45.0 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/010. 7.9 Inference with LoRAs for Style-Specific Generation.mp4 44.6 MB
  • Lesson 1 The What, Why, and How of Generative AI/007. 1.6 Generative versus Discriminative Models.mp4 44.4 MB
  • Lesson 1 The What, Why, and How of Generative AI/004. 1.3 Multitudes of Media.mp4 43.4 MB
  • Lesson 6 Connecting Text and Images/005. 6.4 Embedding Text and Images with CLIP.mp4 43.2 MB
  • Lesson 6 Connecting Text and Images/007. 6.6 Semantic Image Search with CLIP.mp4 42.9 MB
  • Lesson 3 Latent Space Rules Everything Around Me/018. 3.17 Exploring Latent Space.mp4 42.6 MB
  • Lesson 3 Latent Space Rules Everything Around Me/007. 3.6 Convolutional Neural Networks in PyTorch.mp4 42.2 MB
  • Lesson 5 Generating and Encoding Text with Transformers/003. 5.2 Generative Models of Language.mp4 41.7 MB
  • Lesson 2 PyTorch for the Impatient/006. 2.5 Tensors in PyTorch.mp4 40.6 MB
  • Lesson 6 Connecting Text and Images/003. 6.2 Vision-Language Understanding.mp4 40.0 MB
  • Lesson 4 Demystifying Diffusion/011. 4.10 Image Restoration and Enhancement.mp4 39.9 MB
  • Lesson 6 Connecting Text and Images/012. 6.11 Stable Diffusion Deconstructed.mp4 39.6 MB
  • Lesson 5 Generating and Encoding Text with Transformers/006. 5.5 Decoding Strategies.mp4 39.5 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/023. 7.22 Comparing SDXL and SDXL Turbo.mp4 39.4 MB
  • Lesson 3 Latent Space Rules Everything Around Me/019. 3.18 Latent Space Interpolation and Attribute Vectors.mp4 39.3 MB
  • Lesson 2 PyTorch for the Impatient/003. 2.2 The PyTorch Layer Cake.mp4 38.5 MB
  • Lesson 3 Latent Space Rules Everything Around Me/008. 3.7 Components of a Latent Variable Model (LVM).mp4 38.3 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/019. 7.18 Understanding and Experimenting with ControlNet Parameters.mp4 37.6 MB
  • Lesson 3 Latent Space Rules Everything Around Me/017. 3.16 Training a VAE with PyTorch.mp4 37.2 MB
  • Lesson 3 Latent Space Rules Everything Around Me/002. 3.1 Representing Images as Tensors.mp4 36.7 MB
  • Lesson 3 Latent Space Rules Everything Around Me/016. 3.15 Transforming an Autoencoder into a VAE.mp4 36.6 MB
  • Lesson 2 PyTorch for the Impatient/008. 2.7 Backpropagation Is Just the Chain Rule.mp4 36.4 MB
  • Lesson 4 Demystifying Diffusion/004. 4.3 Diffusers and the Hugging Face Ecosystem.mp4 36.3 MB
  • Lesson 3 Latent Space Rules Everything Around Me/011. 3.10 Setting up a Training Loop.mp4 35.6 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/021. 7.20 Few Step Generation with Adversarial Diffusion Distillation (ADD).mp4 35.4 MB
  • Lesson 6 Connecting Text and Images/009. 6.8 Introduction to Latent Diffusion Models.mp4 35.1 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/012. 7.11 Subject-Specific Personalization with Dreambooth.mp4 34.7 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/011. 7.10 Conceptual Overview of Textual Inversion.mp4 34.7 MB
  • Lesson 3 Latent Space Rules Everything Around Me/013. 3.12 Look Ma, No Features!.mp4 34.5 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/026. 7.25 Near Real-Time Inference with PyTorch Performance Optimizations.mp4 33.7 MB
  • Lesson 6 Connecting Text and Images/013. 6.12 Writing Our Own Stable Diffusion Pipeline.mp4 33.3 MB
  • Lesson 2 PyTorch for the Impatient/017. 2.16 Perceptrons and Neurons.mp4 32.9 MB
  • Lesson 1 The What, Why, and How of Generative AI/008. 1.7 The Generative Modeling Trilemma.mp4 32.7 MB
  • Lesson 5 Generating and Encoding Text with Transformers/005. 5.4 Deconstructing Transformers Pipelines.mp4 32.0 MB
  • Lesson 5 Generating and Encoding Text with Transformers/011. 5.10 Embedding Sequences with Transformers.mp4 31.7 MB
  • Lesson 3 Latent Space Rules Everything Around Me/004. 3.3 Features of Convolutional Neural Networks.mp4 31.3 MB
  • Lesson 2 PyTorch for the Impatient/015. 2.14 Comparing Gradient Descent and SGD.mp4 30.6 MB
  • Lesson 6 Connecting Text and Images/011. 6.10 Failure Modes and Additional Tools.mp4 30.6 MB
  • Lesson 3 Latent Space Rules Everything Around Me/015. 3.14 Variational Inference Not Just for Autoencoders.mp4 30.3 MB
  • Lesson 4 Demystifying Diffusion/008. 4.7 Reverse Process as Decoder.mp4 29.9 MB
  • Lesson 4 Demystifying Diffusion/010. 4.9 Image-to-Image Translation with SDEdit.mp4 28.9 MB
  • Lesson 6 Connecting Text and Images/015. 6.14 Improving Generation with Guidance.mp4 27.3 MB
  • Lesson 2 PyTorch for the Impatient/007. 2.6 Introduction to Computational Graphs.mp4 26.3 MB
  • Introduction/001. Programming Generative AI Introduction.mp4 26.1 MB
  • Lesson 6 Connecting Text and Images/008. 6.7 Conditional Generative Models.mp4 25.9 MB
  • Lesson 2 PyTorch for the Impatient/013. 2.12 Introduction to Gradient Descent.mp4 25.4 MB
  • Lesson 5 Generating and Encoding Text with Transformers/010. 5.9 The Vector Space Model.mp4 25.3 MB
  • Lesson 2 PyTorch for the Impatient/004. 2.3 The Deep Learning Software Trilemma.mp4 25.2 MB
  • Lesson 1 The What, Why, and How of Generative AI/003. 1.2 Defining Generative AI.mp4 24.8 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/006. 7.5 Sourcing and Preparing Image Datasets for Fine-Tuning.mp4 24.7 MB
  • Lesson 5 Generating and Encoding Text with Transformers/012. 5.11 Computing the Similarity Between Embeddings.mp4 24.7 MB
  • Lesson 6 Connecting Text and Images/010. 6.9 The Latent Diffusion Model Architecture.mp4 24.6 MB
  • Lesson 2 PyTorch for the Impatient/012. 2.11 Components of a Learning Algorithm.mp4 24.5 MB
  • Lesson 5 Generating and Encoding Text with Transformers/013. 5.12 Semantic Search with Embeddings.mp4 24.4 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/005. 7.4 Overview of Methods for Fine-Tuning Diffusion Models.mp4 23.9 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/013. 7.12 Dreambooth versus LoRA Fine-Tuning.mp4 23.9 MB
  • Lesson 3 Latent Space Rules Everything Around Me/003. 3.2 Desiderata for Computer Vision.mp4 23.6 MB
  • Lesson 2 PyTorch for the Impatient/005. 2.4 What Are Tensors, Really.mp4 23.5 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/002. 7.1 Methods and Metrics for Evaluating Generative AI.mp4 23.4 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/007. 7.6 Generating Automatic Captions with BLIP-2.mp4 22.5 MB
  • Lesson 6 Connecting Text and Images/004. 6.3 Contrastive Language-Image Pretraining.mp4 21.8 MB
  • Lesson 5 Generating and Encoding Text with Transformers/014. 5.13 Contrastive Embeddings with Sentence Transformers.mp4 21.2 MB
  • Lesson 3 Latent Space Rules Everything Around Me/010. 3.9 Defining an Autoencoder with PyTorch.mp4 21.1 MB
  • Lesson 4 Demystifying Diffusion/003. 4.2 Sampling as Iterative Denoising.mp4 20.9 MB
  • Lesson 3 Latent Space Rules Everything Around Me/009. 3.8 The Humble Autoencoder.mp4 20.9 MB
  • Lesson 3 Latent Space Rules Everything Around Me/012. 3.11 Inference with an Autoencoder.mp4 19.0 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/022. 7.21 Reasons to Distill.mp4 18.9 MB
  • Lesson 2 PyTorch for the Impatient/002. 2.1 What Is PyTorch.mp4 18.6 MB
  • Lesson 3 Latent Space Rules Everything Around Me/014. 3.13 Adding Probability to Autoencoders (VAE).mp4 18.4 MB
  • Lesson 4 Demystifying Diffusion/002. 4.1 Generation as a Reversible Process.mp4 18.1 MB
  • Lesson 3 Latent Space Rules Everything Around Me/006. 3.5 The FashionMNIST Dataset.mp4 17.7 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/016. 7.15 Adding Conditional Control to Text-to-Image Diffusion Models.mp4 17.1 MB
  • Lesson 6 Connecting Text and Images/002. 6.1 Components of a Multimodal Model.mp4 16.8 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/009. 7.8 Inspecting the Results of Fine-Tuning.mp4 16.8 MB
  • Lesson 2 PyTorch for the Impatient/014. 2.13 Getting to Stochastic Gradient Descent (SGD).mp4 15.7 MB
  • Lesson 6 Connecting Text and Images/014. 6.13 Decoding Images from the Stable Diffusion Latent Space.mp4 14.7 MB
  • Lesson 2 PyTorch for the Impatient/010. 2.9 PyTorch's Device Abstraction (i.e., GPUs).mp4 13.0 MB
  • Lesson 6 Connecting Text and Images/006. 6.5 Zero-Shot Image Classification with CLIP.mp4 12.5 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/020. 7.19 Generative Text Effects with Font Depth Maps.mp4 7.4 MB
  • Summary/001. Programming Generative AI Summary.mp4 5.0 MB
  • Lesson 3 Latent Space Rules Everything Around Me/001. Topics.mp4 4.8 MB
  • Lesson 4 Demystifying Diffusion/001. Topics.mp4 4.7 MB
  • Lesson 2 PyTorch for the Impatient/001. Topics.mp4 4.5 MB
  • Lesson 7 Post-Training Procedures for Diffusion Models/001. Topics.mp4 4.5 MB
  • Lesson 6 Connecting Text and Images/001. Topics.mp4 4.4 MB
  • Lesson 5 Generating and Encoding Text with Transformers/001. Topics.mp4 4.2 MB
  • Lesson 1 The What, Why, and How of Generative AI/001. Topics.mp4 4.0 MB
  • Lesson 6 Connecting Text and Images/016. 6.15 Playing with Prompts.en.srt 41.4 kB
  • Lesson 4 Demystifying Diffusion/005. 4.4 Generating Images with Diffusers Pipelines.en.srt 36.8 kB
  • Lesson 5 Generating and Encoding Text with Transformers/008. 5.7 Visualizing and Understanding Attention.en.srt 32.6 kB
  • Lesson 2 PyTorch for the Impatient/016. 2.15 Linear Regression with PyTorch.en.srt 29.8 kB
  • Lesson 4 Demystifying Diffusion/006. 4.5 Deconstructing the Diffusion Process.en.srt 25.5 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/004. 7.3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors.en.srt 25.5 kB
  • Lesson 1 The What, Why, and How of Generative AI/009. 1.8 Introduction to Google Colab.en.srt 25.0 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/024. 7.23 Text-Guided Image-to-Image Translation.en.srt 23.0 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/018. 7.17 Depth and Edge-Guided Stable Diffusion with ControlNet.en.srt 22.8 kB
  • Lesson 4 Demystifying Diffusion/007. 4.6 Forward Process as Encoder.en.srt 22.3 kB
  • Lesson 2 PyTorch for the Impatient/008. 2.7 Backpropagation Is Just the Chain Rule.en.srt 21.4 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/017. 7.16 Creating Edge and Depth Maps for Conditioning.en.srt 20.5 kB
  • Lesson 5 Generating and Encoding Text with Transformers/004. 5.3 Generating Text with Transformers Pipelines.en.srt 20.2 kB
  • Lesson 3 Latent Space Rules Everything Around Me/017. 3.16 Training a VAE with PyTorch.en.srt 19.9 kB
  • Lesson 6 Connecting Text and Images/005. 6.4 Embedding Text and Images with CLIP.en.srt 19.4 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/003. 7.2 Manual Evaluation of Stable Diffusion with DrawBench.en.srt 19.0 kB
  • Lesson 3 Latent Space Rules Everything Around Me/019. 3.18 Latent Space Interpolation and Attribute Vectors.en.srt 18.5 kB
  • Lesson 3 Latent Space Rules Everything Around Me/016. 3.15 Transforming an Autoencoder into a VAE.en.srt 18.4 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/014. 7.13 Dreambooth Fine-Tuning with Hugging Face.en.srt 18.2 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/015. 7.14 Inference with Dreambooth to Create Personalized AI Avatars.en.srt 18.1 kB
  • Lesson 5 Generating and Encoding Text with Transformers/006. 5.5 Decoding Strategies.en.srt 17.7 kB
  • Lesson 5 Generating and Encoding Text with Transformers/002. 5.1 The Natural Language Processing Pipeline.en.srt 17.3 kB
  • Lesson 1 The What, Why, and How of Generative AI/006. 1.5 Formalizing Generative Models.en.srt 17.2 kB
  • Lesson 3 Latent Space Rules Everything Around Me/018. 3.17 Exploring Latent Space.en.srt 17.0 kB
  • Lesson 5 Generating and Encoding Text with Transformers/007. 5.6 Transformers are Just Latent Variable Models for Sequences.en.srt 16.9 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/023. 7.22 Comparing SDXL and SDXL Turbo.en.srt 16.4 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/025. 7.24 Video-Driven Frame-by-Frame Generation with SDXL Turbo.en.srt 16.4 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/010. 7.9 Inference with LoRAs for Style-Specific Generation.en.srt 16.1 kB
  • Lesson 2 PyTorch for the Impatient/007. 2.6 Introduction to Computational Graphs.en.srt 16.0 kB
  • Lesson 6 Connecting Text and Images/012. 6.11 Stable Diffusion Deconstructed.en.srt 15.9 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/008. 7.7 Parameter Efficient Fine-Tuning with LoRA.en.srt 15.8 kB
  • Lesson 1 The What, Why, and How of Generative AI/004. 1.3 Multitudes of Media.en.srt 15.3 kB
  • Lesson 2 PyTorch for the Impatient/009. 2.8 Effortless Backpropagation with torch.autograd.en.srt 15.2 kB
  • Lesson 6 Connecting Text and Images/013. 6.12 Writing Our Own Stable Diffusion Pipeline.en.srt 14.9 kB
  • Lesson 4 Demystifying Diffusion/011. 4.10 Image Restoration and Enhancement.en.srt 14.7 kB
  • Lesson 3 Latent Space Rules Everything Around Me/007. 3.6 Convolutional Neural Networks in PyTorch.en.srt 14.5 kB
  • Lesson 6 Connecting Text and Images/007. 6.6 Semantic Image Search with CLIP.en.srt 14.5 kB
  • Lesson 2 PyTorch for the Impatient/018. 2.17 Layers and Activations with torch.nn.en.srt 14.3 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/026. 7.25 Near Real-Time Inference with PyTorch Performance Optimizations.en.srt 14.2 kB
  • Lesson 1 The What, Why, and How of Generative AI/005. 1.4 How Machines Create.en.srt 14.2 kB
  • Lesson 5 Generating and Encoding Text with Transformers/009. 5.8 Turning Words into Vectors.en.srt 13.7 kB
  • Lesson 3 Latent Space Rules Everything Around Me/005. 3.4 Working with Images in Python.en.srt 13.6 kB
  • Lesson 5 Generating and Encoding Text with Transformers/011. 5.10 Embedding Sequences with Transformers.en.srt 13.4 kB
  • Lesson 2 PyTorch for the Impatient/003. 2.2 The PyTorch Layer Cake.en.srt 12.8 kB
  • Lesson 6 Connecting Text and Images/003. 6.2 Vision-Language Understanding.en.srt 12.8 kB
  • Lesson 2 PyTorch for the Impatient/011. 2.10 Working with Devices.en.srt 12.6 kB
  • Lesson 1 The What, Why, and How of Generative AI/007. 1.6 Generative versus Discriminative Models.en.srt 12.6 kB
  • Lesson 5 Generating and Encoding Text with Transformers/003. 5.2 Generative Models of Language.en.srt 12.5 kB
  • Lesson 4 Demystifying Diffusion/009. 4.8 Interpolating Diffusion Models.en.srt 12.5 kB
  • Lesson 6 Connecting Text and Images/015. 6.14 Improving Generation with Guidance.en.srt 12.2 kB
  • Lesson 3 Latent Space Rules Everything Around Me/011. 3.10 Setting up a Training Loop.en.srt 12.1 kB
  • Lesson 1 The What, Why, and How of Generative AI/002. 1.1 Generative AI in the Wild.en.srt 12.1 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/005. 7.4 Overview of Methods for Fine-Tuning Diffusion Models.en.srt 12.0 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/019. 7.18 Understanding and Experimenting with ControlNet Parameters.en.srt 11.4 kB
  • Lesson 2 PyTorch for the Impatient/006. 2.5 Tensors in PyTorch.en.srt 11.4 kB
  • Lesson 3 Latent Space Rules Everything Around Me/013. 3.12 Look Ma, No Features!.en.srt 11.3 kB
  • Lesson 6 Connecting Text and Images/009. 6.8 Introduction to Latent Diffusion Models.en.srt 11.2 kB
  • Lesson 3 Latent Space Rules Everything Around Me/008. 3.7 Components of a Latent Variable Model (LVM).en.srt 10.9 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/007. 7.6 Generating Automatic Captions with BLIP-2.en.srt 10.8 kB
  • Lesson 5 Generating and Encoding Text with Transformers/005. 5.4 Deconstructing Transformers Pipelines.en.srt 10.7 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/006. 7.5 Sourcing and Preparing Image Datasets for Fine-Tuning.en.srt 10.3 kB
  • Lesson 3 Latent Space Rules Everything Around Me/002. 3.1 Representing Images as Tensors.en.srt 10.3 kB
  • Lesson 2 PyTorch for the Impatient/019. 2.18 Multi-layer Feedforward Neural Networks (MLP).en.srt 10.1 kB
  • Lesson 4 Demystifying Diffusion/010. 4.9 Image-to-Image Translation with SDEdit.en.srt 10.0 kB
  • Lesson 4 Demystifying Diffusion/008. 4.7 Reverse Process as Decoder.en.srt 9.9 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/011. 7.10 Conceptual Overview of Textual Inversion.en.srt 9.9 kB
  • Lesson 3 Latent Space Rules Everything Around Me/004. 3.3 Features of Convolutional Neural Networks.en.srt 9.7 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/012. 7.11 Subject-Specific Personalization with Dreambooth.en.srt 9.7 kB
  • Lesson 3 Latent Space Rules Everything Around Me/015. 3.14 Variational Inference Not Just for Autoencoders.en.srt 9.6 kB
  • Lesson 5 Generating and Encoding Text with Transformers/010. 5.9 The Vector Space Model.en.srt 9.6 kB
  • Lesson 5 Generating and Encoding Text with Transformers/012. 5.11 Computing the Similarity Between Embeddings.en.srt 9.6 kB
  • Lesson 1 The What, Why, and How of Generative AI/008. 1.7 The Generative Modeling Trilemma.en.srt 9.5 kB
  • Lesson 2 PyTorch for the Impatient/017. 2.16 Perceptrons and Neurons.en.srt 9.2 kB
  • Lesson 2 PyTorch for the Impatient/012. 2.11 Components of a Learning Algorithm.en.srt 9.1 kB
  • Lesson 5 Generating and Encoding Text with Transformers/014. 5.13 Contrastive Embeddings with Sentence Transformers.en.srt 8.9 kB
  • Lesson 6 Connecting Text and Images/011. 6.10 Failure Modes and Additional Tools.en.srt 8.9 kB
  • Lesson 4 Demystifying Diffusion/004. 4.3 Diffusers and the Hugging Face Ecosystem.en.srt 8.8 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/021. 7.20 Few Step Generation with Adversarial Diffusion Distillation (ADD).en.srt 8.7 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/002. 7.1 Methods and Metrics for Evaluating Generative AI.en.srt 8.5 kB
  • Lesson 5 Generating and Encoding Text with Transformers/013. 5.12 Semantic Search with Embeddings.en.srt 8.4 kB
  • Lesson 2 PyTorch for the Impatient/004. 2.3 The Deep Learning Software Trilemma.en.srt 8.3 kB
  • Introduction/001. Programming Generative AI Introduction.en.srt 8.2 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/013. 7.12 Dreambooth versus LoRA Fine-Tuning.en.srt 8.1 kB
  • Lesson 6 Connecting Text and Images/004. 6.3 Contrastive Language-Image Pretraining.en.srt 7.7 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/022. 7.21 Reasons to Distill.en.srt 7.6 kB
  • Lesson 1 The What, Why, and How of Generative AI/003. 1.2 Defining Generative AI.en.srt 7.4 kB
  • Lesson 2 PyTorch for the Impatient/013. 2.12 Introduction to Gradient Descent.en.srt 7.4 kB
  • Lesson 2 PyTorch for the Impatient/015. 2.14 Comparing Gradient Descent and SGD.en.srt 7.3 kB
  • Lesson 3 Latent Space Rules Everything Around Me/010. 3.9 Defining an Autoencoder with PyTorch.en.srt 7.3 kB
  • Lesson 6 Connecting Text and Images/010. 6.9 The Latent Diffusion Model Architecture.en.srt 7.3 kB
  • Lesson 6 Connecting Text and Images/002. 6.1 Components of a Multimodal Model.en.srt 7.1 kB
  • Lesson 6 Connecting Text and Images/008. 6.7 Conditional Generative Models.en.srt 6.9 kB
  • Lesson 3 Latent Space Rules Everything Around Me/009. 3.8 The Humble Autoencoder.en.srt 6.8 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/009. 7.8 Inspecting the Results of Fine-Tuning.en.srt 6.6 kB
  • Lesson 2 PyTorch for the Impatient/005. 2.4 What Are Tensors, Really.en.srt 6.5 kB
  • Lesson 4 Demystifying Diffusion/002. 4.1 Generation as a Reversible Process.en.srt 6.5 kB
  • Lesson 3 Latent Space Rules Everything Around Me/014. 3.13 Adding Probability to Autoencoders (VAE).en.srt 6.4 kB
  • Lesson 3 Latent Space Rules Everything Around Me/003. 3.2 Desiderata for Computer Vision.en.srt 6.3 kB
  • Lesson 3 Latent Space Rules Everything Around Me/006. 3.5 The FashionMNIST Dataset.en.srt 5.8 kB
  • Lesson 3 Latent Space Rules Everything Around Me/012. 3.11 Inference with an Autoencoder.en.srt 5.8 kB
  • Lesson 6 Connecting Text and Images/014. 6.13 Decoding Images from the Stable Diffusion Latent Space.en.srt 5.6 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/016. 7.15 Adding Conditional Control to Text-to-Image Diffusion Models.en.srt 5.6 kB
  • Lesson 4 Demystifying Diffusion/003. 4.2 Sampling as Iterative Denoising.en.srt 5.5 kB
  • Lesson 2 PyTorch for the Impatient/014. 2.13 Getting to Stochastic Gradient Descent (SGD).en.srt 5.5 kB
  • Lesson 2 PyTorch for the Impatient/002. 2.1 What Is PyTorch.en.srt 5.3 kB
  • Lesson 2 PyTorch for the Impatient/010. 2.9 PyTorch's Device Abstraction (i.e., GPUs).en.srt 5.2 kB
  • Lesson 6 Connecting Text and Images/006. 6.5 Zero-Shot Image Classification with CLIP.en.srt 4.8 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/020. 7.19 Generative Text Effects with Font Depth Maps.en.srt 3.7 kB
  • Summary/001. Programming Generative AI Summary.en.srt 1.4 kB
  • Lesson 4 Demystifying Diffusion/001. Topics.en.srt 1.3 kB
  • Lesson 2 PyTorch for the Impatient/001. Topics.en.srt 1.2 kB
  • Lesson 5 Generating and Encoding Text with Transformers/001. Topics.en.srt 1.2 kB
  • Lesson 3 Latent Space Rules Everything Around Me/001. Topics.en.srt 1.2 kB
  • Lesson 6 Connecting Text and Images/001. Topics.en.srt 1.1 kB
  • Lesson 1 The What, Why, and How of Generative AI/001. Topics.en.srt 1.1 kB
  • Lesson 7 Post-Training Procedures for Diffusion Models/001. Topics.en.srt 1.0 kB

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