Programming Generative AI | A83722EB98ED43F80334C3FF41D57025808A2264

Programming Generative AI

Name	Programming Generative AI	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	4.3 GB
Total Files	232
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Hash	A83722EB98ED43F80334C3FF41D57025808A2264

/Lesson 2 PyTorch for the Impatient/
016. 2.15 Linear Regression with PyTorch.mp4	136.2 MB
001. Topics.en.srt	1.2 KB
001. Topics.mp4	4.5 MB
002. 2.1 What Is PyTorch.en.srt	5.3 KB
002. 2.1 What Is PyTorch.mp4	18.6 MB
003. 2.2 The PyTorch Layer Cake.en.srt	12.8 KB
003. 2.2 The PyTorch Layer Cake.mp4	38.5 MB
004. 2.3 The Deep Learning Software Trilemma.en.srt	8.3 KB
004. 2.3 The Deep Learning Software Trilemma.mp4	25.2 MB
005. 2.4 What Are Tensors, Really.en.srt	6.5 KB
005. 2.4 What Are Tensors, Really.mp4	23.5 MB
006. 2.5 Tensors in PyTorch.en.srt	11.4 KB
006. 2.5 Tensors in PyTorch.mp4	40.6 MB
007. 2.6 Introduction to Computational Graphs.en.srt	16.0 KB
007. 2.6 Introduction to Computational Graphs.mp4	26.3 MB
008. 2.7 Backpropagation Is Just the Chain Rule.en.srt	21.4 KB
008. 2.7 Backpropagation Is Just the Chain Rule.mp4	36.4 MB
009. 2.8 Effortless Backpropagation with torch.autograd.en.srt	15.2 KB
009. 2.8 Effortless Backpropagation with torch.autograd.mp4	58.5 MB
010. 2.9 PyTorch's Device Abstraction (i.e., GPUs).en.srt	5.2 KB
010. 2.9 PyTorch's Device Abstraction (i.e., GPUs).mp4	13.0 MB
011. 2.10 Working with Devices.en.srt	12.6 KB
011. 2.10 Working with Devices.mp4	56.2 MB
012. 2.11 Components of a Learning Algorithm.en.srt	9.1 KB
012. 2.11 Components of a Learning Algorithm.mp4	24.5 MB
013. 2.12 Introduction to Gradient Descent.en.srt	7.4 KB
013. 2.12 Introduction to Gradient Descent.mp4	25.4 MB
014. 2.13 Getting to Stochastic Gradient Descent (SGD).en.srt	5.5 KB
014. 2.13 Getting to Stochastic Gradient Descent (SGD).mp4	15.7 MB
015. 2.14 Comparing Gradient Descent and SGD.en.srt	7.3 KB
015. 2.14 Comparing Gradient Descent and SGD.mp4	30.6 MB
016. 2.15 Linear Regression with PyTorch.en.srt	29.8 KB
017. 2.16 Perceptrons and Neurons.en.srt	9.2 KB
017. 2.16 Perceptrons and Neurons.mp4	32.9 MB
018. 2.17 Layers and Activations with torch.nn.en.srt	14.3 KB
018. 2.17 Layers and Activations with torch.nn.mp4	65.3 MB
019. 2.18 Multi-layer Feedforward Neural Networks (MLP).en.srt	10.1 KB
019. 2.18 Multi-layer Feedforward Neural Networks (MLP).mp4	48.9 MB
/Introduction/
001. Programming Generative AI Introduction.mp4	26.1 MB
001. Programming Generative AI Introduction.en.srt	8.2 KB
/Lesson 1 The What, Why, and How of Generative AI/
001. Topics.en.srt	1.1 KB
001. Topics.mp4	4.0 MB
002. 1.1 Generative AI in the Wild.en.srt	12.1 KB
002. 1.1 Generative AI in the Wild.mp4	70.8 MB
003. 1.2 Defining Generative AI.en.srt	7.4 KB
003. 1.2 Defining Generative AI.mp4	24.8 MB
004. 1.3 Multitudes of Media.en.srt	15.3 KB
004. 1.3 Multitudes of Media.mp4	43.4 MB
005. 1.4 How Machines Create.en.srt	14.2 KB
005. 1.4 How Machines Create.mp4	51.6 MB
006. 1.5 Formalizing Generative Models.en.srt	17.2 KB
006. 1.5 Formalizing Generative Models.mp4	59.7 MB
007. 1.6 Generative versus Discriminative Models.en.srt	12.6 KB
007. 1.6 Generative versus Discriminative Models.mp4	44.4 MB
008. 1.7 The Generative Modeling Trilemma.en.srt	9.5 KB
008. 1.7 The Generative Modeling Trilemma.mp4	32.7 MB
009. 1.8 Introduction to Google Colab.en.srt	25.0 KB
009. 1.8 Introduction to Google Colab.mp4	121.0 MB
/Lesson 3 Latent Space Rules Everything Around Me/
001. Topics.en.srt	1.2 KB
001. Topics.mp4	4.8 MB
002. 3.1 Representing Images as Tensors.en.srt	10.3 KB
002. 3.1 Representing Images as Tensors.mp4	36.7 MB
003. 3.2 Desiderata for Computer Vision.en.srt	6.3 KB
003. 3.2 Desiderata for Computer Vision.mp4	23.6 MB
004. 3.3 Features of Convolutional Neural Networks.en.srt	9.7 KB
004. 3.3 Features of Convolutional Neural Networks.mp4	31.3 MB
005. 3.4 Working with Images in Python.en.srt	13.6 KB
005. 3.4 Working with Images in Python.mp4	53.5 MB
006. 3.5 The FashionMNIST Dataset.en.srt	5.8 KB
006. 3.5 The FashionMNIST Dataset.mp4	17.7 MB
007. 3.6 Convolutional Neural Networks in PyTorch.en.srt	14.5 KB
007. 3.6 Convolutional Neural Networks in PyTorch.mp4	42.2 MB
008. 3.7 Components of a Latent Variable Model (LVM).en.srt	10.9 KB
008. 3.7 Components of a Latent Variable Model (LVM).mp4	38.3 MB
009. 3.8 The Humble Autoencoder.en.srt	6.8 KB
009. 3.8 The Humble Autoencoder.mp4	20.9 MB
010. 3.9 Defining an Autoencoder with PyTorch.en.srt	7.3 KB
010. 3.9 Defining an Autoencoder with PyTorch.mp4	21.1 MB
011. 3.10 Setting up a Training Loop.en.srt	12.1 KB
011. 3.10 Setting up a Training Loop.mp4	35.6 MB
012. 3.11 Inference with an Autoencoder.en.srt	5.8 KB
012. 3.11 Inference with an Autoencoder.mp4	19.0 MB
013. 3.12 Look Ma, No Features!.en.srt	11.3 KB
013. 3.12 Look Ma, No Features!.mp4	34.5 MB
014. 3.13 Adding Probability to Autoencoders (VAE).en.srt	6.4 KB
014. 3.13 Adding Probability to Autoencoders (VAE).mp4	18.4 MB
015. 3.14 Variational Inference Not Just for Autoencoders.en.srt	9.6 KB
015. 3.14 Variational Inference Not Just for Autoencoders.mp4	30.3 MB
016. 3.15 Transforming an Autoencoder into a VAE.en.srt	18.4 KB
016. 3.15 Transforming an Autoencoder into a VAE.mp4	36.6 MB
017. 3.16 Training a VAE with PyTorch.en.srt	19.9 KB
017. 3.16 Training a VAE with PyTorch.mp4	37.2 MB
018. 3.17 Exploring Latent Space.en.srt	17.0 KB
018. 3.17 Exploring Latent Space.mp4	42.6 MB
019. 3.18 Latent Space Interpolation and Attribute Vectors.en.srt	18.5 KB
019. 3.18 Latent Space Interpolation and Attribute Vectors.mp4	39.3 MB
/Lesson 4 Demystifying Diffusion/
001. Topics.en.srt	1.3 KB
001. Topics.mp4	4.7 MB
002. 4.1 Generation as a Reversible Process.en.srt	6.5 KB
002. 4.1 Generation as a Reversible Process.mp4	18.1 MB
003. 4.2 Sampling as Iterative Denoising.en.srt	5.5 KB
003. 4.2 Sampling as Iterative Denoising.mp4	20.9 MB
004. 4.3 Diffusers and the Hugging Face Ecosystem.en.srt	8.8 KB
004. 4.3 Diffusers and the Hugging Face Ecosystem.mp4	36.3 MB
005. 4.4 Generating Images with Diffusers Pipelines.en.srt	36.8 KB
005. 4.4 Generating Images with Diffusers Pipelines.mp4	102.3 MB
006. 4.5 Deconstructing the Diffusion Process.en.srt	25.5 KB
006. 4.5 Deconstructing the Diffusion Process.mp4	85.2 MB
007. 4.6 Forward Process as Encoder.en.srt	22.3 KB
007. 4.6 Forward Process as Encoder.mp4	70.7 MB
008. 4.7 Reverse Process as Decoder.en.srt	9.9 KB
008. 4.7 Reverse Process as Decoder.mp4	29.9 MB
009. 4.8 Interpolating Diffusion Models.en.srt	12.5 KB
009. 4.8 Interpolating Diffusion Models.mp4	51.7 MB
010. 4.9 Image-to-Image Translation with SDEdit.en.srt	10.0 KB
010. 4.9 Image-to-Image Translation with SDEdit.mp4	28.9 MB
011. 4.10 Image Restoration and Enhancement.en.srt	14.7 KB
011. 4.10 Image Restoration and Enhancement.mp4	39.9 MB
/Lesson 5 Generating and Encoding Text with Transformers/
001. Topics.en.srt	1.2 KB
001. Topics.mp4	4.2 MB
002. 5.1 The Natural Language Processing Pipeline.en.srt	17.3 KB
002. 5.1 The Natural Language Processing Pipeline.mp4	46.7 MB
003. 5.2 Generative Models of Language.en.srt	12.5 KB
003. 5.2 Generative Models of Language.mp4	41.7 MB
004. 5.3 Generating Text with Transformers Pipelines.en.srt	20.2 KB
004. 5.3 Generating Text with Transformers Pipelines.mp4	50.4 MB
005. 5.4 Deconstructing Transformers Pipelines.en.srt	10.7 KB
005. 5.4 Deconstructing Transformers Pipelines.mp4	32.0 MB
006. 5.5 Decoding Strategies.en.srt	17.7 KB
006. 5.5 Decoding Strategies.mp4	39.5 MB
007. 5.6 Transformers are Just Latent Variable Models for Sequences.en.srt	16.9 KB
007. 5.6 Transformers are Just Latent Variable Models for Sequences.mp4	45.0 MB
008. 5.7 Visualizing and Understanding Attention.en.srt	32.6 KB
008. 5.7 Visualizing and Understanding Attention.mp4	59.0 MB
009. 5.8 Turning Words into Vectors.en.srt	13.7 KB
009. 5.8 Turning Words into Vectors.mp4	54.3 MB
010. 5.9 The Vector Space Model.en.srt	9.6 KB
010. 5.9 The Vector Space Model.mp4	25.3 MB
011. 5.10 Embedding Sequences with Transformers.en.srt	13.4 KB
011. 5.10 Embedding Sequences with Transformers.mp4	31.7 MB
012. 5.11 Computing the Similarity Between Embeddings.en.srt	9.6 KB
012. 5.11 Computing the Similarity Between Embeddings.mp4	24.7 MB
013. 5.12 Semantic Search with Embeddings.en.srt	8.4 KB
013. 5.12 Semantic Search with Embeddings.mp4	24.4 MB
014. 5.13 Contrastive Embeddings with Sentence Transformers.en.srt	8.9 KB
014. 5.13 Contrastive Embeddings with Sentence Transformers.mp4	21.2 MB
/Lesson 6 Connecting Text and Images/
001. Topics.en.srt	1.1 KB
001. Topics.mp4	4.4 MB
002. 6.1 Components of a Multimodal Model.en.srt	7.1 KB
002. 6.1 Components of a Multimodal Model.mp4	16.8 MB
003. 6.2 Vision-Language Understanding.en.srt	12.8 KB
003. 6.2 Vision-Language Understanding.mp4	40.0 MB
004. 6.3 Contrastive Language-Image Pretraining.en.srt	7.7 KB
004. 6.3 Contrastive Language-Image Pretraining.mp4	21.8 MB
005. 6.4 Embedding Text and Images with CLIP.en.srt	19.4 KB
005. 6.4 Embedding Text and Images with CLIP.mp4	43.2 MB
006. 6.5 Zero-Shot Image Classification with CLIP.en.srt	4.8 KB
006. 6.5 Zero-Shot Image Classification with CLIP.mp4	12.5 MB
007. 6.6 Semantic Image Search with CLIP.en.srt	14.5 KB
007. 6.6 Semantic Image Search with CLIP.mp4	42.9 MB
008. 6.7 Conditional Generative Models.en.srt	6.9 KB
008. 6.7 Conditional Generative Models.mp4	25.9 MB
009. 6.8 Introduction to Latent Diffusion Models.en.srt	11.2 KB
009. 6.8 Introduction to Latent Diffusion Models.mp4	35.1 MB
010. 6.9 The Latent Diffusion Model Architecture.en.srt	7.3 KB
010. 6.9 The Latent Diffusion Model Architecture.mp4	24.6 MB
011. 6.10 Failure Modes and Additional Tools.en.srt	8.9 KB
011. 6.10 Failure Modes and Additional Tools.mp4	30.6 MB
012. 6.11 Stable Diffusion Deconstructed.en.srt	15.9 KB
012. 6.11 Stable Diffusion Deconstructed.mp4	39.6 MB
013. 6.12 Writing Our Own Stable Diffusion Pipeline.en.srt	14.9 KB
013. 6.12 Writing Our Own Stable Diffusion Pipeline.mp4	33.3 MB
014. 6.13 Decoding Images from the Stable Diffusion Latent Space.en.srt	5.6 KB
014. 6.13 Decoding Images from the Stable Diffusion Latent Space.mp4	14.7 MB
015. 6.14 Improving Generation with Guidance.en.srt	12.2 KB
015. 6.14 Improving Generation with Guidance.mp4	27.3 MB
016. 6.15 Playing with Prompts.en.srt	41.4 KB
016. 6.15 Playing with Prompts.mp4	126.6 MB
/Lesson 7 Post-Training Procedures for Diffusion Models/
001. Topics.en.srt	1.0 KB
001. Topics.mp4	4.5 MB
002. 7.1 Methods and Metrics for Evaluating Generative AI.en.srt	8.5 KB
002. 7.1 Methods and Metrics for Evaluating Generative AI.mp4	23.4 MB
003. 7.2 Manual Evaluation of Stable Diffusion with DrawBench.en.srt	19.0 KB
003. 7.2 Manual Evaluation of Stable Diffusion with DrawBench.mp4	56.8 MB
004. 7.3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors.en.srt	25.5 KB
004. 7.3 Quantitative Evaluation of Diffusion Models with Human Preference Predictors.mp4	66.6 MB
005. 7.4 Overview of Methods for Fine-Tuning Diffusion Models.en.srt	12.0 KB
005. 7.4 Overview of Methods for Fine-Tuning Diffusion Models.mp4	23.9 MB
006. 7.5 Sourcing and Preparing Image Datasets for Fine-Tuning.en.srt	10.3 KB
006. 7.5 Sourcing and Preparing Image Datasets for Fine-Tuning.mp4	24.7 MB
007. 7.6 Generating Automatic Captions with BLIP-2.en.srt	10.8 KB
007. 7.6 Generating Automatic Captions with BLIP-2.mp4	22.5 MB
008. 7.7 Parameter Efficient Fine-Tuning with LoRA.en.srt	15.8 KB
008. 7.7 Parameter Efficient Fine-Tuning with LoRA.mp4	47.6 MB
009. 7.8 Inspecting the Results of Fine-Tuning.en.srt	6.6 KB
009. 7.8 Inspecting the Results of Fine-Tuning.mp4	16.8 MB
010. 7.9 Inference with LoRAs for Style-Specific Generation.en.srt	16.1 KB
010. 7.9 Inference with LoRAs for Style-Specific Generation.mp4	44.6 MB
011. 7.10 Conceptual Overview of Textual Inversion.en.srt	9.9 KB
011. 7.10 Conceptual Overview of Textual Inversion.mp4	34.7 MB
012. 7.11 Subject-Specific Personalization with Dreambooth.en.srt	9.7 KB
012. 7.11 Subject-Specific Personalization with Dreambooth.mp4	34.7 MB
013. 7.12 Dreambooth versus LoRA Fine-Tuning.en.srt	8.1 KB
013. 7.12 Dreambooth versus LoRA Fine-Tuning.mp4	23.9 MB
014. 7.13 Dreambooth Fine-Tuning with Hugging Face.en.srt	18.2 KB
014. 7.13 Dreambooth Fine-Tuning with Hugging Face.mp4	49.9 MB
015. 7.14 Inference with Dreambooth to Create Personalized AI Avatars.en.srt	18.1 KB
015. 7.14 Inference with Dreambooth to Create Personalized AI Avatars.mp4	53.6 MB
016. 7.15 Adding Conditional Control to Text-to-Image Diffusion Models.en.srt	5.6 KB
016. 7.15 Adding Conditional Control to Text-to-Image Diffusion Models.mp4	17.1 MB
017. 7.16 Creating Edge and Depth Maps for Conditioning.en.srt	20.5 KB
017. 7.16 Creating Edge and Depth Maps for Conditioning.mp4	61.2 MB
018. 7.17 Depth and Edge-Guided Stable Diffusion with ControlNet.en.srt	22.8 KB
018. 7.17 Depth and Edge-Guided Stable Diffusion with ControlNet.mp4	72.2 MB
019. 7.18 Understanding and Experimenting with ControlNet Parameters.en.srt	11.4 KB
019. 7.18 Understanding and Experimenting with ControlNet Parameters.mp4	37.6 MB
020. 7.19 Generative Text Effects with Font Depth Maps.en.srt	3.7 KB
020. 7.19 Generative Text Effects with Font Depth Maps.mp4	7.4 MB
021. 7.20 Few Step Generation with Adversarial Diffusion Distillation (ADD).en.srt	8.7 KB
021. 7.20 Few Step Generation with Adversarial Diffusion Distillation (ADD).mp4	35.4 MB
022. 7.21 Reasons to Distill.en.srt	7.6 KB
022. 7.21 Reasons to Distill.mp4	18.9 MB
023. 7.22 Comparing SDXL and SDXL Turbo.en.srt	16.4 KB
023. 7.22 Comparing SDXL and SDXL Turbo.mp4	39.4 MB
024. 7.23 Text-Guided Image-to-Image Translation.en.srt	23.0 KB
024. 7.23 Text-Guided Image-to-Image Translation.mp4	76.2 MB
025. 7.24 Video-Driven Frame-by-Frame Generation with SDXL Turbo.en.srt	16.4 KB
025. 7.24 Video-Driven Frame-by-Frame Generation with SDXL Turbo.mp4	82.6 MB
026. 7.25 Near Real-Time Inference with PyTorch Performance Optimizations.en.srt	14.2 KB
026. 7.25 Near Real-Time Inference with PyTorch Performance Optimizations.mp4	33.7 MB
/Summary/
001. Programming Generative AI Summary.en.srt	1.4 KB
001. Programming Generative AI Summary.mp4	5.0 MB
Total files 232