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Programming Generative AI

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Programming Generative AI

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Total Size

4.3 GB

Total Files

232

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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


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