فهرست مطالب

• Preface
• Introduction to Generative AI: Drawing Data from Models
• Discriminative versus generative models
• Implementing generative models
• The rules of probability
• Discriminative and generative modeling, and Bayes’ theorem
• Why generative models?
• Unique challenges of generative models
• Summary
• References
• Building Blocks of Deep Neural Networks
• Perceptrons: A brain in a function
• Multilayer perceptrons and backpropagation
• Varieties of networks: convolution and recursive
• Networks for sequential data
• Transformers
• Building a better optimizer
• Summary
• References
• The Rise of Methods for Text Generation
• Text representation
• Text generation and the magic of LSTMs
• LSTM variants and convolutions for text
• Summary
• References
• NLP 2.0: Using Transformers to Generate Text
• Attention
• Self-attention
• Transformers
• NLP tasks and transformer architectures
• DistilBERT in action
• Text generation with GPT
• Summary
• References
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• LLM Foundations
• Recap: Transformer architectures
• Updated training setup
• Instruction fine-tuning
• Hands-on: Instruction tuning
• Reinforcement Learning with Human Feedback (RLHF)
• Hands-on: RLHF using PPO
• LLMs
• Summary
• Open-Source LLMs
• The LLaMA models
• Mixtral
• Dolly
• Falcon
• Grok-1
• Summary
• References
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• Prompt Engineering
• Prompt engineering
• Prompting techniques
• Cross-domain prompting
• Adversarial prompting
• Limitations of prompt engineering
• Summary
• References
• LLM Toolbox
• The LangChain ecosystem
• Building a simple LLM application
• Creating complex applications with LangGraph
• Summary
• References
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• LLM Optimization Techniques
• Why optimize?
• Pre-training optimizations
• Fine-tuning optimizations
• Inference time improvements
• Emerging trends and research areas
• Summary
• References
• Emerging Applications in Generative AI
• Advances in model development
• New usages for LLMs
• Summary
• References
• Neural Networks Using VAEs
• Creating separable encodings of images
• The variational objective
• The reparameterization trick
• Inverse autoregressive flow
• Importing CIFAR
• Creating the network in PyTorch
• Summary
• References
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• Image Generation with GANs
• Generative adversarial networks
• Vanilla GAN
• Improved GANs
• Challenges
• Summary
• References
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• Style Transfer with GANs
• Pix2Pix-GAN: paired style transfer
• CycleGAN: unpaired style transfer
• Summary
• References
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• Deepfakes with GANs
• Deepfakes overview
• Modes of operation
• Key feature set
• High-level workflow
• Re-enactment using Pix2Pix
• Challenges
• Off-the-shelf implementations
• Summary
• References
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• Diffusion Models and AI Art
• A walk through image generation: Why we need diffusion models
• Running Stable Diffusion in the cloud
• Deep dive into the text-to-image pipeline
• Summary
• References
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• Other Books You May Enjoy
• Index