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Home > Data > Artificial Intelligence > Generative AI with Python and PyTorch
Generative AI with Python and PyTorch
Generative AI with Python and PyTorch

Generative AI with Python and PyTorch: Navigating the AI frontier with LLMs, Stable Diffusion, and next-gen AI applications , Second Edition

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Generative AI with Python and PyTorch

References

  1. https://www.smithsonianmag.com/smart-news/artificial-intelligence-art-wins-colorado-state-fair-180980703/
  2. ChatGPT technical report: https://arxiv.org/abs/2303.08774
  3. https://arxiv.org/abs/2107.03374
  4. https://www.nature.com/articles/s41598-019-42294-8
  5. https://deepmind.com/research/case-studies/alphago-the-story-so-far
  6. https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
  7. https://arxiv.org/abs/1810.04805
  8. Brandon, John (16 February 2018). Terrifying high-tech porn: Creepy ‘deepfake’ videos are on the rise, Fox News. Retrieved 20 February 2018.
  9. https://upload.wikimedia.org/wikipedia/en/thumb/7/71/Deepfake_example.gif/280px-Deepfake_example.gif
  10. https://devopstar.com/static/2293f764e1538f357dd1c63035ab25b0/d024a/fake-facebook-conversation-example-1.png
  11. https://openai.com/blog/better-language-models/
  12. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html
  13. https://salu133445.github...

Perceptrons: A brain in a function

The simplest neural network architecture—the perceptron—was inspired by biological research to understand the basis of mental processing in an attempt to represent the function of the brain with mathematical formulae. In this section, we will cover some of this early research and how it inspired what is now the field of deep learning and generative AI.

From tissues to TLUs

The recent popularity of AI algorithms might give the false impression that this field is new. Many recent models are based on discoveries made decades ago that have been reinvigorated by the massive computational resources available in the cloud and customized hardware for parallel matrix computations such as Graphical Processing Units (GPUs), Tensor Processing Units (TPUs), and Field-Programmable Gate Array (FPGAs). If we consider research on neural networks to include their biological inspiration as well as computational theory, this field is over a hundred...

Multilayer perceptrons and backpropagation

While large research funding for neural networks declined until the 1980s after the publication of Perceptrons, researchers still recognized that these models had value, particularly when assembled into multilayer networks, each composed of several perceptron units. Indeed, when the mathematical form of the output function (that is, the output of the model) was relaxed to take on many forms (such as a linear function or a sigmoid), these networks could solve both regression and classification problems, with theoretical results showing that three-layer networks could effectively approximate any output.13 However, none of this work addressed the practical limitations of computing the solutions to these models, with rules such as the perceptron learning algorithm described earlier proving a great limitation to their applied use. A central problem was how to appropriately estimate the weights in the hidden layers of the network, which form the...

Varieties of networks: convolution and recursive

Up until now, we’ve primarily discussed the basics of neural networks by referencing feedforward networks, where every input is connected to every output in each layer. While these feedforward networks are useful for illustrating how deep networks are trained, they are only one class of a broader set of architectures used in modern applications, including generative models. Thus, before covering some of the techniques that make training large networks practical, let’s review these alternative deep models.

Networks for seeing: convolutional architectures

As noted at the beginning of this chapter, one of the inspirations for deep neural network models is the biological nervous system. As researchers attempted to design computer vision systems that would mimic the functioning of the visual system, they turned to the architecture of the retina, as revealed by physiological studies by neurobiologists David Hubel and...

Networks for sequential data

In addition to image data, natural language text has also been a frequent topic of interest in neural network research. However, unlike the datasets we’ve examined thus far, language has a distinct order that is important to its meaning. Thus, to accurately capture the patterns in language- or time-dependent data, it is necessary to utilize networks designed for this purpose.

RNNs and LSTMs

Let’s imagine we are trying to predict the next word in a sentence, given the words up until this point. A neural network that attempted to predict the next word would need to take into account not only the current word but also a variable number of prior inputs. If we instead used only a simple feedforward MLP, the network would essentially process the entire sentence or each word as a vector. This introduces the problem of either having to pad variable-length inputs to a common length and not preserving any notion of correlation (that is, which...

Transformers

While we will discuss this topic in more detail in Chapter 4, it is important to note that convolutional and recursive units have been replaced in many current applications by transformers, a type of architecture first described in 2017 (https://arxiv.org/abs/1706.03762). In a way, transformers combine the strengths of both recursive and convolutional networks.

Like convolutional networks, they compute the relative similarity between elements in a sequence or matrix; however, unlike convolutional networks, they perform this calculation between all elements rather than just locally. Like LSTMs, they preserve a context window through positional encoding elements, the all-to-all pairwise similarity (also known as self-attention), and pass through connections that resemble the memory units in LSTMs. However, unlike LSTMs, they can be computed in parallel, enabling more efficient training.

Figure 2.17 gives an overview of how this remarkable operation works; each...

Building a better optimizer

So far in this chapter, we have discussed several examples in which better neural network architectures allowed for breakthroughs; however, just as (and perhaps even more) important is the optimization procedure used to minimize the error function in these problems, which “learns” the parameters of the network by selecting those that yield the lowest error. Referring to our discussion of backpropagation, this problem has two components:

  • How to initialize the weights: In many applications historically, we see that the authors used random weights within some range, and hoped that the use of backpropagation would result in at least a locally minimal loss function from this random starting point. Whether the activation functions in the network had saturated or 0 values (increasing the likelihood of uninformative gradients during training of the model) was not considered.
  • How to find the local minimum loss: In basic backpropagation...

Summary

In this chapter, we’ve covered the basic vocabulary of deep learning—how initial research into perceptrons and MLPs led to simple learning rules being abandoned for backpropagation. We also looked at specialized neural network architectures such as CNNs, based on the visual cortex, and recurrent networks, specialized for sequence modeling. Finally, we examined variants of the gradient descent algorithm proposed originally for backpropagation, which have advantages such as momentum, and described weight initialization schemes that place the parameters of the network in a range that is easier to navigate to a local minimum.

With this context in place, we are all set to dive into projects in generative modeling, beginning with the generation of MNIST digits using deep belief networks in Chapter 11, Neural Networks Using VAEs.

References

  1. López-Muñoz, F., Boya, J., and Alamo, C. (2006). Neuron theory, the cornerstone of neuroscience, on the centenary of the Nobel Prize award to Santiago Ramón y Cajal. Brain Research Bulletin. 70 (4–6): 391–405. https://pubmed.ncbi.nlm.nih.gov/17027775/
  2. Ramón y Cajal, S. (1888). Estructura de los centros nerviosos de las aves.
  3. McCulloch, W.S. and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133. https://doi.org/10.1007/BF02478259
  4. Rashwan, M., Ez, R., and Abd El reheem, G. (2017). Computational Intelligent Algorithms For Arabic Speech Recognition. Journal of Al-Azhar University Engineering Sector. 12. 886-893. 10.21608/auej.2017.19198. https://jaes.journals.ekb.eg/article_19198.html
  5. Artificial neuron. Wikipedia. Retrieved April 26, 2021, from https://en.wikipedia.org/wiki/Artificial_neuron
  6. Shackleton-Jones, N. (2019, May...
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Key benefits

  • Implement real-world applications of LLMs and generative AI
  • Fine-tune models with PEFT and LoRA to speed up training
  • Expand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex
  • Purchase of the print or Kindle book includes a free eBook in PDF format

Description

Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.

Who is this book for?

This book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.

What you will learn

  • Grasp the core concepts and capabilities of LLMs
  • Craft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs
  • Understand how attention and transformers have changed NLP
  • Optimize your diffusion models by combining them with VAEs
  • Build text generation pipelines based on LSTMs and LLMs
  • Leverage the power of open-source LLMs, such as Llama and Mistral, for diverse applications
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Length: 450 pages
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ISBN-13 : 9781835884447
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Category :
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Concepts :
Artificial Intelligence
Tools :
PyTorch

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Table of Contents

17 Chapters
Introduction to Generative AI: Drawing Data from Models Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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
Join our communities on Discord and Reddit
LLM Foundations Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Open-Source LLMs
The LLaMA models
Mixtral
Dolly
Falcon
Grok-1
Summary
References
Join our communities on Discord and Reddit
Prompt Engineering Chevron down icon Chevron up icon
Prompt Engineering
Prompt engineering
Prompting techniques
Cross-domain prompting
Adversarial prompting
Limitations of prompt engineering
Summary
References
LLM Toolbox Chevron down icon Chevron up icon
LLM Toolbox
The LangChain ecosystem
Building a simple LLM application
Creating complex applications with LangGraph
Summary
References
Join our communities on Discord and Reddit
LLM Optimization Techniques Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Emerging Applications in Generative AI
Advances in model development
New usages for LLMs
Summary
References
Neural Networks Using VAEs Chevron down icon Chevron up icon
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
Join our communities on Discord and Reddit
Image Generation with GANs Chevron down icon Chevron up icon
Image Generation with GANs
Generative adversarial networks
Vanilla GAN
Improved GANs
Challenges
Summary
References
Join our communities on Discord and Reddit
Style Transfer with GANs Chevron down icon Chevron up icon
Style Transfer with GANs
Pix2Pix-GAN: paired style transfer
CycleGAN: unpaired style transfer
Summary
References
Join our communities on Discord and Reddit
Deepfakes with GANs Chevron down icon Chevron up icon
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
Join our communities on Discord and Reddit
Diffusion Models and AI Art Chevron down icon Chevron up icon
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
Join our communities on Discord and Reddit
Other Books You May Enjoy Chevron down icon Chevron up icon
Other Books You May Enjoy
Index Chevron down icon Chevron up icon
Index

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About the authors

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Profile icon Joseph Babcock
Joseph Babcock
Joseph Babcock has spent over a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Throughout his career, he has worked on recommender systems, petabyte-scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to drug discovery and genomics.
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Profile icon Raghav Bali
Raghav Bali
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Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.
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What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela