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

Joseph Babcock, Raghav Bali

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.

Generative AI with Python and TensorFlow 2. Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

Joseph Babcock, Raghav Bali

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment.Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.

Mastering Predictive Analytics with Python. Click here to enter text

Joseph Babcock

The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life

Python: Advanced Predictive Analytics. Gain practical insights by exploiting data in your business to build advanced predictive modeling applications

Ashish Kumar, Joseph Babcock

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python.You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling.Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books:1. Learning Predictive Analytics with Python2. Mastering Predictive Analytics with Python