what are the best books to learn generative ai for developers

• The Reddit post is titled “Book Recommendation” posted in the subreddit r/learnmachinelearning.
• A user asks for a book recommendation to learn machine learning.
• Multiple users recommended different books:
  • “Python Machine Learning” by Sebastian Raschka recommended by a reddit user who used it for their thesis and found it helpful (28 karma).
  • “Python Machine Learning: Machine Learning, and Deep Learning with Python, sickit-learn, and TensorFlow” also by Sebastian Raschka recommended by another user. (9 karma).
  • “Hands-on Machine Learning with Scikit-Learn, Keras etc” by Aurélien Géron recommended by a user who heard good things (15 karma).
  • “Machine Learning with PyTorch and Scikit-Learn” by Sebastian Raschka recommended by a user who recently bought it (2 karma).
  • “Deep Learning” by Ian Goodfellow recommended by a user for mathematical understanding of deep learning (2 karma).
  • “Deep Learning” by Chollet recommended by a user for practical application in Keras. They also mention it requires prerequisite ML/math knowledge because of Chollet’s brief explanations about what’s going on under the hood (2 karma).
  • “Probabilistic Machine Learning: An Introduction” by Murphy was recommended as one of the best books to learn deep learning along with “Probabilistic Machine Learning: Advanced Topics” by a user (14 karma).
  • “Pattern Recognition and Machine Learning” by Bishop recommended as a very good probabilistic book for basic machine learning. The user also recommended “The Elements of Statistical Learning” and “An Introduction to Statistical Learning: With Applications in R” for risk minimization. The user also mentioned that there’s a dissonance between these books and current state-of-the-art research which focused on deep learning. They recommend “Goodfellows Deep Learning” for this, with “Probabilistic Machine Learning: An Introduction” and “Probabilistic Machine Learning: Advanced Topics” as the best books on the topics, but note that there are tons of free resources for those as well (7 karma).
  • “Machine Learning: a probabilistic perspective” by Murphy recommended specifically for unsupervised learning (4 karma).
• A user recommends five other resources besides books:
  • machinelearningmastery.com
  • towardsdatascience
  • Medium
  • Geeksforgeeks (usually for python but they also got some ML stuff)
  • Stack

• Relevant: True

• Importance: 8

• Reddit thread titled “Books to get started with Machine Learning.”

• Original poster is an undergraduate student looking for books to help them get started.

• They are looking for books that are slightly mathematical but still have a good amount of practical examples and exercises.

• Multiple users highly recommend “Hands-On Machine Learning with Scikit Learn, Keras and Tensorflow” by Geron.

• Other frequently recommended books include “An Introduction to Statistical Learning” by Gareth James et al., “Deep Learning with Python” by François Chollet, and “Elements of Statistical Learning” by Trevor Hastie et al.

• One user specifically recommends “Dive into Deep Learning” by Aston Zhang et al. for deep learning specifically.

• Some users suggest beginning with more basic books before moving on to more complex ones.

• One user recommends “Applied Predictive Modeling” by Max Kuhn as a better introduction than “Introduction to Statistical Learning” for beginners.

• Numerous comments discussing specific aspects and benefits of each book recommended.

• Some users caution that certain books may require more mathematical maturity and experience.

• Overall consensus is that books are a good way to learn the mathematics behind machine learning.

• One user notes the importance of putting in the work and reading to truly understand the material.

• Multiple users recommend supplementing book learning with personal projects and coding in Python.

• One user suggests using Geeks for Geeks for simple explanations and coding examples.

• Multiple users suggest R as an alternative language for those with a statistics background.

• Some users recommend additional resources, including online classes and other books.

• User comments range in length and detail, with some providing more in-depth explanations and experiences with the recommended books.

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Here are my notes on the webpage “Modern Generative AI with ChatGPT and OpenAI Models”:

• Generative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities.
• The book “Modern Generative AI with ChatGPT and OpenAI Models” is about learning generative AI models and AI language models using ChatGPT and OpenAI.
• The book provides insights into the inner workings of the LLMs and guides you through creating your own language models.
• The book covers the theory behind generative AI models and the road to GPT3 and GPT4.
• There is a focus on the GPT architecture for generative AI models.
• The book explores use cases where ChatGPT can boost productivity and enhance creativity.
• You’ll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities.
• The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster.
• Enterprise-level scenarios that leverage OpenAI models’ APIs available on Azure infrastructure are also illustrated (both generative models like GPT-3 and embedding models like Ada).
• For each scenario, you’ll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models’ integration into your applications.
• The book provides an introduction to the field of generative AI, helping you understand how these models are trained to generate new data.
• The book is intended for individuals interested in boosting their daily productivity, business persons looking to dive deeper into real-world applications to empower their organizations, data scientists and developers trying to identify ways to boost ML models and code, marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models.
• The book also covers how to ensure responsible AI and ethics in generative AI systems.
• You should have a basic understanding of Python; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.
• If you purchase the print or Kindle book, it includes a free PDF eBook.

• The book “Generative Deep Learning, 2nd Edition” teaches the basics of deep learning and progresses to cutting-edge architectures.
• It covers how to use TensorFlow and Keras to create generative AI models from scratch.
• The book includes tips and tricks for making models learn more efficiently and become more creative.
• The book covers a range of generative models including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.
• Readers of the book can learn how to use VAEs to change facial expressions in photos, how to train GANs to generate images based on their own datasets, how to build diffusion models to produce new varieties of flowers, and how to train their own GPT for text generation.
• The book also covers how large language models like ChatGPT are trained.
• State-of-the-art architectures such as StyleGAN2 and ViT-VQGAN are explained.
• The book covers how to compose polyphonic music using Transformers and MuseGAN.
• Readers will learn how generative world models can solve reinforcement learning tasks.
• The book explores multimodal models such as DALL.E 2, Imagen, and Stable Diffusion.
• The book explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create a competitive advantage.
• There are illustrations throughout the book to better understand concepts.
• The author is David Foster, a machine learning engineer and researcher with a Ph.D. in machine learning from Imperial College London.
• The book is 512 pages long and was published on April 26, 2021, by O’Reilly Media, Inc.
• The book is available in various formats: Paperback, Ebook, Safari Books Online, and Kindle.
• The book has received positive reviews from readers.
• The book is suitable for machine learning engineers and data scientists who want to create generative deep learning models.

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• Reddit post in r/learnmachinelearning titled “Looking for good learning sources around generative AI, specifically LLM”
• Posted 8 months ago and has 14 points
• The post is by a user who is asking for good video content sources to learn about the concepts associated with generative AI such as RL, RLHF, transformer, etc
• The user is looking for a source that explains the concepts from the ground up in extremely simple language, using analogies/stories familiar to a child between 10-12 years old.
• The user would prefer channels that explain the concepts in a sequential manner and make short and crisp videos
• One of the comments by another user mentions “CS231n: Convolutional Neural Networks for Visual Recognition” course by Stanford University, as a recommended learning source for generative AI. The comment has 2 karma.
• Another comment by a user mentions that generative AI is a complex topic and that they recommend starting with simpler topics like supervised learning, unsupervised learning, computer vision, natural language processing, etc. before diving into generative AI. The comment has 3 karma.
• Another user comments saying they face difficulty finding sources for learning generative AI, as opposed to image recognition which has ample. - The comment has 2 karma.
• Other comments recommend several YouTube channels and online courses to learn generative AI, including “The AI Epiphany”, “DeepLearning.AI”, “Two Minute Papers”, “Siraj Raval”, “Datacamp”, “Udemy”, etc. with varying karma scores.
• Several users recommend specific books on the topic of generative AI, including “Grokking Deep Learning” by Andrew Trask, “Hands-On Generative Adversarial Networks with Keras: Design and implement highly effective generative adversarial network models using TensorFlow and Keras” by Jason Brownlee, “Generative Adversarial Networks Cookbook: Over 100 recipes for improving your generative models using Python, TensorFlow, and Keras” by Jakub Langr, and “Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play” by David Foster, among others. These comments have various karma scores.
• Several comments also mention blogs and research papers as helpful resources for learning about generative AI, including OpenAI blogs, Yann LeCun’s research publications, and Google AI Blog among others. These comments also have varying karma scores.

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Relevant: true Importance: 6 Notes:

• A machine learning practitioner explains that most of the books on a list of “Must Read Artificial Intelligence Books” from three years ago are currently outdated.
• They recommend “Artificial Intelligence: A Modern Approach” by Stuart Russell & Peter Norvig as the only book on the list worth reading as it gives a good idea of what the domain of artificial intelligence is made of and tells us about the different approaches/problems that can be solved through the field.
• Some users provide other book suggestions, including textbooks, pop-science books, and books tilted towards explaining domain problems and solutions:
  • “Prediction Machines” by Joshua Gans, Ajay Agrawal, and Avi Goldfarb.
  • “Machine Learning: A Probabilistic Perspective” by Kevin P. Murphy.
  • “The Elements of Statistical Learning” by Jerome H. Friedman, Robert Tibshirani, & Trevor Hastie.
  • “Pattern Recognition and Machine Learning” by Christopher Bishop.
  • “Deep Learning” by Ian Goodfellow, Yoshua Bengio, & Aaron Courville.
  • “The Nature of Statistical Learning Theory” by Vladimir Vapnik.
  • “Information Theory, Inference, and Learning Algorithms” by David MacKay.
  • “Reinforcement Learning” by Andrew Barto & Richard S. Sutton.
  • “Neural Networks and Deep Learning” by Michael Nielsen.
  • “The Book of Why” by Judea Pearl.
  • “The Deep Learning Revolution” by Terry Sejnowski.
  • “The Master Algorithm” by Pedro Domingos.
• Some users provide their personal recommendations:
  • “Elements of Statistical Learning”
  • “Machine Learning: A probabilistic perspective by Kevin Patrick Murphy”
  • “Artificial Intelligence: A Modern Approach” has been somebody’s holy grail.
• One user suggests “Life 3.0” by Max Tegmark as a great read but 50% of the book is about physics and mathematics.
• Some users discuss the relevancy of the books due to the fast-changing field of AI, and whether technical books are better served as time-capsules than valid educational materials.
• One user asks about science-fiction books that give air to non-tech/society what-ifs, and there are no responses provided.
• One user shares a list of Amazon links to purchase the books mentioned.

• Reddit post titled “[D] 10 Insightful & Practical AI/ML Books to Read in 2021”
• The post is from 2 years ago and has 160 Karma points
• Crossminds AI blog created a post listing the 10 AI/ML books recommended
• These 10 books cover various topics from fundamental concepts to algorithms and applications of AI/ML
• Also includes a collection of author talks and book reviews that could be helpful before diving deep into the books
• List of books:
  • “Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell
  • “Rebooting AI” by Gary Marcus and Ernest Davis
  • “Human Compatible: Artificial Intelligence and the Problem of Control” by Stuart Russel
  • “You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place” by Janelle Shane
  • “The Hundred-Page Machine Learning Book” by Andriy Burkov
  • “Interpretable Machine Learning: A Guide for Making Black Box Models Explainable” by Christoph Molnar
  • “Machine Learning Yearning” by Andrew Ng
  • “Machine Learning Engineering” by Andriy Burkov
  • “Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd Edition)” by Aurélien Géron
  • “Approaching (Almost) Any Machine Learning Problem” by Abhishek Thakur
  • Comments on the post:
    • One user asks how long it takes to read one book, while another recommends active reading and modern technology to increase reading speed
    • Another user asks if people actually read 10 technical books in a year and receives a range of responses on the matter
    • Many comments include additional book recommendations
    • Some recommendations include “Deep Learning with Python” by Francois Chollet and “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World” by Pedro Domingos
    • Some users recommend resources to supplement learning including the data camp for building practical intuition and books on linear algebra to bridge programming to math
    • A few users express frustration with poorly written tutorials and courses in the field
    • One user shares a personal story of reading a ML book on a mountain during an expedition
• Some users recommend books by authors on the initial list, including

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💭  Looking into

Overview of Generative AI techniques and their uses in real-world applications

💭  Looking into

A list of 3 books with their pros and cons so the user can pick the best one for their needs