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The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. In Deep Learning with Python, Third Edition you’ll discover: Deep learning from first principles The latest features of Keras 3 A primer on JAX, PyTorch, and TensorFlow Image classification and image segmentation Time series forecasting Large Language models Text classification and machine translation Text and image generation—build your own GPT and diffusion models! Scaling and tuning models With over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. About the Technology In less than a decade, deep learning has changed the world—twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. About the Book Deep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. What's Inside Hands-on, code-first learning Comprehensive, from basics to generative AI Intuitive and easy math explanations Examples in Keras, PyTorch, JAX, and TensorFlow About the Reader For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. About the Authors François Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras. Quotes Perfect for anyone interested in learning by doing from one of the industry greats. - Anthony Goldbloom, Founder of Kaggle A sharp, deeply practical guide that teaches you how to think from first principles to build models that actually work. - Santiago Valdarrama, Founder of ml.school The most up-to-date and complete guide to deep learning you’ll find today! - Aran Komatsuzaki, EleutherAI Masterfully conveys the true essence of neural networks. A rare case in recent years of outstanding technical writing. - Salvatore Sanfilippo, Creator of Redis

**RSVP instructions: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

Description: Join us for a fireside chat with Santiago Valdarrama, a machine learning engineer, educator, and freelancer, renowned for his hands-on, pragmatic approach to AI and ML. Hugo Bowne-Anderson will host this Outerbounds event, diving into the real-world challenges and opportunities of implementing machine learning at scale. ​ Santiago, creator of a highly acclaimed end-to-end machine learning course, is dedicated to equipping engineers with the practical skills needed to excel in real-world ML environments. His expertise in simplifying complex concepts and preparing students for real-world challenges offers invaluable insights for ML practitioners at all levels.

​Key topics of discussion: ​- Full Machine Learning Lifecycle: How to master the entire process from data collection to deployment and monitoring. - ​ML in Production: Overcoming common pitfalls in deploying machine learning models. - AI/ML Evolution: What sets modern AI approaches apart from traditional ML methods? - ​Freelancing in ML: What does it take to succeed as a freelancer in the machine learning space? - ​Future ML Skills: Which competencies will be critical for ML engineers in the AI-driven future?

​This conversation aims to bridge the gap between academic knowledge and industry application, offering actionable insights on implementing machine learning solutions.

​This fireside chat is relevant for students, practitioners, and leaders in the ML space, providing actionable insights and a realistic perspective on the current and future state of machine learning engineering.

**RSVP instructions: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

Description: Join us for a fireside chat with Santiago Valdarrama, a machine learning engineer, educator, and freelancer, renowned for his hands-on, pragmatic approach to AI and ML. Hugo Bowne-Anderson will host this Outerbounds event, diving into the real-world challenges and opportunities of implementing machine learning at scale. ​ Santiago, creator of a highly acclaimed end-to-end machine learning course, is dedicated to equipping engineers with the practical skills needed to excel in real-world ML environments. His expertise in simplifying complex concepts and preparing students for real-world challenges offers invaluable insights for ML practitioners at all levels.

​Key topics of discussion: ​- Full Machine Learning Lifecycle: How to master the entire process from data collection to deployment and monitoring. - ​ML in Production: Overcoming common pitfalls in deploying machine learning models. - AI/ML Evolution: What sets modern AI approaches apart from traditional ML methods? - ​Freelancing in ML: What does it take to succeed as a freelancer in the machine learning space? - ​Future ML Skills: Which competencies will be critical for ML engineers in the AI-driven future?

​This conversation aims to bridge the gap between academic knowledge and industry application, offering actionable insights on implementing machine learning solutions.

​This fireside chat is relevant for students, practitioners, and leaders in the ML space, providing actionable insights and a realistic perspective on the current and future state of machine learning engineering.

We talked about:

Santiago’s background “Transitioning to ML” vs “Adding ML as a skill” Getting over the fear of math for software developers Learning by explaining Seven lessons I learned about starting a career in machine learning Lesson 1 – Take the first step Lesson 2 – Learning is a marathon, not a sprint Lesson 3 – If you want to go quickly, go alone. If you want to go far, go together. Lesson 4 – Do something with the knowledge you gain Lesson 5 – ML is not just math. Math is not scary. Lesson 6 – Your ability to analyze a problem is the most important skill. Coding is secondary. Lesson 7 – You don’t need to know every detail Tools and frameworks needed to transition to machine learning Problem-based learning vs Top-down learning Learning resources Santiago’s favorite books Santiago’s course on transitioning to machine learning Improving coding skills Building solutions without machine learning Becoming a better engineer What is the difference between machine learning and data science? Getting into machine learning - Reiteration Getting past the math

Links:

Santiago's Twitter: https://twitter.com/svpino Santiago's course: https://gumroad.com/svpino#kBjbC Pinned tweet with a roadmap: https://twitter.com/svpino/status/1400798154732212230

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