My passion for generative AI first ignited in 2016 when I spoke about it at a conference, and ever since then, I can’t stop! I've created an online course, a newsletter and even wrote a book to spread knowledge on this groundbreaking technology. As an instructor, I empower others to explore the boundless potential of generative AI applications. Day in day out, I assist clients in crafting their own generative AI solutions, tailoring them to their unique needs.
I wrote...
Generative AI: Navigating the Course to the Artificial General Intelligence Future
While it’s not the newest tech, I love that it covers the essential groundwork that sparked the modern AI revolution. I personally think its perfect for engineers and data scientists. It's also a great precursor to my book, giving you the strong foundation you need before diving into the next wave of AI advancements.
Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.
Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll…
I truly believe that this is the book that brought my generation of AI experts into the fold. Despite having studied AI and ML, this book took me by the hand and grounded me in the fundamentals. I love the fact that it covers everything from mathematical basics to industry-level techniques.
Written by the OGs of deep learning, it's an absolute must-read for anyone serious about the field. Highly recommend for students and engineers alike.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all…
Bishop’s book laid the mathematical groundwork for me, making it a solid foundation for anyone venturing into Generative AI.
I love how it covers Bayesian inference, graphical models, and machine learning fundamentals in a clear, approachable way. I also think, in my personal opinion, that reading my book after this one would be a natural progression to understand where AI is heading, building on the core concepts that Bishop established.
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models…
I have a deep appreciation for Stuart Russell's book because it brilliantly balances theoretical foundations with practical applications in AI. This book is not just a textbook; it’s a comprehensive guide that covers everything from problem-solving and knowledge representation to machine learning and ethics.
Russell's clear explanations and engaging examples make complex concepts accessible, which resonates with my own passion for demystifying AI for readers. I recommend it to anyone interested in understanding AI's potential and challenges, as it equips you with the knowledge to navigate this rapidly evolving field responsibly and thoughtfully.
I absolutely love Nick Bostrom's book because it dives deep into the fascinating yet daunting future of artificial intelligence, a topic that resonates with my own work. Bostrom's exploration of how superintelligent AI could emerge and the profound risks it poses is both thought-provoking and essential reading for anyone curious about technology's trajectory.
His insights on the challenges of control and alignment really struck a chord with me, as they highlight the importance of designing AI systems that prioritize human values. This book not only raises critical questions but also inspires a sense of urgency to navigate the future responsibly, making it a personal favorite and a vital resource for anyone interested in the intersection of AI and ethics.
The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains.
If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence.
This book explains all things Generative AI. It covers key ideas, real-life uses, and critical ethical issues. Ideal for tech fans, professionals, and business leaders, it helps readers understand and make the most of AI technology.
You'll learn how AI is changing different industries and get practical advice on keeping up with these changes. This book is a must-read for anyone wanting to succeed in the AI-driven future.
