Understanding Deep Learning: Beyond the Code
This blog post is a summary of a fascinating conversation with Professor Simon Prince about his new book, Understanding Deep Learning. Unlike many practical books focusing on coding, Professor Prince's book delves into the underlying ideas that drive deep learning, equipping readers to tackle novel situations where existing recipes fall short.
What You'll Learn from the Book
The book covers a wide range of topics, including:
- Deep neural networks and the trainingtesting pipeline.
- Different architectures: Convolutional networks, residual networks, graph neural networks, and transformers.
- Generative models: Normalizing flows, VAEs, GANs, and diffusion models.
- A section on reinforcement learning.
Professor Prince highlights two particularly interesting chapters: one exploring why deep neural networks work, and another addressing the ethical implications of AI.
Who Should Read This Book?
This book is designed to be accessible to a wide audience:
- Beginners: If you're new to deep learning, this book will take you from scratch to near the cutting edge.
- Educators: It's an invaluable resource for teaching deep learning.
- Practitioners and Researchers: It can help fill in gaps in your knowledge and offer a fresh perspective.
The book also contains 275 figures and Python notebooks.
The Mystery of Deep Learning
Professor Prince acknowledges the somewhat ironic title of his book, as, at the time of writing, nobody fully understands how deep learning models truly work. These models learn piecewise linear functions, effectively chopping up the input space into countless tiny regions – often more than there are atoms in the universe! How they generalize and learn these functions remains a significant mystery.
Why Does Deep Learning Work?
It's remarkable that the fitting algorithm avoids being trapped in local minima or near saddle points. It can efficiently recruit spare model capacity to fit unexplained training data. This is startling because fitting deep networks reliably and efficiently and achieveing good generalization is not at all obvious.
Professor Prince suggests that the data, models, training algorithms, or a combination thereof possess special properties that enable this success.
He references his book where he discusses the challenges of optimizing highdimensional loss functions. Overparameterization and the choice of activation function are identified as critical factors that make this tractable in deep networks. During training, parameters move through a lowdimensional subspace towards a family of connected global minima.
The Ethical Considerations of AI
Professor Prince emphasizes the importance of discussing the ethical implications of AI. He believes it's irresponsible to ignore the potential for harm alongside the immense benefits this potent technology offers. AI is poised to revolutionize healthcare, design, entertainment, transportation, education, and countless areas of commerce.
The Responsibility of Scientists and Engineers
He argues that anyone involved in studying, researching, or writing about AI must contemplate the degree to which scientists are accountable for the uses of their technology. He encourages readers to consider the role of capitalism in driving AI development, and the potential for legal frameworks and social good applications to lag behind. Are scientists and engineers able to control the progress of this field and mitigate potential harms? What kind of organizations are they willing to work for, and how seriously do those organizations commit to reducing the potential harms of AI?
Ethical AI is a collective action problem.
The book's ethical discussion concludes with an appeal to scientists to consider the moral and ethical implications of their work and potential for misuse of the systems they create.
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Professor Prince's Background
Professor Prince started his career in psychology before wandering through various scientific fields, including neuroscience, augmented reality, and medical imaging. He's best known for his earlier work in computer vision. He's currently a professor at the University of Bath.
The Importance of Ideas Over Code
Professor Prince emphasizes that experimentalists have outpaced the theory, and the sheer volume of research papers makes it challenging to find good resources. He believes the book can connect the important ideas of the last decade in deep learning and save the community a giant amount of time.
He sees deep learning as a science of modelling functions and probability distributions in very high dimensions.
The Neuron Analogy: A Misleading Metaphor
Professor Prince dislikes the neural metaphor. There is no evidence that the brain works in any way that deep neural networks do. Using this metaphor implies that networks are having thoughts, like us, and that's deeply misleading to those outside the community.
Final Thoughts
Understanding Deep Learning promises to be a thoughtprovoking exploration of the ideas behind this rapidly evolving field. By focusing on the "why" rather than just the "how," Professor Prince aims to equip readers with the knowledge and critical thinking skills needed to navigate the complexities of deep learning and contribute to its future.