Prof. Chris Bishop's NEW Deep Learning Textbook!

Professor Chris Bishop is a Technical Fellow and Director at Microsoft Research AI4Science, in Cambridge. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh, and in 2017 he was elected Fellow of the Royal Society. Chris was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.

At Microsoft Research, Chris oversees a global portfolio of industrial research and development, with a strong focus on machine learning and the natural sciences.
Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.

Chris's contributions to the field of machine learning have been truly remarkable. He has authored (what is arguably) the original textbook in the field - 'Pattern Recognition and Machine Learning' (PRML) which has served as an essential reference for countless students and researchers around the world, and that was his second textbook after his highly acclaimed first textbook Neural Networks for Pattern Recognition.

Recently, Chris has co-authored a new book with his son, Hugh, titled 'Deep Learning: Foundations and Concepts.' This book aims to provide a comprehensive understanding of the key ideas and techniques underpinning the rapidly evolving field of deep learning. It covers both the foundational concepts and the latest advances, making it an invaluable resource for newcomers and experienced practitioners alike.

Buy Chris' textbook here:

More about Prof. Chris Bishop:

Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more.
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail

TOC:
00:00:00 - Intro to Chris
00:06:54 - Changing Landscape of AI
00:08:16 - Symbolism
00:09:32 - PRML
00:11:02 - Bayesian Approach
00:14:49 - Are NNs One Model or Many, Special vs General
00:20:04 - Can Language Models Be Creative
00:22:35 - Sparks of AGI
00:25:52 - Creativity Gap in LLMs
00:35:40 - New Deep Learning Book
00:39:01 - Favourite Chapters
00:44:11 - Probability Theory
00:45:42 - AI4Science
00:48:31 - Inductive Priors
00:58:52 - Drug Discovery
01:05:19 - Foundational Bias Models
01:07:46 - How Fundamental Is Our Physics Knowledge?
01:12:05 - Transformers
01:12:59 - Why Does Deep Learning Work?
01:16:59 - Inscrutability of NNs
01:18:01 - Example of Simulator
01:21:09 - Control

[carloalessi]

The book just arrived and it's amazing! One humble request: could we ask Prof. Bishop to make a YouTube series with a short lecture for each chapter of the book? That is a bit of effort, but would be an amazing contribution to aid the readers. 🙏

[steevensonemile]

Pattern recognition and machine learning. - The only book that made me clear on what is really PCA. A Book from Bishop really worth to have on the desk.

[patriot-qu]

Wow! As someone who "grew up" on Bishop's original book, i'm so glad to see this interview,

[zeev]

i picked up his 'neural networks for pattern recognition' in 2002 and just couldn't get through it as an undergraduate math major.
i wish i had. Clearly his mind is vital for pushing the envelope, and his instincts on AI 's biggest impact being to push frontiers of science is spot on. more important than anything else.

[jd.]

Tim -- This was a great video. There was something about the aesthetic quality (the shallow depth of field/bokeh background) that was very eye catching. I also appreciated editing during the back-and-forth discussion sections; specifically, how it cut away to show relevant pages/info. Your skills as an interviewer are second to none!

[rodvik]

Wow first time I have heard Professor Bishop speak. What a sensible and measured thinker. Great interviewer as well. Thank you this made my day!

[_gunna]

Useful analogy from him at somewhere aroudn 16:00 or so:
"The remarkable thing about GPT4 is that you often see people when they first use it -- they'll ask "How tall is the Eiffel tower", and they'll be disappointed to just get the right answer. It's like being the keys to a very expensive cupholder and examining the cupholder; you don't realize that you have to start up the car and drive off in it to get the full experience (conversation, code, etc)"
I've had this experience multiple times

[darylallen]

22 minutes in and I'm loving what this guy has to say. Finally there is an expert in the field who can articulate every maddening experience I've had when I encounter people who say "LLMs can't do X." Or they say, "I tried to do x, y, z with the LLM and it failed, therefore LLMs are useless and will never be capable of anything more in the future."

[AICoffeeBreak]

Loved the whole show and the ending music!❤

[itssoaztek]

Such a great interview! Great job. Very good questions and answers.

[TMS-EE]

Superb conversation that really goes up a notch after 50 minutes to discuss AI in solving scientific problems (I've been interested in HPC for some time). I ordered the book midway thru watching. It was fascinating to see them discussing all of the topics that the current AI developments are leaving open for future research questions. Tho it's fascinating to me that his son, and co-author, is working at Wayve. Essential perspective on DL.

[houssamassila]

I had the absolute delight to read chapter 13 on GNNs. What a good and masterful description that was. I had it recommended to me by my supervisor but I don't know if I am allowed to state their name. I can't thank them enough for introducing me to The Bishop.

[marcfruchtman]

Thank you for this sage like Interview... I was really needing a primer on Neural Networks... and I believe Chris Bishop's books might be very helpful.
This interview has a lot of great insights -- for example, LLM are outperforming Specialist models of the past such as a specific AI that understood source code but the LLM did a better job. I think they will find that as the tech improves, specialist versions will do better, but the early versions were simply too specialized.

[diga]

Thank you for another great video. Amazing sound and video quality!

[Dan-hwiu]

Absolutely phenomenal interview, thanks Tim. Like Bishop, I lamented missing both the tumultuous 20th century physics and future space exploration. But now that creative AI exists which can _actually_ reason, I feel like a lotto winner. This next decade+ will be a revolution. Let's do out best to take care of one another along the way.

[JoonyoungKim-vf]

Hi thx for the great textbook

just wondering what would you recommend
1. start reading the deep learning textbook
2. start reading mathematics for machine learning then jump into the deep learning textbook.

I have poor mathematical background and wonder if i can read the deep learning textbook.

[ML_Indian]

Wow, wow, wow.
What a surprise MLST 🎉
❤
And in the intro(starting 2 minutes), the background music 🎶🎶, ahhhh, brilliant choice.

[michaelwangCH]

His book is still the reference of stats and ML students around world today. I am surprised that took Chris Bishop so long to renew his book - I am excited about his new ML book.

[JoeSmith]

'Just make the models bigger and harder to run for normal people and keep adding more and more data'... sure, maybe it could be the right strategy, but I doubt it and it seems more designed to back up what the big tech companies want. The actual innovation is coming from stuff like Mixtral being efficient at a reasonable size.

[SkilledApple]

This was a very insightful and interesting conversation!