Deep Learning
"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 the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Stats for nerds
44recommend
86mentions
82people
2015–2026years
53% recommend40% neutral7% critical
Mentions per year
What Hacker News says
- I found Deep Learning by No Starch Press to be an excellent primer the foundation of AI:
- This Deep Learning textbook by Ian Goodfellow is a good intro to the fundamentals: https://www.deeplearningbook.org/
It does require some math, but has an appendix with a refresher.
Then, read through this list from Ilya Sutskevar: https://github.com/dzyim/ilya-sutskever-recommended-reading
Be patient. It might take a couple of years to get up to speed. :)
- I think Ian Goodfellow's "The Deep Learning" textbook fits well to the list.
- It depends?
1. Programming Philosophy?
- The Unix Philosophy
- 97 Things every programmer should know
2. CS Fundamentals
- The Elements of Computing Systems by Simon
- Algorithms by Jeff Erickson
3. Data Science
- Programming Collective Intelligence by Toby Segaran
- Deep Learning by Ian Goodfellow & Yoshua Bengio
- 4 trustworthy pdfs of very high quality that I've used as resources to fill my gaps:
- The 3 Kevin Murphy textbooks on Probabilistic Machine Learning
- Deep Learning by Goodfellow et al
I think they do contain some of the nuggets you're looking for, and have explanations too. I typically just use the index to find what I'm looking for but you could def pass them through a pdf llm tool to have natural language search/extract answers too eg PocketLLM [0]
- I don’t think it was just post 2022.
“Neural networks” “learned” data by being “trained” since they were first described in the late 1900s.
The same language was used in Ian Goodfellow’s (excellent) 2012 text book “Deep Learning”
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2019–2025
