Grokking Deep Learning
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks.
Stats for nerds
7recommend
15mentions
10people
2016–2025years
60% recommend33% neutral7% critical
Mentions per year
What Hacker News says
- The sutton and barto book is often given as an introductory ai book to people with no experience in ai or rl. This is unfortunate as it functions as neither a good rl book nor a good ai book.
Wheras the introductory book Grokking Deep Learning walks you through implementing your own pytorch, and has a portion about rl near the end, then has a follow up book on rl, and it is trivial to have your own from scratch model and framework playing tic tac toe, snake, even without any math skills beyond multiplication.
This happens without just smacking the reader with the modified bellman equation, and a bunch of chain rule backwards, and padded paragraphs intended to sell additional versions to universities.
- A lot of it was contract work. Some of it I didnt even have a contract. I have no agency. If i removed my gigs less than a year then my resume is going to be almost blank.
Secondly, on the ML front. I didnt just pick it up. Firstly, I practiced on and off for years. I failed to learn it handfuls of times, trudging through formal education resources about integrals and backprop and matrix math.
Eventually on one of my unemployment stints I found this book called Grokking Deep Learning and drove across the country and went into the redwoods in california, and I studied it for months on end. No internet on my desktop, only my laptop for looking stuff up. With no distractions the amount I accomplished was great.
Forgive me for getting angry but Im starting to get mad that people don't believe I could exist. I woke up at the god damn crack of dawn every day and studied ML, implemented autograd, my own little pytorch, and every architecture I could find, and read a zillion ml papers. And when I got bored in the afternoon I went and hiked up the mountain. Im a real human being. I am an RL expert. I dont think im special. I believe anyone else can do this too. People just don't know what they are capable of. They won't believe me because they don't even believe in themselves.
- You should take a look at Grokking Deep Learning[1] by Andrew Trask. Its one of the few books on Deep Learning which a student with high school mathematics knowledge can read and understand. DL is about programming the computer so its expected you would be able to write code using python.
- 1. Grokking Deep Learning (Andrew W. Trask) and Neural Networks and Deep Learning (Michael Nielsen)
2. I'll probably be off-point here, but maybe The Book of Why (Judea Pearl) could be interesting reading for you.
FloydHub has a great article about this topic on their blog: https://blog.floydhub.com/best-deep-learning-books-updated-f....
- - "Rationality: From AI to Zombies" - probably the most influential book I've read in my life, profoundly changed the way I think. It's a collection of LessWrong essays on science and rationality.
- "On Intelligence" and "I am a Strange Loop" - how mind works.
- "Rework", "Zero to One", "Start Small, Stay Small" - insightful startup advice.
- "Atomic Habits" - by far the best book on developing habits. Thought it was just another one of those self-help books, but turned out to be very insightful and well written.
- "The Obstacle Is the Way" - amazing introduction into stoic philosophy, by Ryan Holiday (author of Trust me I'm lying). He summarized everything I wanted to learn about the subject, and explained it extremely well. Absolute best book to read in hectic/stressful situations, audio version is great too.
- Fun autobiographies: Ghost in the Wires (Kevin Mitnick), iWoz (Steve Wozniak), Catch me if you can (Frank Abagnale), Just for Fun (Linus Torvalds), Elon Musk, Surely You're Joking, Mr. Feynman!
- How companies work: Creativity Inc (Pixar), In the Plex (Google)
- On writing: Art of fiction/nonfiction by Ayn Rand, Story (and Dialogue) by Robert McKee, Save the Cat, Step by Step to Standup Comedy.
- Grokking Deep Learning - by far the simplest and clearest introduction into deep learning, starts from scratch and takes you through the whole thing, without any of the scary/overwhelming math.
- Refactoring UI (design tips), Programmer’s Introduction to Mathematics, Master Algorithm - still reading these books, but they look REALLY good.
- Other: The Selfish Gene, Flow: The Psychology of Optimal Experience, Serious Creativity, Hackers & Painters, Hacking Growth, Angel (on angel investing, by Jason Calacanis).
Also collections of essays by Paul Graham [1] and Scott Alexander [2]:
[1] https://www.dropbox.com/s/2no0sqybnxurpcd/Paul%20Graham%20-%...
[2] https://www.dropbox.com/s/i43lqpdyd4qa255/The%20Library%20of...
- Good post. I want to add several great resources.
- Grokking Deep Learning
https://www.manning.com/books/grokking-deep-learning
This is a fantastic book that assumes no prerequisites other than knowing python, and takes you through the fundamentals of DL. It has very intuitive and easy to follow explanations, and doesn't use any libraries other than NumPy, so you're building the whole thing yourself, from scratch.
- Deep Learning With Python
https://machinelearningmastery.com/deep-learning-with-python...
This is kind of the opposite of the previous one, it doesn't go into math and theory, instead it guides you through building several practical projects with a very simple to use DL library(keras). It's a great way to gain practical experience in addition to theory from the previous book. Also has no prerequisites other than python, and makes it very easy to get started.
- 3blue1brown videos on neural networks:
https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...
Extremely brilliant high-level concise overview of how ANNs work. I highly recommend you get started here. You should also check out his videos on calulus and linear algebra, they're fantastic way to learn the math you need.
- Khan Academy videos - one of the easiest ways to learn the math prerequisites.
Calculus:
https://www.khanacademy.org/math/calculus-home
Linear Algebra:
https://www.khanacademy.org/math/linear-algebra
Probability and Statistics:
https://www.khanacademy.org/math/statistics-probability
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
http://shop.oreilly.com/product/0636920052289.do
I haven't read this one yet, but it looks very promising, and a lot of people seem to find it very useful.
- Andrew Ng's Coursera course
https://www.coursera.org/learn/machine-learning
Everyone knows about this one, I just think every article on AI resources should mention it, one of the most popular ways to get started with ML.
- New MIT courses on Self-Driving cars and AGI
https://selfdrivingcars.mit.edu
- The Master Algorithm
https://www.audible.com/pd/Science-Technology/The-Master-Alg...
Excellent high-level overview of ML field and algorithms.
====
Other great stuff:
- Artificial Intelligence: A Modern Approach
The leading textbook in Artificial Intelligence. It's not the fastest way to get started, but it's considered one of the best AI textbooks ever written.
- Stanford AI course (CS 188)
https://www.youtube.com/playlist?list=PLIeooNSdhQE5kRrB71yu5...
Brilliant course based on AIMA. Not DL, but solid fundamentals of AI and ML.
- Couple of great playlists on DL, just to complete the collection:
Machine Learning with Python
https://www.youtube.com/playlist?list=PLQVvvaa0QuDfKTOs3Keq_...
Neural Networks Demystified
https://www.youtube.com/watch?v=bxe2T-V8XRs&index=1&list=PLi...
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