Neural Networks and Deep Learning
by Charu C. Aggarwal · first published 2018
97% recommend1% neutral1% critical
Affiliate links. As an Amazon Associate I earn from qualifying purchases.
Mentions per year
What Hacker News says
Related books
2014–2019Neural networks
Raúl Rojas
Recommended by 2 · mentioned by 22 recommend · 2 mention2 recommend · 22 recommend
2016–2023Hands-On Machine Learning with Scikit-Learn and TensorFlow
Aurélien Géron
Recommended by 16 · mentioned by 1816 recommend · 18 mention16 recommend · 1816 recommend
2019–2024Deep Learning from Scratch
Seth Weidman
Recommended by 4 · mentioned by 94 recommend · 9 mention4 recommend · 94 recommend
2014–2026Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Recommended by 85 · mentioned by 10885 recommend · 108 mention85 recommend · 10885 recommend
2016–2026Python machine learning
Sebastian Raschka
Recommended by 5 · mentioned by 115 recommend · 11 mention5 recommend · 115 recommend
2007–2026Pattern Recognition and Machine Learning
Christopher M. Bishop
Recommended by 80 · mentioned by 10480 recommend · 104 mention80 recommend · 10480 recommend
2016–2017Deep Learning: A Practitioner's Approach
Josh Patterson, Adam Gibson
Recommended by 1 · mentioned by 41 recommend · 4 mention1 recommend · 41 recommend
2016–2024Make your own neural network
Tariq Rashid
Recommended by 11 · mentioned by 1211 recommend · 12 mention11 recommend · 1211 recommend
I really like Aggarwal's "Neural Networks and Deep Learning". I recommend it highly. (At least this summer Springer was giving away a PDF - not sure if that's still true.) To me, Goodfellow et al. spent the first hundred and fifty pages on stuff which is important,…
on “The Elements of Statistical Learning [pdf]” ↗…over implementation so it's more for developing a principled understanding of machine learning fundamentals than for getting you up to speed with modern tools necessary for solving real problems. 2. "Neural Networks and Deep Learning" - Nielson This is a free online book that is very accessible and engaging. It covers basic theory and implements examples in Python.
on “Ask HN: Starting machine learning? Books, videos and other resources” ↗From the yc reading list 2015 [http://themacro.com/articles/2015/12/yc-2015-reading-list/], they recommend [for Neural Networks] this book: http://neuralnetworksanddeeplearning.com/ It's more about understanding than "learning tools."
on “Google's Free Deep Learning Course” ↗Looking at headers: Last-Modified: Tue, 30 Apr 1996 18:53:31 GMT Still, looks good, if a bit dated. For me, these two tutorials were the ones that helped me understand NN the most: - http://neuralnetworksanddeeplearning.com/ - https://mattmazur.com/2015/03/17/a-step-by-step-backpropagat...
on “A Basic Introduction to Neural Networks (2007)” ↗For anyone looking for a broad intro to neural networks, I read a bunch of stuff when I was getting started and these two were the most outstanding: http://neuralnetworksanddeeplearning.com/ http://cs231n.stanford.edu/
on “Introduction to Convolutional Neural Nets” ↗I've found http://neuralnetworksanddeeplearning.com by Michael Neilson to be an excellent resource.
on “Generating Magic cards using deep, recursive neural networks” ↗This looks interesting, can't wait to dig into it. Another great great free online book on this topic: http://neuralnetworksanddeeplearning.com/
on “Free “Deep Learning” Textbook by Goodfellow and Bengio Now Finished” ↗Yes, I did my research but there is no such interactive tutorial online like Treehouse or Codecademy. There are so many tutorials but none of it tells you the whole path. Here are the resources I found useful: ========================================== Advices from Open AI, Facebook AI leaders Courses You MUST Take: Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning)
on “Ask HN: Why there is no Codecademy for ML or AI?” ↗This one http://neuralnetworksanddeeplearning.com It goes much deeper then the videos. Also it is free.
on “Intro to Deep Learning” ↗Not a full course I'd say, but I've used this one to learn the math behind deep neural networks and code my own from scratch in elixir and C: http://neuralnetworksanddeeplearning.com/
on “Andrew Ng updates his Machine Learning course” ↗To be honest, this isn't the best list, it's a bit too blog heavy. I've started reading up on ML only recently but here are my recommendations. Note that I haven't went through all of them in entirety but they all seem useful. Note that a lot of them overlap to a large degree and that this list is more of a "choose your own adventure" than "you have to read all of these". Reqs: * Metacademy (htt
on “Machine Learning for Software Engineers” ↗A less mathy explanation with some real examples: http://neuralnetworksanddeeplearning.com/chap1.html Coding a digit recognizer using a neural network is an extremely rewarding exercise and there's a lot of help on the web to get you started.
on “What does a neural network actually do?” ↗Not having read Rojas' book, I think the best text I've ever read on the subject is Michael Nielsens (in progress) "Neural Networks and Deep Learning." [1] Chapter 2 covers back propagation. It has a similarly approachable yet formal style, and I have recommended it to people with no ML experience in the past who have found it very intuitive. [1] http://neuralnetworksanddeeplearning.com
on “A Gentle Introduction to Backpropagation” ↗See also Michael Nielsen's book Neural Networks and Deep Learning http://neuralnetworksanddeeplearning.com/
on “Deep Learning: An MIT Press book in preparation” ↗I wanted to share this resource as well. I started this online free book "Neural Networks and Deep Learning" (http://neuralnetworksanddeeplearning.com/). I think it has a pretty good explanation and illustration.
on “Hacker's Guide to Neural Networks” ↗