Thread to Discuss Books/MOOCs


(Sanyam Bhutani) #1

Hello Everyone,

Since I have some time on my hands, I think I want to cover, read more stuff along with course.
So I wanted to create a discussion thread for these resources. :nerd_face:

I’ve created this thread, to discuss these resources. Also it’d be great if anyone would be interested in forming a book study group :slight_smile:

Here’s a target that I want to complete along with the MOOC:

  1. Learn Python 3 the Hard Way
  2. Python for Data Analysis 2nd ed
  3. Machine Learning and deep learning with Python
  4. Hands on Machine Learning with Scikit-Learn and Tensorflow
  5. Stanford CS231N
  6. Neural nets and Deep learning

Edit:
Bonus Targets:
3blue1brown Channel
Christopher Olah’s Blog
Machine learning Reddit

@Jeremy please let me know if I’m creating a redundant thread or I’m being spammy😅

Regards,
Sanyam Bhutani.


(Jeremy Howard (Admin)) #2

These all look like good resources to me - nicely curated! Also the 3blue1brown videos are cool, and the colah blog posts.


(Rahul Pathak) #3

Consider me in


(Sanyam Bhutani) #4

I’ve added them as well.
Kindly convert this into a wiki thread so that others may add resources as well.


(Jeremy Howard (Admin)) #5

That’s a good idea! Done :slight_smile:


(sergii makarevych) #6

How do you do that?


(Sanyam Bhutani) #7

You can edit the original post to add stuff but changing the type needs admin access I think.


(sergii makarevych) #8

Okay, now I get it. Jeremy did that, not you. Thanks for clarifying.


(Vitaly Bushaev) #9

How about adding some books on math that would useful for deep learning ?


(Rahul Pathak) #10

Maths for deep learning is Linear Algebra, Probability/Statistics and Calculus. This is my list
Coding the Matrix
Linear Algebra - Strang
Introduction to Probability Joseph K. Blitzstein
Thomas Calculus


(Jeremy Howard (Admin)) #11

IMO these books, whilst good math books, don’t provide the right background for deep learning.


(Rahul Pathak) #12

@jeremy can you give us some good references which we can follow for deep learning.


(Jeremy Howard (Admin)) #13

I don’t think studying any math references will help you do better in this course. However, if you have an interest in the math behind deep learning (and it becomes useful if you want to study academic papers, since they tend to be written in a math-heavy way), part 1 of this book covers all the necessary foundations: http://www.deeplearningbook.org/


(Mudit Verma) #14

Some other resources are ,
Backprop
Image Kernels
NLP
PCA
Data Science Tips & Tricks
GANs are Love!
Tensorflow Docs for beginners
(Although I prefer caffe & keras)
COLAH BLOG
amazing vision projects
karpathy sir !

These are a few resource to look. I go by looking at topics and reading from various blogs and videos rather than a single source , it gives me idea to how a problem can be tackled by same tools in different ways , but everybody has their own way of learning stuff !


(Mudit Verma) #15

Bible for me !


(Rahul Pathak) #16

Yes agree, and that’s why I have the hardcopy of this book already. Book will make more sense when I will follow that along with this course learning.


(Divyansh Jha) #17

added subreddit of machine learning a very good resource to interact with fellow AI engineers/researchers


(ecdrid) #18

I am also highly interested in forming a book study group…


(Dipjyoti Bisharad) #19

I would like to be in the book study group.


(Sanyam Bhutani) #20

Sounds great.
I’ll be going through Learn Python the HardWay and Python ML,DL with Scikit-Learn and Tensorflow first if anyone is interested :slight_smile:
Followed by Python For Data Analysis next week onwards.