Notes for Math in Computer science

Gradient from KhanAcademy: "https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/gradient-and-directional-derivatives/v/why-the-gradient-is-the-direction-of-steepest-ascent"

Easy calculus by Thompsom 1941: "http://djm.cc/library/Calculus_Made_Easy_Thompson.pdf" or "http://www.gutenberg.org/ebooks/33283"

Coding matrix by Klein through Python Programming: "http://codingthematrix.com/" including vector, matrix, eigenvalue, Gaussian, SVD.

Matrix calculus for Deep learning: "https://explained.ai/matrix-calculus/index.html"

Learning Deep Learning from non-CS Ph.D.: "https://vimeo.com/214233053"

Free courses in DL: https://simoninithomas.github.io/Deep_reinforcement_learning_Course/

Practices in DL: https://www.fast.ai/2019/01/24/course-v3/

To become a data scientist, focus on coding: https://www.fast.ai/2017/03/23/focus-on-coding/

DL glossary: http://www.wildml.com/deep-learning-glossary/

TensorFlow is an open source C++/Python software library for numerical computation using data flow graphs, particularly Deep Neural Networks. It was created by Google. In terms of design, it is most similar to Theano, and lower-level than Caffe or Keras.

Open source for CS: https://github.com/mvillaloboz/open-source-cs-degree

Google source: https://techdevguide.withgoogle.com/paths/

Software Engineering vs Computer Science: https://medium.freecodecamp.org/computer-science-vs-software-engineering-which-one-is-a-better-major-88482c38446b

Java for C++ programmer: http://pages.cs.wisc.edu/~hasti/cs368/JavaTutorial/

Python for Java programmer: http://python4java.necaiseweb.org/OOP/DefiningClasses
Python Numpy tutorial: http://cs231n.github.io/python-numpy-tutorial/#numpy-broadcasting

Learning Maths for Machine Learning and Deep Learning (Part 2)

I could not believe the response I got for my previous blog post learning maths for Machine Learning and Deep Learning. There are definitely lots of people like me, who are interested in learning math in greater depth. I’ve returned in 2018 with an updated list because I’ve been totally blown away by resources I’ve recently discovered.

Click here to share this article on LinkedIn »

The Essence of Linear Algebra (YouTube)

3Blue1Brown has created an amazing youtube video series entitled “The Essence of Linear Algebra”. The series of videos are highly visual and 3Blue1Brown explains linear algebra concepts from a geometric perspective. I must admit that 3Blue1Brown offers explanations and intuitions on linear algebra that I’d wish were taught in all books and lectures. I certainly found myself wishing that I’d had this resource way back when I was a first year engineering student. The custom animations are programmed in Python by 3Blue1Brown.

The Essence of Calculus (YouTube)

“The Essence of Calculus” is another gem from 3Blue1Brown. Also a youtube video series with custom animations, the series will make you feel like youdiscovered Calculus.

The Matrix Calculus You Need For Deep Learning (Online)

Terence Parr and Jeremy Howard (yes thats the same Jeremy Howard from fast.ai fame) explain Matrix Calculus from first principles. If you have programmed a neural network and implemented back propagation, this resource is the next step in completely understanding the underpinning mathematics.

Mathematics for Machine Learning (Book)

Still a work in progress book but chapters for Analytical Geometry, Linear Algebra and Vector Calculus look intuitive and comprehensive.

That’s all for the moment. Please share any useful resources you find in the comments. All the best in your Math travels….

Link: "https://itnext.io/learning-maths-for-machine-learning-and-deep-learning-part-2-ebbbc918724"

• Machine Learning
• Deep Learning
• Mathematics
• Linear Algebra
• Calculus

Happy New Year 2019

Living in Western Australia with my family - my wonderful wife and a little pretty daughter - is a great opportunity to experiment a new life in a different country from Vietnam. I know that the academic path in one of the highest education system in the world - Australia - is not as easy as doing research in my country. However, I think that it will benefit to the future job. In WA, I met many friends from Vietnam and overseas. I have just spent most of the spare time with my little family and Vietnamese friends. The most current important task of my life is to finish the candidacy report which is the first step to enroll the main Ph.D. program in CU. The short time in literature review may broaden my mind a little that makes me feel a lack of knowledge in my field. Hope that in this year I will boost my academic research with a great plan and the study guide from my supervisor.

As the first day of the new year 2019, I wish all my family members will have a great life, health, and happiness. I also try to study hard to finish my study plan in this year with the highest achievement. Besides, improving English skill is also a crucial task for my family that helps us easily adapt to the new life in Australia - an English speaking country.