# Favoured

**MLAPP**(Kevin Murphy), Machine Learning - A Probablistic Perspective, is more**comprehensive, insightful and interesting**, and contains more "real" examples/problems. However, the presents are kinda**out of order**, which can be difficult to follow for a first book.**PRML**(Christopher Bishop), Pattern Recognition and Machine Learning, is for the ones are very comfortable with calculus/linear algebra.**ESL**(Trevor Hastie), Elements of Statistical Learning, 2n ed. Springer 2017.**MLY**(Andrew Ng), Machine Learning Yearning (《机器学习秘籍》), and someone's notes/understanding of this book: part 1 (bak), part 2 (bak) and part 3.

# Good Text Books

Machine Learning by Peter Flach (Cambridge University Press 2012) - The Art and Science of Algorithms that Make Sense of Data. [The examples in this book are clear and easy to understand.]

Machine Learning by Tom M Mitchell (McGraw-Hill 1997). (Classical, many original terms and ideas.)

PRML by Christopher Bishop.

# Deep Learning

- DL With Python, by Francois Chollet (also the author of Keras). Detailed introduction of CNN & RNN on CV & text analysis in Keras.
- DL for CV with Python [//trilogy], by Adrian Rosebrock.
- Hands on ML with SL and TF [//genius for clarity], by Aurélien Géron.
- DL - A Practioner's Approach, [java], by Adam Gibson & Josh Patterson.
- DL with Python, by Jason Brownlee. [//with code, easy to understand]
- Practical Python and Open CV, by Adrian Rosebrock. [// fast into CV]
- DL with TF, by Giancarlo Zaccone, [// DL 101]. ref(cn)