Books recommendation series: Elements of statistical learning

Since long I was thinking to write down my recommendation of the books I have read recently or in past as well. The plan is to post atleast one book recommendation weekly.

Check below my first recommendation.

Book

Elements of statistical learning
Hastie, Tibshirani, and Friedman (2009 )
https://web.stanford.edu/~hastie/ElemStatLearn/

Theme

During the past decade has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting–the first comprehensive treatment of this topic in any book. 

Takeaway

It is a rigorous and mathematically dense book on machine learning techniques. It gives a very good explanation of how the correlation between Bias, Variance and Model Complexity works. If you have the mathematical background (calculus, linear algebra etc) this is a very good introduction to Machine Learning and covers most of the MI topics. I can say that it has a nice balance between mathematical concepts and intuitive reasoning.
I highly recommend this book for anyone entering to the field of AI/ML.

Suggestions

  • What this book doesn’t provide? is a pragmatic approach or Hands-on practice.
  • Deep analysis of why a specific method works (but it gives you some intuition about what a method is trying to do)
  • If you are doing self-study and don’t have any background in machine learning or statistics advice to refine your understanding of linear algebra and calculus before reading this book.
  • Free PDF is available but suggest to buy a print book.

Download and purchase link

Buy: https://web.stanford.edu/~hastie/ElemStatLearn/
PDF: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf



Deep learning specialization notes

A couple of months back I have completed Deep Learning Specialization taught by AI guru Andrew NG. During the learning process, I have made personal notes from all the 5 courses.  Notes are based on lecture video and supplementary material provided and my own understanding of the topic. I have used lots of diagrams and code snippets which I made from course videos and slides. I am fully complying with The Honor Code. No programming assignment and solutions are published on GitHub or any other site.

Please note that most of the places I am not using exact mathematical symbol and other notation, instead using plain English name this is just to save some time, also please note that this is a personal diary made during course and I guess a bit longer too and few places not very well organized, so in any form doesn’t replace the content and learning process one follows during course which includes quizzes, programming assignments, project etc. This is a great course so I encourage you to enroll.

What you will learn at the end of the specialization:

Neural Networks and Deep Learning: This course gives foundations of neural networks and deep learning. How to build and train. At the end of this course, we’ll in position to recognize cat so will make a cat recognizer.  [PDF]

Improving Deep Neural Networks – Hyperparameter Tuning, Regularization and Optimization: In this course, we’ll learn about practical aspects of the NN. Now you have made NN/deep network so the focus is on how to make it perform well. We’ll fine tune various things like hyperparamater tuning, regularization algorithms and optimization algorithms like RMSProp, Adam etc. So this course helps greatly in making model perform well.  [PDF]

Structuring your Machine Learning Project: In this course, we’ll learn how to structure machine learning projects. It is observed that strategy for machine learning projects has been changed a lot in deep learning era. For example, the way you divide data in train/test/dev set has been changed in the era of deep learning also whether train and test data comes from the same distributions etc.? we’ll also learn about end-to-end deep learning. The material in this course is relatively unique.  [PDF]

Convolutional Neural Networks(CNN): CNN is often applied in images mainly in computer vision problems. In this course, we’ll learn about how to make these models using CNN’s.  [PDF]

Natural Language Processing-Building Sequence Models: In this course, we’ll learn about algorithms like Recurrent Neural Network (RNN’s), LSTM (Long Short-Term Memory) and learn how to apply them with the sequence of data like natural language processing, speech recognition, music generation etc.  [PDF]

 

Happy learning!

Pl, drop a note in case of any feedback.

 

References:

Deep Learning Specialization:
https://www.deeplearning.ai/

Github (Source code and diagrams used in notes):
https://github.com/ppant/deeplearning.ai-notes

Deep learning Specialization completion certificate: https://www.coursera.org/account/accomplishments/specialization/WVPVCUMH94YS