Guide to Deep Learning

This blog contains a guide to get started with Deep Learning as well as get in-depth knowledge of the field.

The guide is divided into three sections:

1. Prerequisites: You will need experience in these before starting off with deep learning.
2. Basic: This contains resources for beginners, with an estimated time of completion.
3. Advanced: This contains resources for better insights into the mathematics and details of Deep Learning, provided for both theory and implementation.

Do give the disclaimer at the bottom a read.

Welp, the obsession is increasing day-by-day

I. Prerequisites

1. Basic knowledge of Python.

II. Basic

1. Udacity’s Intro to Machine Learning Course (UD-120): This course gives you hands-on learning of the basics of Machine Learning, which will be useful later on.

• Estimated time to complete: 1 week
• You can leave lecture 6 and 17

2. Book by Michael Neilsen: This book takes you through the basics of Deep Learning, covering what neural networks are as well as a thorough mathematical overview of the backpropagation algorithm. It also contains a comprehensive explanation of a Handwritten Digit Classifier written completely in NumPy.

• Estimated time to complete: 1–2 weeks
• Read all 6 chapters
• Try to code the classifier by yourself without referring to the book.

3. Stanford’s Computer Vision course (CS-231n) (Winter 2016): This course gives an overview of neural networks and CNNs, which are useful in the application of deep learning to the field of computer vision.

• Estimated time to complete: 1 week
• Watch the first 7 lectures
• The rest of the lectures give a brief overview of the different directions in which deep learning has been applied to the field of computer vision and are optional.
• Assignments of the course can be skipped

4. Andrew Ng’s Deeplearning.ai 5-Course Specialization: This specialization is a must for learning the basic theory of deep learning and getting a first-hand experience of applying it to some amusing tasks.

• Estimated time to complete: 1–1.5 months
• Don’t leave the assignments!
• Tip: Make sure to apply for financial aid 15 days before you plan to start the course so that you can do the assignments.

5. Blogs: The following blogs can be read for a better understanding of useful concepts in the field of ML and DL:

• Recommender System through Collaborative Filtering
• Introduction to k-Nearest Neighbours
• Blogs by Christopher Olah (read blogs of sections named Neural Networks, RNN, CNN, Visualizing Neural Networks)
• Ensembling Methods
• An Overview of Gradient Descent Optimization Algorithms
• Lecture Notes of CS-231N (read Module 1 and 2)
• Blogs by Machine Learning Mastery (refer for machine learning algorithms and their Python implementations)
• Blogs by Distill (generally has really good blogs to read)
• Introduction to RNNs (read parts 1 to 4)
• Maximum Likelihood Estimation (MLE)
• Maximum A-Posteriori (MAP)

III. Advanced

A. Theory

1. Deep Learning Book: This book is considered the bible of deep learning and gives a very thorough explanation of deep learning concepts.

• Parts 1 and 2 are a must read
• Part 3 can be read as per interest
• Many concepts and questions asked in ML and DL interviews are explained in this book.

2. Linear Algebra:

• Three Blue One Brown Playlist: A very nice visualization of linear algebra concepts is given in the videos, which helps to understand some common operations.
• Gilbert Strang’s Introduction to Linear Algebra Course: A lengthy but very comprehensive explanation of linear algebra concepts.

3. Probability:

• Statistics 110: A lengthy but comprehensive explanation of probability concepts.

4. Andrew Ng’s Machine Learning Course (CS-229): Usually an introductory course to the field of Machine Learning. A very thorough explanation of the mathematics behind some of the algorithms and theory learned in course UD-120 is given in this course.

5. Stanford’s Natural Language Processing Course (CS-224N) (Winter 2019): A very nice course for learning about the application of deep learning in the field of natural language processing. Both lectures and assignments are worth doing.

B. Implementation

1. Fast.ai: This course takes you through the process of implementing deep learning concepts, from the model architectures to the nit-picky details.

• Part 1: http://course18.fast.ai/index.html
• Part 2: http://course18.fast.ai/part2.html
• Jupyter Notebooks:
  a. dl1 : part 1 directory
  b. dl2 : part 2 directory

2. PyTorch

• Course by Facebook AI

3. Tensorflow

• Deeplearning.ai 2-Course Specialization

Disclaimer

1. This guide is based on the opinions of the members of VLG. There may be other ways to learn which suit different individuals.
2. This guide also does not contain resources for Reinforcement Learning as of now.