In this blogpost, I will be addressing the question which I get all the time from colleagues and researchers:
” How to get started with Deep Learning? “
As with learning any new concept in the computing field, there are two aspect:
- Theoretical understanding of the topic
- Hands-on and practical implementation
Therefore, I decided rather than writing the concepts here, I will cite the best resources that helped me personally take off in this area.
So lets get started!
In terms of books, there are many good books out there that focuses on the mathematical details behind deep learning, however, I find those two books are easy read and cover so much details that helps you to build a strong foundation without digging too much into the maths:
- Deep Learning with Python by François Chollet
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Tensorflow tutorials are the best for hands-on experience with different deep learning models and architectures, I highly recommend that you try to run some of their code and observe the results.
Moving on to online courses:
- Coursera’s Deep Learning course is very helpful and interesting.
When it comes to youtube channels, here are my recommendations:
For more recommendations on resources Python Programmer is a great youtube channel.
Finally, I would recommend reading many many research papers in deep learning and its applications. Not only they show how fascinating and powerful deep learning is, but they provide an in-depth explanation on different implementation techniques.
By the end of completing all the above, you should be good to go.
Please let me know if you found these resources to be helpful and feel free to add any other recommendations in the comment section below.
With you a great day!
PS. I am not sponsored or affiliated by any of these authors or organisations.