New to Deep Learning? Want to lean how to write Deep Learning programs from the beginning, with simple step-by-step examples?

Build Deeper: The Path to Deep Learning is a complete and practical guide that can help you take the first few steps in deep learning. It will guide you step-by-step, from understanding the basic concepts, to building your first practical application.

It covers:
  • What Deep Learning is, and where it fits with Artificial Intelligence and Machine Learning.
  • How Deep Learning came to be, its predecessors, and the path it took to evolve into what it is today.
  • The important milestones it has passed through the years, and the impact they had on the field.
  • What tools are available for us to learn and build deep learning applications, and how to set them up: Python, TensorFlow, Theano, Keras, and more, on any OS of your choosing: Windows, Linux, or Mac OS.
  • Building our first simple deep learning model.
  • The internal workings of a deep learning model.
  • Using more advanced topics such as Data Augmentation, Transfer Learning, Bottleneck Features, and Fine Tuning to build a practical deep learning application.
  • Getting started with Computer Vision.

All you need now is a little enthusiasm … who knows where it will take you!

Go a little deeper to discover...

Available in Amazon, in Paperback and Kindle.

Here is a collection of resources to get you started with Deep Learning, Machine Learning, Computer Vision, Image Processing, Python, and OpenCV.


  • PyImageSearch - PyImageSearch by Adrian Rosebrock is an excellent place to learn OpenCV and Image Processing. 
  • Machine Learning Mastery - Machine Learning Mastery by Jason Brownlee has an excellent guide to get you started in Machine Learning concepts.
  • Adventures in Machine Learning - Adventures in Machine Learning by Andy Thomas has excellent tutorials on TensorFlow and CNNs, with very detailed explanations.
  • Adit Deshpande'd Blog - Has comprehensive articles on the internals of Deep Learning. 

Useful Articles


Deep Learning Research Papaers

  • AlexNet - "ImageNet Classification with Deep Convolutional Neural Networks"
    - 2012 - Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
  • ZF Net - "Visualizing and Understanding Convolutional Networks"
    - 2013 - Matthew D. Zeiler, Rob Fergus
  • VGG Net - "Very Deep Convolutional Networks for Large-scale Image Recognition"
    - 2014 - Karen Simonyan, Andrew Zisserman
  • GoogLeNet - "Going Deeper with Convolutions"
    - 2015 - Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, Google Inc., University of North Carolina, Chapel Hill, University of Michigan, Ann Arbor, Magic Leap Inc.
  • Microsoft ResNet - "Deep Residual Learning for Image Recognition"
    - 2015 - Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Microsoft Research


A collection of books which I find useful on Deep Learning, Computer vision, and Machine Learning topics in general.
(Note: The Amazon links in the following are affiliate links)

Deep Learning (Adaptive Computation and Machine Learning series) - The MIT Press - Ian Goodfellow, Yoshua Bengio, Aaron Courville

This book, I would say, should be a must-have for any Deep Learning enthusiast. It's written by three experts in the Deep Learning field, and gives a clear flow of knowledge from the mathematical background to the practical aspects of Deep Learning. I would highly recommend this.

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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - O'Reilly Media - Aurélien Géron

As the name suggests, this is the "Hands-on" book for Scikit-Learn and TensorFlow. It's filled with practical scenarios with step-by-step code. I highly recommend this book.

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Python Machine Learning – by Sebastian Raschka

The 'go to' book for Python Machine Learning. If you are unsure where to start Machine Learning in Python, start with this book.

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TensorFlow For Machine Intelligence: A hands-on introduction to learning algorithms - by Sam Abrahams (Author), Danijar Hafner (Author), Erik Erwitt (Author), Ariel Scarpinelli (Author), Troy Mott (Editor)

New to TensorFlow? Try this book to get started.

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Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library - by Adrian Kaehler, Gary Bradski

Compared to OpenCV 2, resources for OpenCV 3 used to be bit hard to come by. This book changes it. Although it focuses on C++, I would recommend Python programmers to also read it, as it comprehensively covers features of OpenCV 3.

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Automatic Speech Recognition: A Deep Learning Approach (Signals and Communication Technology) - by Dong Yu, Li Deng

Dive into Automatic Speech Recognition using Deep Neural Networks. This is one of the very few book covering this field.

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Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners - by Scott Hartshorn

Random Forests can be extremely powerful for certain machine learning scenarios. This is an excellent book to learn the concepts behind Decision Trees and Random Forests.

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