# Introduction:

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence

Because of the current accomplishments of artificial neural networks across a wide range of tasks deep learning has turned out to be to a great degree prevalent. This post plans to be a beginning stage for those inspired by adapting more about it.

In the event that you as of now have an essential comprehension of :

**a.) linear algebra, **

**b.) calculus, **

**c.) probability and **

**d.) any programming experience**

I suggest beginning with Stanford’s CS231n . The course notes are exhaustive and elegantly composed. The slides for every lesson are likewise accessible, and despite the fact that the going with recordings were expelled from the official website, re-transfers are very simple to discover on the web.

In the event that you don’t have the pertinent math foundation: There is an unbelievable measure of free material online that can be utilized to take in the required math learning. Gilbert Strang’s course on direct variable based math is an incredible prologue to the field. For alternate subjects, edX has courses from MIT in both math and probability .

On the off chance that you are keen on adapting more about machine learning: Andrew Ng’s Coursera class is a well-known choice as a top of the line in machine learning. See my earlier post on this.

Once you got these basics you can start these** 6 Easy steps to start learning Deep Learning:**

## Step 1. Read Free Online Books on Deep Learning

1.) Artificial Intelligence: A Modern Approach

2.) Deep Learning by Microsoft Research

3.) Deep Learning by Yoshua Bengio, Ian Goodfellow, and Aaron Courville

4.) Deep Learning Tutorial by LISA lab, University of Montreal

5.) Neural Networks and Deep Learning by Michael Nielsen

6.) Deep Learning in Neural Networks: An Overview

7.) Deep Learning applied to NLP

8.) Deep Learning for NLP (without Magic)

9.) Deep Learning for Natural Language Processing (Oxford)

10.) A Primer on Neural Network Models for Natural Language Processing

11.) An introduction to genetic algorithms

12.) neuraltalk by Andrej Karpathy

13.) Natural Language Processing with Deep Learning (Stanford)

## Step 2. Take Courses on Deep Learning

1.) Deep Learning Course by CILVR lab @ NYU

2.) Machine Learning – Carnegie Mellon by Tom Mitchell

3.) Machine Learning – Caltech by Yaser Abu-Mostafa

4.) Machine Learning – Stanford by Andrew Ng in Coursera

5.) A.I – Berkeley by Dan Klein and Pieter Abbeel

6.) A.I – MIT by Patrick Henry Winston

7.) Neural Networks for Machine Learning by Geoffrey Hinton in Coursera

9.) Neural networks class by Hugo Larochelle from Université de Sherbrooke

10.) Vision and learning – computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers

11.) Deep Learning – Nvidia

12.) Deep Learning for Natural Language Processing – Stanford

13.) Convolutional Neural Networks for Visual Recognition – Stanford by Fei-Fei Li, Andrej Karpathy

Also see my earlier post for 7 Best Online courses to Learn Deep Learning

## Step 3. Must See Videos and Lectures on Deep Learning

1.) What is Deep Learning | Deep Learning Simplified | Deep Learning

2.) TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Python

3.)Deep Learning, Self-Taught Learning, and Unsupervised Feature Learning By Andrew Ng

4.) Deep Learning of Representations by Yoshua bengio

5.) Machine Learning Discussion Group – Deep Learning w/ Stanford AI Labby Adam Coates

6.) Making Sense of the World with Deep Learning By Adam Coates

7.) The Unreasonable Effectiveness of Deep Learning by Yann LeCun

8.) Visual Perception with Deep Learning By Yann LeCun

9.) The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks

10.) Demystifying Unsupervised Feature Learning By Adam Coates

11.) Unsupervised Deep Learning – Stanford by Andrew Ng in Stanford (2011)

12.) Natural Language Processing By Chris Manning in Stanford

13.) Recent Developments in Deep Learning By Geoff Hinton

Also see my earlier post for 13 Top Best Deep Learning Videos, Tutorials & Courses on Youtube

## Step 4. Tutorials on Deep Learning

1.) The Best Machine Learning Tutorials On The Web

2.) Deep Learning for NLP (without Magic)

3.) Deep Learning from the Bottom up

4.) A Deep Learning Tutorial: From Perceptrons to Deep Networks

5.) Theano Tutorial

6.) UFLDL Tutorial 1

7.) UFLDL Tutorial 2

8.) Torch7 Tutorials

9.) VGG Convolutional Neural Networks Practical

10.) Neural Networks for Matlab

11.) Using convolutional neural nets to detect facial keypoints tutorial

11.) AT&T Laboratories Cambridge face database

12.) UC Irvine Machine Learning Repository

13.) Berkeley Segmentation Dataset 500

## Step 5. Frameworks on Deep Learning

1. DeepLearning4J

2. Caffe

3. Theano

4. Torch7

5. Brain

6. DeepLearnToolbox

7. Neon – Python based Deep Learning Framework

8. cuda-convnet

9. NuPIC

10. convetjs

11. Deepnet

12. Chainer – A flexible framework of neural networks for deep learning

13. OpenDL

14. Nvidia DIGITS – a web app based on Caffe

15. Minerva – a fast and flexible tool for deep learning on multi-GPU

16. Keras – Theano based Deep Learning Library

17. RNNLM Toolkit

18. MatConvNet: CNNs for MATLAB

## Step 6. Go through White Papers on Deep Learning

course site (10-805 DEEP LEARNING) for a perusing bunch @ CMU on Deep Learning. The prior papers traverse the unassuming beginnings of the neural system to the begin of the deep learning worldview. This course is as yet in progress and in this way more papers are en route:

The accompanying connection has an extremely far reaching perusing list for deep learning papers. Somebody comfortable with machine learning basics will increase much from a chose to peruse off the below page link:

Along these lines, you would build up a significantly more extensive comprehension of the subject.

### Miscellaneous on Deep Learning

1. Here’s a list of resources for people starting out with deep learning

2. Caffe Webinar

3. Misc from MIT’s ‘Advanced Natural Language Processing’ course

4. Google Plus – Deep Learning Community

5. TorontoDeepLEarning convnet

6. 100 Best Github Resources in Github for DL

7. Word2Vec

8. Caffe DockerFile

9. Implementing a Distributed Deep Learning Network over Spark

10. Torch7 Cheat sheet

11. Google deepdream – Neural Network art

12. Misc from MIT’s ‘Machine Learning’ course

13. A chess AI that learns to play chess using deep learning.

14. Reproducing the results of “Playing Atari with Deep Reinforcement Learning” by DeepMind

15. The original code from the DeepMind article + tweaks

16. Misc from MIT’s ‘Neural Coding and Perception of Sound‘ course

17. A recurrent neural network designed to generate classical music.

18. Misc from MIT’s ‘Networks for Learning: Regression and Classification’ course

Finally,

The most comprehensive source of information on Deep Learning I’ve found is Stanford CS231 class.

Just watch all these lectures and do all assignments.

#### End Note:

In the event that you’d get a kick out of the chance to prepare neural systems, you ought to presumably do it on a GPU. You don’t need to, however, it’s significantly quicker on the off chance that you do. NVIDIA cards are the business standard, and keeping in mind that most research labs utilize designs cards, there are a couple of reasonable cards that can likewise complete the work.

**Don’t be afraid to fail**. The majority of your time in deep learning will be spent trying to figure out why an algorithm didn’t pan out how you expected or why I got the error ABC… that’s normal. Tenacity is key. Just go for it. If you think some algorithm might work… try it with a small set of data and see how it does. These early projects are a sandbox for learning the methods by failing – to make use of it and give everything a try that makes sense.

**Related:**

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