## Getting Started with Deep Learning and Python

**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.

To Choose which language Python Vs R is good for Machine Learning see the case study **here**.

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 Basics of 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 Online Free Courses on Deep Learning**

I would recommend to see first ** 7 Best Online courses to Learn 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**

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

Firstly I would recommend **13 Top Best Deep Learning Videos, Tutorials & Courses on Youtube**

**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**

**Step 4. Deep Learning FREE Tutorials**

**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.

PS: Want to know in-depth AI and ML latest resources around the web you MUST see **this index page here**.

**Closing 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:**

**HOW TO LEARN MACHINE LEARNING IN 90 DAYS**

**6 Easy Steps To Get Started Learning Artificial Intelligence**

**Index of Best AI/Machine Learning Resources**

**10 Free Training Courses on Machine Learning and Artificial Intelligence**

** A Complete Guide on Getting Started with Deep Learning **

**Learn TensorFlow and deep learning, without a Ph.D.**

**Python vs R for Machine Learning**

**3 Best Free Python IDE for machine learning**

**15 algorithms machine learning engineers must need to know**

**10 Top Videos, Lecturers & Courses on Machine Learning for Beginners and Advanced**

**10 Free Must-Read eBooks on Machine Learning Basics**

**Top 10 Best Deep Learning Videos, Tutorials & Courses on Youtube from 2017**

**7 Best Online courses to Learn Deep Learning**

**Top 50 Best YouTube Videos on Neural Networks**

**Essential Cheat Sheets for Machine Learning Python and Maths**

**Deeplearning.ai: Announcing New Deep Learning Courses on Coursera**

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