by David Venturi

Dive into Deep Learning with 15 free online courses

Inceptionism: Going deeper into Neural Networks by Mike Tyka

Every day brings new headlines for how deep learning is changing the world around us. A few examples:

Want to see what the fuss is all about? Looking to master the technical content to advance your career or start your own company? I explored the open source project Class Central and found 31 online courses (15 of which are completely free) that cover everything from the basics of deep learning to the most cutting edge research today.

Class Central’s homepage.

Before we start, you may be asking yourself: “What exactly is deep learning?” Here’s a succinct description:

“Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.”
— Jason Brownlee from Machine Learning Mastery

Without further ado…

Online Deep Learning Courses

Creative Applications of Deep Learning with TensorFlow
via Kadenze
★★★★★ (14 ratings)

We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications. Free and paid options are available.

Prominent review (by Christopher Kelly): “I have an undergraduate degree in computer science … I’ve spent a ton of time on Khan Academy and Coursera and I’m blown away by the quality and professionalism of the content of this course. Highly recommended!”

Neural Networks for Machine Learning
University of Toronto via Coursera
★★★★★ (18 ratings)

Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well. Free and paid options are available.

Prominent review (by Bobby Brady): “This is one of those chance in a lifetime courses you have to get to learn from the greats. Geoffrey Hinton was one of the most important and influential researchers to work on artificial intelligence and neural nets back in the 80’s. Currently he is working with Google in their AI/deep learning initiatives.”

Geoffrey Hinton, the “godfather of deep learning,” who teaches Neural Networks for Machine Learning

Practical Deep Learning For Coders, Part 1
★★★★☆ (3 ratings)

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one — learning how to get a GPU server online suitable for deep learning — and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. Free.

Prominent review (by Anonymous): “This is really a hidden gem in a field that rapidly growing. Jeremy Howard does an excellent job of both walking through the basics and presenting state of the art results. I was surprised time and again when not only was he presenting material developed within the last year, but even within the week the course was running … You practice on real life data through Kaggle competitions. I would strongly recommend this course to anyone looking to go from zero real world experience to competing with experts in the field.”

6.S191: Introduction to Deep Learning
Massachusetts Institute of Technology (MIT)
★★★★☆ (1 rating)

A week-long intro to deep learning methods with applications to machine translation, image recognition, game playing, image generation and more. A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures. Free.

6.S094: Deep Learning for Self-Driving Cars
Massachusetts Institute of Technology (MIT)
★★★★☆ (1 rating)

This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application. Free.

Deep Learning
Google via Udacity
★★☆☆☆ (20 ratings)

In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods. Free.

Deep Learning for Natural Language Processing
University of Oxford

This is an applied course focusing on recent advances in analyzing and generating speech and text using recurrent neural networks. The mathematical definitions of the relevant machine learning models are introduced and their associated optimization algorithms are derived.

The course, which is free, is lead by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.

The University of Oxford is one of the world’s leading academic institutions and one of the oldest. They teach Deep Learning for Natural Language Processing.

CS224n: Natural Language Processing with Deep Learning
Stanford University

The course provides a thorough introduction to cutting-edge research in deep learning applied to NLP. On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some recent models involving a memory component. Through lectures (note: Winter 2017 videos now posted) and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems. Free.

CS231n: Convolutional Neural Networks for Visual Recognition
Stanford University

This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Free.

Stanford University hosts CS224n and CS231n, two popular deep learning courses.

Machine Learning
Nando de Freitas/University of British Columbia

This course focuses on the exciting field of deep learning. By drawing inspiration from neuroscience and statistics, it introduces the basic background on neural networks, back propagation, Boltzmann machines, autoencoders, convolutional neural networks and recurrent neural networks. It illustrates how deep learning is impacting our understanding of intelligence and contributing to the practical design of intelligent machines. Free.

Deep Learning Summer School 2015 and 2016
Various organizers (including Yoshua Bengio and Yann LeCun) via Independent

Deep Learning Summer School is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.

It isn’t organized like a traditional online course, but its organizers (including deep learning luminaries such as Bengio and LeCun) and the lecturers they attract make this series a gold mine for deep learning content. It is free.

Online Course on Neural Networks
Hugo Larochelle/Université de Sherbrooke

“Welcome to my online course on neural networks! I’ve put this course together while teaching an in-class version of it at the Université de Sherbrooke. This is a graduate-level course, which covers basic neural networks as well as more advanced topics.” Free.

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

This three-hour course (video and slides) offers developers a quick introduction to deep-learning fundamentals, with some TensorFlow thrown into the bargain. Free.

Deep Learning 101
Big Data University

The further one dives into the ocean, the more unfamiliar the territory can become. Deep learning, at the surface might appear to share similarities. This course is designed to get you hooked on the nets and coders all while keeping the school together. Free.

Big Data University teaches both Deep Learning 101 and Deep Learning with TensorFlow.

Deep Learning with TensorFlow
Big Data University

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you’ll use Google’s library to apply deep learning to different data types in order to solve real world problems. Free.

Deep Learning in Python

In this course, you’ll gain hands-on, practical knowledge of how to use neural networks and deep learning with Keras 2.0, the latest version of a cutting edge library for deep learning in Python. Partially free.

The following courses, sorted by rating, are all hosted on Udemy. Prices vary depending on Udemy discounts, which are frequent. You are often able to purchase access for as little as $10.

Please note that the offerings from Lazy Programmer Inc. come with a suggested order as per the “Useful Course Ordering” guideline in their course descriptions.


Deep Learning A-Z™: Hands-On Artificial Neural Networks
Kirill Eremenko and the SuperDataScience Team via Udemy
4.7 stars (388 ratings)

Learn to create deep learning algorithms in Python from two machine learning and data science experts. Templates included. This course is taught by the same instructor that teaches my top recommendation for intro to data science courses.

I ranked every Intro to Data Science course on the internet, based on thousands of data points

Zero to Deep Learning™: Mastering Keras
Data Weekends, Jose Portilla, Francesco Mosconi
4.8 stars (23 ratings)

Understand and build Deep Learning models for images, text, sound and more using Python and Keras.

Deep Learning Prerequisites: The Numpy Stack in Python
Lazy Programmer Inc. via Udemy
4.6 stars (1,551 ratings)

The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence. Free.

Data Science: Deep Learning in Python
Lazy Programmer Inc. via Udemy
4.6 stars (1,381 ratings)

A guide for writing your own neural network in Python and Numpy, and how to do it in Google’s TensorFlow.

Deep Learning Prerequisites: Linear Regression in Python
Lazy Programmer Inc. via Udemy
4.6 stars (751 ratings)

Learn linear regression from scratch and build your own working program in Python for data analysis.

Deep Learning Prerequisites: Logistic Regression in Python
Lazy Programmer Inc. via Udemy
4.6 stars (624 ratings)

Data science techniques for professionals and students — learn the theory behind logistic regression and code in Python.

Deep Learning: Convolutional Neural Networks in Python
Lazy Programmer Inc. via Udemy
4.6 stars (304 ratings)

This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

Data Science: Practical Deep Learning in Theano + TensorFlow
Lazy Programmer Inc. via Udemy
4.6 stars (293 ratings)

Take deep learning to the next level with SGD, Nesterov momentum, RMSprop, Theano, TensorFlow, and using the GPU on AWS.

Deep Learning: Recurrent Neural Networks in Python
Lazy Programmer Inc. via Udemy
4.6 stars (248 ratings)

GRU, LSTM, and more modern deep learning, machine learning, and data science for sequences.

Natural Language Processing with Deep Learning in Python
Lazy Programmer Inc. via Udemy
4.6 stars (194 ratings)

Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.

Unsupervised Deep Learning in Python
Lazy Programmer Inc. via Udemy
4.6 stars (153 ratings)

Autoencoders and Restricted Boltzmann Machines for Deep Neural Networks in Theano, and t-SNE and PCA.

Unleash Deep Learning: Begin Visually with Caffe and DIGITS
Razvan Pistolea via Udemy
4.5 stars (36 ratings)

An introduction to Deep Learning tools using Caffe and DIGITS where you get to create your own Deep Learning Model.

Deep Learning with TensorFlow
Packt Publishing via Udemy
3.9 stars (96 ratings)

Channel the power of deep learning with Google’s TensorFlow!

Deep Learning with Python
Packt Publishing via Udemy
3.4 stars (31 ratings)

Dive into the future of data science and implement intelligent systems using deep learning with Python.

Though it is more of a program than a singular online course, below you’ll find a Udacity Nanodegree targeting the fundamentals of deep learning.

At time of publishing, the current enrollment period is closed, but you can sign up for the waiting list to be notified when the next enrollment period begins.


Deep Learning Foundations Nanodegree
Siraj Raval via Udacity

Artificial Intelligence is transforming our world in dramatic and beneficial ways, and Deep Learning is powering the progress. Together with Siraj Raval, Udacity provides a dynamic introduction to this amazing field, using weekly videos, exclusive projects, and expert feedback and review to teach you the foundations of this future-shaping technology.

If you enjoyed reading this, check out some other explorations of Class Central’s dataset:

If you know of any courses that I may have missed, please let me know in the responses!

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Originally published on Class Central.