Deep Learning (DL) is a subset of machine learning based on artificial neural networks. DL can be supervised, semi-supervised or unsupervised and has seen success in industries such as healthcare, finance, gaming, customer service, retail and many more. Whilst the idea of DL was born as early as 1940, and the first mathematical model of a neural network was established back in 1943 by Walter Pitts and Warren McCulloch, it has only been in recent years that these methods have had a real-world impact on both business and society. As AI becomes prevalent in every industry, DL techniques are becoming increasingly popular when looking to solve challenges. Whilst it's important to make sure that the problem in hand actually requires DL and not to jump straight into it because of the current hype in the space, there are several areas where is is the best tool available.

If you're working in AI and already have a solid foundation, you may be looking towards DL models. As a collaborative community spearheaded by the likes of Google, open-sourcing is an incredibly useful and popular tool when starting out.

Here are our top 10 resources for getting started in Deep Learning:

  1. Deep Learning Courses, Coursera
    "In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing."

    Founded by Andrew Ng and Daphne Koller, Coursera is an online learning platform offering some of the leading courses available online.
  2. Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville
    An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

    Ian, Yoshua and Aaron have all spoken at RE•WORK summits and you can watch videos of their presentations and interviews on our YouTube channel as a bonus resource!
  3. Practical Deep Learning for Coders
    To do this course, all you need is to have been coding for at least a year and to have a GPU and appropriate software. he course is video based and will run you through each session step by step. "You don’t need much data, you don’t need university-level math, and you don’t need a giant data center."
  4. An Introduction to PyTorch – A Simple yet Powerful Deep Learning Library
    "Every once in a while, a python library is developed that has the potential of changing the landscape in the field of deep learning. PyTorch is one such library."
    This article takes a hands on approach to PyTorch covering the basics, and also providing case studies. Whilst this article assumes that you have a basic understanding of DL, it provides additional resources for complete beginners.
  5. YouTube Deep Learning Playlist
    With presentations, interviews and fireside chats from some of the global leaders in the space, this free resource brings together some of the most cutting edge research and applications of deep learning. Watch videos from Yoshua Bengio, Yann LeCun, Geoffrey Hinton, Andrew Ng, Chelsea Finn, Ian Goodfellow and more.
  6. TensorFlow
    If you haven't used TensorFlow before, now is the time to get started. "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications." It allows you to build and train DL models easily, train and deploy them on the cloud, on device or in the browser in a variety of languages, and is a simple and flexible architecture.
  7. Deep Learning Summer School Talks
    "Deep neural networks are a powerful method for automatically learning distributed representations at multiple levels of abstraction. Over the past decade, they have dramatically pushed forward the state-of-the-art in domains as diverse as vision, language understanding, robotics, game playing, graphics, health care, and genomics. The Deep Learning Summer School (DLSS) covers both the foundations and applications of deep neural networks, from fundamental concepts to cutting-edge research results."

    These free videos are from events hosted by the Canadian Institute for Advances Research (CIFAR) and the Vector Institute.
  8. Information Theory of Deep Learning. Naftali Tishby
    YouTube video: The workshop aims at bringing together leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience. No formal submission is required. Participants are invited to present their recently published work as well as work in progress, and to share their vision and perspectives for the field.
  9. Social Networks and Communities
    There's a lot to be said about the likes of GitHub, Quora, and LinkedIn groups. As previously mentioned, the DL world is a collaborative one, and experts and beginners alike are constantly striving to help each other as well as improve their own models.
  10., Deep Learning
    Of the 1,557,544 e-prints available on arXiv, there are 16,355 DL papers. arXiv is owned and operated by Cornell University, a private not-for-profit educational institution. arXiv is funded by Cornell University, the Simons Foundation and by the member institutions.

Interested in a more hands on approach to Deep Learning? Join us for the Global Deep Learning Summit series, where we bring together leading experts in the field to share their most cutting-edge work and research progressions as well as exploring how it is impacting society.