Dive into Deep Learning: 15 Free Online Courses for Mastering AI

Deep learning is driving a new era of artificial intelligence. In the past decade, this subfield of machine learning has achieved remarkable breakthroughs in areas like computer vision, natural language processing, robotics, and autonomous systems. Tech giants like Google, Facebook, Microsoft, and Amazon are heavily investing in deep learning research and applications, while startups are harnessing its power to disrupt industries from healthcare to finance to manufacturing.

As Andrew Ng, a leading AI researcher and founder of deeplearning.ai, puts it: "AI is the new electricity. Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don‘t think AI will transform in the next several years."

The numbers speak for themselves. According to a report by Markets and Markets, the global deep learning market size is expected to grow from $3.18 billion in 2018 to $18.16 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 41.7% during the forecast period. A McKinsey Global Institute report estimates that AI could create an additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year.

At the heart of this AI revolution are artificial neural networks, the key algorithms powering deep learning. First proposed in the 1940s and initially known as "cybernetics", neural networks are mathematical models loosely inspired by the biological neural networks in the human brain. As data flows through the network, each artificial neuron (or node) performs a simple computation and passes the result to connected neurons in the next layer. With enough nodes and layers, neural networks can learn complex patterns and functions from labeled training data.

The concept of artificial neural networks dates back to 1943, when neurophysiologist Warren McCulloch and mathematician Walter Pitts published a seminal paper on how neurons might work. They modeled a simple neural network using electrical circuits. But it wasn‘t until the 1980s that neural network research really took off, thanks to the pioneering work of Geoffrey Hinton, David Rumelhart, and others.

In 1986, Hinton co-authored a landmark paper that introduced the backpropagation algorithm for training multilayer perceptrons, a class of feedforward neural networks. Backpropagation allowed neural networks to adjust their connection weights to minimize the difference between predicted and actual outputs, enabling them to learn complex mappings from input to output.

However, neural networks soon fell out of favor in the 90s and early 2000s, as they proved difficult to train and often performed worse than other machine learning methods. "The community lost interest in neural networks for a decade, as recounted in a Wired article," wrote LeCun.

All that changed in 2012, when Hinton and his students at the University of Toronto made a major breakthrough. They trained a deep convolutional neural network (CNN) that almost halved the error rate on the ImageNet image classification challenge. Their AlexNet paper, published in the proceedings of NeurIPS, showed that deep learning could outperform traditional computer vision algorithms, ushering in a new era of AI.

ImageNet classification error rates:

• 2011 (pre-deep learning): 25.8%
• 2012 (AlexNet): 16.4%
• 2015 (ResNet): 3.57%

Today, state-of-the-art CNNs can classify images with an accuracy of over 99%, surpassing human-level performance. "The development of deep learning is a remarkable story," reflected Hinton in a 2017 interview. "It went from being a crazy idea that almost no one believed in, to being a technology that‘s indispensable."

Deep learning has also made significant strides in natural language processing (NLP), the field of AI focused on understanding and generating human language. In 2011, Google researchers Jeff Dean and Samy Bengio developed word2vec, an unsupervised learning algorithm that could map words to high-dimensional vectors, capturing their semantic meaning.

By training on large text corpora like Google News, word2vec could learn that words like "king" and "queen" or "brother" and "sister" are semantically related. This allowed NLP models to go beyond simple keyword matching and reason about the relationships between words.

More recently, a new breed of language models based on the Transformer architecture, such as Google‘s BERT and OpenAI‘s GPT-3, have achieved remarkable results on a wide range of NLP tasks. By training on massive amounts of unlabeled text data, these models can perform reading comprehension, question answering, language translation, and even open-ended text generation at near-human levels.

"The past few years have seen an explosion of language models in NLP. Today, if you have any task related to language, you‘ll likely use some kind of deep learning model, often pre-trained on a large amount of text," said Sebastian Ruder, a research scientist at DeepMind, in a blog post.

Deep learning is also making waves in the field of robotics and autonomous systems. By combining computer vision, NLP, and reinforcement learning, researchers are developing robots that can navigate complex environments, manipulate objects, and even carry out multi-step instructions.

In 2018, researchers from UC Berkeley and Google Brain introduced QT-Opt, a deep reinforcement learning algorithm that could train a robot hand to solve a Rubik‘s cube with unprecedented dexterity. The algorithm, based on a combination of CNNs and recurrent neural networks (RNNs), learned to control the robot hand through trial and error, guided by a reward function.

"What excites me the most is using deep learning to build autonomous systems that can perceive, reason, and act in the real world," said Pieter Abbeel, a pioneer of deep reinforcement learning and professor at UC Berkeley, in an interview. "This is really the core of artificial intelligence—the quest to build machines that can match and ultimately surpass human intelligence."

But deep learning isn‘t just advancing the frontiers of research—it‘s also driving real-world applications across industries. In healthcare, deep learning is being used to analyze medical images, predict disease progression, and discover new drugs. In finance, it‘s powering fraud detection, credit risk assessment, and algorithmic trading. In manufacturing, it‘s enabling predictive maintenance, defect detection, and supply chain optimization.

"We‘re seeing deep learning being applied to an incredibly wide range of problems, from detecting cancer to generating art to playing complex strategy games," said Jeff Dean, Google Senior Fellow and head of Google AI, in a talk. "And we‘re still just scratching the surface of what‘s possible."

So how can you get started with deep learning and become part of this AI revolution? Fortunately, thanks to the rapid growth of online education, there are now dozens of excellent free courses that can take you from beginner to expert, no PhD required.

Here are 15 of the best free online courses for diving into deep learning, taught by some of the pioneers and leaders of the field:

1. Neural Networks for Machine Learning

• Instructor: Geoffrey Hinton (Google/University of Toronto)
• Platform: Coursera
• Duration: 16 weeks
• Prerequisites: Basic programming, linear algebra, probability
• Assignments: Quizzes, programming exercises in MATLAB/Octave

In this foundational course, the "Godfather of Deep Learning" himself provides a comprehensive introduction to feedforward and recurrent neural networks, backpropagation, Boltzmann machines, autoencoders, and more. Through lectures and programming exercises, you‘ll learn the core concepts and algorithms that underpin modern deep learning.

2. Intro to Deep Learning

• Instructors: Alexander Amini and Ava Soleimany (MIT)
• Platform: GitHub/YouTube
• Duration: 8 weeks
• Prerequisites: Python programming, linear algebra, calculus, probability
• Assignments: Labs in TensorFlow and PyTorch, final project

This introductory course from MIT covers the fundamentals of deep learning, from basic neural networks to CNNs, RNNs, and unsupervised learning. Through weekly labs and a final project, you‘ll gain hands-on experience building and training neural networks in TensorFlow and PyTorch, two of the most popular deep learning frameworks.

3. CS231n: Convolutional Neural Networks for Visual Recognition

• Instructors: Fei-Fei Li, Justin Johnson, Serena Yeung (Stanford)
• Platform: Stanford University Website/YouTube
• Duration: 10 weeks
• Prerequisites: Python programming, linear algebra, calculus
• Assignments: Programming assignments in NumPy/TensorFlow, final project

This in-depth course focuses on the application of deep learning to computer vision, covering state-of-the-art techniques for image classification, object detection, face recognition, and more. Through lectures and assignments, you‘ll learn how to implement and train CNNs from scratch, as well as use popular deep learning libraries.

4. Deep Learning Specialization

• Instructor: Andrew Ng (deeplearning.ai)
• Platform: Coursera
• Duration: 4 months
• Prerequisites: Python programming, linear algebra, calculus
• Assignments: Programming assignments in NumPy/TensorFlow, quizzes, final projects

This comprehensive specialization, comprised of five courses, covers the full spectrum of deep learning, from neural networks to CNNs, RNNs, and practical techniques for training and deploying models. Taught by Andrew Ng, co-founder of Coursera and a leading figure in AI education, this specialization has reached over 350,000 learners worldwide.

5. Practical Deep Learning for Coders

• Instructors: Jeremy Howard, Rachel Thomas (fast.ai)
• Platform: fast.ai
• Duration: 7 weeks
• Prerequisites: 1 year coding experience
• Assignments: Jupyter notebooks, projects, Kaggle competitions

This hands-on course is designed to get you building state-of-the-art deep learning models fast, using the PyTorch framework. With an emphasis on practical skills and best practices, you‘ll learn how to preprocess data, train models on GPUs, and deploy them in the real world. Many alumni have gone on to win Kaggle competitions and land jobs at top tech companies.

Other notable free courses include:

• Deep Learning (with PyTorch) by Yann LeCun and Alfredo Canziani (NYU)
• Deep Unsupervised Learning by Pieter Abbeel (UC Berkeley)
• Deep Learning for Natural Language Processing by Phil Blunsom and the DeepMind NLP team (University of Oxford)
• Creative Applications of Deep Learning with TensorFlow by Parag Mital (Kadenze Academy)

By working through these courses, you‘ll gain a strong foundation in deep learning, backed by hands-on projects in domains like computer vision, NLP, and generative modeling. To further hone your skills, you can participate in Kaggle competitions, contribute to open-source projects, and even pursue research or startup ideas.

But this is just the beginning. As Yann LeCun wrote in 2018, "Deep Learning is not ‘over‘. As a scientific endeavor, Deep Learning is just beginning, exploring a brand new continent. We have built the equivalent of a few coastal outposts. There is a whole continent to explore beyond the coast, full of riches that we cannot even imagine today."

The future of deep learning is bright, and the opportunities are endless. By diving into these free courses and investing in your skills, you‘ll be well-positioned to ride the wave of AI innovation and shape the future of technology. As Geoffrey Hinton said in his NeurIPS 2016 keynote, "We‘re just at the beginning of a big revolution, and there‘s still a lot to do. It‘s a very exciting time to be working in AI."

So what are you waiting for? Choose a course, fire up your GPU, and start your deep learning journey today. The future is yours to create.