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While deep learning is considered to be a small branch of the tree of artificial intelligence, it’s already a branch that seems to be outgrowing the tree itself. Deep learning is the development of ‘thinking’ computer systems, called neural networks, and utilizing it requires coding strategies foreign to old-school programmers. With the help of deep learning, we can teach our computers to learn for themselves in a way that gives us actionable results. And, you have the chance to be at the forefront of it all, as specialists in deep learning are needed now more than ever before.
Most programmers know how to command computers to perform specific commands in specific orders, but few know how to create computer programs which can think for themselves.
We’ve compiled this list of the best deep learning courses to help you get ahead of the curve. If you’re looking to start a career in deep learning, then these training programs will serve as an excellent starting point for a prosperous career. Or, if you’re already familiar with the fundamentals of deep learning, then one of the more advanced courses on this list might be a perfect suit for you.
We’ve selected these courses based on their accessibility, variety, and lesson structure, among other factors. Whether you’re a budding coder looking to break into AI or someone just looking to gain a cursory knowledge of knowledge engineering, these are all good choices for you if you’re wondering how to learn deep learning algorithms.
Without further ado, let’s break the best of them down, one by one.
What is the best deep learning course?
Here are our choices for the best deep learning course:
- AI & Machine Learning Career Track
- Complete Guide to TensorFlow for Deep Learning with Python
- Deep Learning Specialization
- Deep Learning Nanodegree
- Deep Learning A-Z™: Hands-On Artificial Neural Networks
- An Introduction to Practical Deep Learning
- Deep Learning, by 3Blue1Brown
- Deep Learning: Recurrent Neural Networks in Python
- Advanced AI: Deep Reinforcement Learning in Python
- Deep Learning with Keras
- Introduction to Deep Learning
AI & Machine Learning Career Track
Who can take this course: Those students who can demonstrate expertise in software and pass a programming challenge will be eligible for admission.
What you’ll learn: We covered this course in greater detail in our article on machine leaning courses, where we ranked it as the very best course available, despite its tough admission criteria.
During our previous review, we focused on it mostly in the context of ML, though, and we barely mentioned the value it holds as a deep learning course. In reality, though, the course material is just as much about deep learning as it is about machine learning.
In units four, five, and six, the following deep learning topics are covered, among others:
- Foundations of deep learning & building real-world applications
- Natural language processing case studies
- Computer vision & deep learning for images
Verdict: We said it before and we’ll say it again: Springboard’s courses on artificial intelligence, machine learning, and deep learning are some of the very best in the world. The admission process will be tough, and the graduating process will be even tougher, but those students who do manage to finish the curriculum will be rewarded accordingly. Springboard guarantees a job proposal for all graduates, which is very valuable by itself. Even more valuable, than the job offer, though, will be the actual knowledge you gain from this course.
If you fulfill the admission criteria for this course, it will likely be the best deep learning course you could ever partake in. The advantages of this online course are incalculable.
Best Free Course:
Deep Learning Specialization
Who can take this course: This deep learning certification program from Coursera is ideal for students who know basic Python programming and algebra. Prior knowledge in deep learning is considered beneficial, but not compulsory.
What you’ll learn: This deep learning course covers various topics in the field of A.I and deep learning, such as:
- Neural Networks (Convolutional)
- Hyperparameter tuning, Regularization, and Optimization
- Structuring Deep Learning Projects
- Sequence Modelling (in the context of natural language processing)
The names of these topics might seem confusing at first, but the course instructor has done an excellent job at making the syllabus easy to understand and follow. The material starts off with the basic knowledge, before moving onto the more technical know-how of deep learning.
When you complete this course, you will have a solid foundation of skills which you can use to start building your own convolutional neural networks. The inclusion of natural language processing lectures in the course syllabus is also a very welcome addition to the curriculum. Many courses on this list failed to cover NLP in detail, even though it could be considered one of the key topics in deep learning.
Verdict: This is by far the best deep learning course which you can access for free. No other free deep learning courses even came close to the level of depth that this course has. “Deep Learning Specialization” on Coursera is on par with courses costing hundred of dollars, so the price-to-quality ratio for this one is off the charts. As is the case with most of the deep learning courses on this list, it does require some prior knowledge in programming, though, which could be a setback for some.
This online course was voted the best deep learning course by FloydHub – a hub for all things A.I.
Best for Beginners:
Complete Guide to TensorFlow for Deep Learning with Python
Who can take this course: Anyone who wants to dive into Google’s TensorFlow system stands to benefit the most from this course. The course content is introductory in nature, so prior knowledge in programming is not compulsory (although it will be beneficial).
What you’ll learn: This online training program will give you basic knowledge of Python, deep learning, A.I, and mathematics, making it a comprehensive introduction to the basics of deep learning and neural networks. Using the TensorFlow framework as the basis for the course, Jose Portilla teaches students deep learning in a specific context that shies away from abstraction. He gives students an excellent overview of the basics of deep learning and provides a springboard so that the students can start to build neural networks of their own.
The course explains the essentials of deep learning in a comprehensive way, before moving onto the more technical skills and exercises which will enable you to start building your very own neural networks. It’s not the most advanced deep learning course out there, but it does an excellent job at covering the fundamentals.
Verdict: If you’ve ever thought of fully immersing yourself in a TensorFlow course as a way to gain experience in deep learning, then this is the course for you. This course is one of the best deep learning online courses out there. Especially for those who want to learn how to use Google’s Deep Learning Framework without having advanced knowledge in Python. For these reasons, we consider it the best deep learning course for beginners.
Deep Learning Nanodegree
Who can take this course: This deep learning certification is for students who already have intermediate experience with Python programming. The students are also expected to have a basic understanding of calculus and linear algebra. Prior knowledge in deep learning is not required.
What you’ll learn: Anyone looking to integrate a combined and comprehensive deep learning certification into their skillset will stand to benefit from this course. This online course covers many topics related to artificial intelligence but it goes the deepest into deep learning with neural networks. At first, students get a general overview of neural networks, and then the course gets more specific by diving deeper into convolutional neural networks and recurrent neural networks separately.
Using five specially designed projects, this course teaches its students how to set up neural networks capable of different tasks such as image recognition and classification. Additionally, you will learn the basics of setting up the core systems of AI-assisted tasks and execute projects that use PyTorch and Amazon Sagemaker as tools.
And, finally, when you pass this course, you will be automatically admitted into Udacity’s more advanced courses on the topic of A.I – the Self-Driving Car Engineer and Flying Car and Autonomous Flight Engineer programs. It should be mentioned, though, that you will need to pay for those programs separately, despite being automatically admitted after graduation.
Verdict: This deep learning course from Udacity gives students an excellent foundation of knowledge, by using Python as the framework for deep ‘earning algorithms. The course syllabus is easy to follow considering the technical subject areas and the instructors teach complex ideas in simple ways. The course starts off with the basics, before diving deeper into the more advanced lectures, giving students a chance to catch up easily. Finally, the course has an all-star team of Course instructors, filled with deep learning experts from Google and various prestigious STEM universities.
All in all, “Deep Learning Nanodegree” by Udacity is one of the best deep learning courses currently available – as long as you know a little Python beforehand.
Deep Learning A-Z™: Hands-On Artificial Neural Networks
Who can take this course: Students interested in getting into the thick of coding their own deep learning algorithms should take this course. Alternatively, those looking for a program that teaches deep learning training with PyTorch and TenserFlow will find lots to learn from this course. The material is relatively basic in nature, so this course could be considered beginner-friendly.
What you’ll learn: The course starts off with teaching students the basics of what builds a neural network and the role of deep learning in developing software solutions. After that, the course continues by offering a good balance of TensorFlow and PyTorch exercises. It has students recreate real-world examples of deep learning software such as recommender systems and image recognition programs. The detailed step-by-step exercises ensure that the technical parts are easy to follow, and the theory classes are easy to understand.
Verdict: For people who have light experience in coding, this course is a solid pick. Not only does it provide a good overview of the two most-used open source libraries used in deep learning, but it also gives an excellent overview of the common applications of deep learning in everyday applications.
An Introduction to Practical Deep Learning
Who can take this course: This deep learning training course is perfect for students who want a basic overview of the capabilities of artificial neural networks. It’s short, and it’s beginner-friendly, so all students with a basic overview of mathematics will be able to study the course material.
What you’ll learn: The course syllabus consists of 5 learning modules:
- Introduction to Deep Learning and Deep Learning Basics
- Convolutional Neural Networks, Fine-Tuning, and Detection
- Recurrent Neural Networks
- Training Tips and Multinode Distributed Training
- Hot Research and Intel’s Roadmap
The course starts off with the very basics of deep learning and moves on from there to the more advanced topics surrounding convolutional and recurrent neural networks. Perhaps the most valuable section of this course is the fifth, where the Intel engineers who created this course provide their very own roadmap. It’s very interesting to read, as it provides an insight into the inner workings of one of the most successful technology companies in the world.
Verdict: The folks over at dev.to gave this course the title of the top deep learning course of 2019, and while we did not rank it as highly as them, we still agree that it’s one of the best choices out there. It’s short in terms of material, but the bite-sized nature of the course makes it ideal for those students who want to learn the fundamentals of deep learning quickly. The fact that you can participate in this course for free makes it even better.
Deep Learning, by 3Blue1Brown
Who can take this course: This deep learning course is unlike all others on this list. It’s very easy to follow, it does not require any prerequisite knowledge, and it’s suitable for absolutely anyone interested in deep learning and neural networks.
What you’ll learn: This video course, created by YouTuber 3Blue1Brown, will teach you not only the basics of neural networks, but it also how the human brain works, and how it handles problem-solving. The videos are full of illustrative pictures, graphs, and animations, which make the course material very easy to follow and understandable.
There are 4 video chapters in total, each of which answers a different question:
- What is a neural network?
- Gradient descent, how do neural networks learn?
- What is backpropagation really doing?
- Backpropagation calculus
All of the videos are illustrated beautifully, and they prove that difficult subjects CAN be taught with simple methods. In terms of accessibility, this is the most beginner-friendly deep learning course we have seen.
Verdict: This series of videos by 3Blue1Brown was created in 2017, which is a relatively long time for a technical topic. However, to this date, they are still one of the most informative deep learning videos out there. If you haven’t yet checked out 3Blue1Brown’s channel on YouTube, then we highly recommend you do so.
Deep Learning: Recurrent Neural Networks in Python
Who can take this course: Data engineers looking to gain some experience with deep learning are the ideal candidates for this course. The course requires you to have prior knowledge of the basics of deep learning algorithms alongside experience with Hidden Markov models.
What you’ll learn: Visualization of the structure that makes up deep learning programs is one of the most challenging parts of designing a program. This course allows you to dive into the technical aspects of adding time concepts to your neural networks, by integrating more advanced algorithms to generate even better content.
Verdict: If you’re looking for a more complex way to make your deep learning program generate content such as written output, this course is ideal for you. You’ll be able to refine how your neural networks collect and identify data, build a framework using a recurrent neural network, and generate content that is far superior to usual neural network models. For advanced students, this is a very good deep learning course.
Advanced AI: Deep Reinforcement Learning in Python
Who can take this course: Ideal students for this course are technical-minded data professionals looking for the latest developments in AI techniques via deep learning. This course allows you to flex a little more creativity in methods to create neural networks and looks at different solutions to solving the problem of interaction between program and data.
What you’ll learn: Reinforcement learning is having your program actively interact with a data set. This course teaches you how to set up a deep learning algorithm that doesn’t just integrate existing data but actively seeks out the best possible solution or configuration according to what it learns. In other words, it’s about building deep learning programs that are actively striving to attain an ideal solution, rather than just formulating their own out of the data that’s been given.
Verdict: Learning about the different methods of teaching deep learning systems can be useful to data engineers who want to build sophisticated deep learning programs. This course is an excellent guide into the different possibilities that can be used to build a goal-oriented deep learning program.
Deep Learning with Keras
Who can take this course: This deep learning course is basic in nature, but it’s still best suited for students who have some prior skills in programming (mainly Python).
What you’ll learn: The primary aim of this training program is to teach students how to use the Keras Deep Learning Library. Keras is one of the most useful resources for creating deep learning programs with Python, and this makes Jerry Kurata’s course very valuable for anyone looking to use deep learning with the Python programming language.
The course begins with an introductory session that explains the basics of Keras and neural networks, before moving onto more complex subjects. Students who take this course will learn how to construct models in Keras, how to work with layers in Keras, and ultimately – how to build both convolutional and recurrent neural networks through Keras.
The course material is very practical and hands-on, making it very valuable for anyone who wants to start building projects straight from the get-go.
Verdict: A 2.5-hour course is not enough to cover all the important details of deep learning. However, we found that despite the short course material, the instructor managed to cover an impressive amount of topics, with plenty of real-life examples and useful tips regarding working with Keras.
It’s not the most in-depth deep learning course in terms of content length, but it’s one of the most practical and straight-to-the-point. We highly recommend it to anyone who is interested in creating neural networks through Keras and Python.
Introduction to Deep Learning
Who can take this course: Those already familiar with the basics of machine learning and are studying about its subsets are the best fit for this course.
What you’ll learn: This course teaches students about the basics of neural networks, the kinds of data that you can expect to use them on, and the applications you can create that use these processes. Learn about how your algorithms can generate content from context and generate actionable data from raw input. It also gives a succinct explanation of the role of deep learning in different directions of AI, and shows basic examples of each.
Verdict: This is a deep learning program that’s best for those who already have some idea of what deep learning is. It can help experienced coders by providing a refresher on what makes deep learning so important when it comes to AI.
What’s the story behind deep learning?
The first programmable computer was created by Konrad Zuse between 1936 and 1938 in his parents’ living room. Computers have come a long way since then, but despite the impressive growth in computer processing powers, they still tend to struggle with human-like learning. Computers have always been programmed to perform specific commands in specific orders. The times, though – they are changing. More and more, computers are starting to act like humans – they can analyze, gather data, and learn by themselves. All because of advancements in the field of deep learning.
Deep learning has a relatively simple goal – programming computers to solve problems similarly to human brains with the help of neural networks. However, despite the simple idea, it has been one of the hardest things us humans have ever tried to code.
The crux of what makes deep learning so difficult—and the reason why it’s such an important factor in creating highly advanced technology—is that concepts like learning and adaptation aren’t native to a program’s mind. This is one of the reasons why some degree of human oversight is still required to operate our most sophisticated systems today. A computer, by itself, isn’t built for that sort of thing. However, with the help of powerful machines and even more complex algorithms, this goal becomes a little bit closer for us to reach. And, there’s solid evidence that deep learning can be the final piece of the puzzle that pushes us towards intelligent computers, revolutionizing the way people interact with tech forever.
How do I choose a good deep learning course?
The biggest thing that will inform your choice between these programs should be the tools that you’ll end up using. Deep learning primarily uses either PyTorch of TenserFlow as the open source libraries for developing algorithms, and while both do require a background on programming languages such as Python in order to be used reliably, the applications of each are quite different.
Building into that is the end goal of your deep learning studies: will you transition into fully autonomous applications such as self-driving cars and vehicles? Or will you remain in the purely digital sphere of interpreting and generating data? The kind of training you’ll receive will be crucial to establishing your forward career as a data scientist or give you new opportunities to explore in your field.
It’s also important to note that these courses need a lot of time and effort to fully digest. Even the shortest of these programs recommend that you go through their contents twice, and once you start building your own algorithms after the program, you will still likely need some initial referencing to get it done. Make sure that you have the time and the resources to spare before taking any of these courses to ensure that you benefit as much as possible from them.
Deep learning and the push for self-sufficient technology
As one of the building blocks of machine learning and a precursor to more sophisticated artificial intelligence systems, deep learning holds incredible potential. Things like generating words, recognizing images, and sorting sounds (which are some of the earliest skills that humans learn) will finally be accessible to our machines, giving them more autonomy in their performance.
While there are still considerable barriers for deep learning as an accessible system in everyday use (such as the vast amount of raw data required and the processing power needed to train a program). The potential applications of deep learning can help us harness our technology in ways that we could only dream of. It’s not unreasonable to say that deep learning is the first true step toward fully realized artificially intelligent programs.
So if you’ve ever wanted to take the step towards creating extremely intelligent and advanced software, take a look at the deep learning courses we’ve listed above.
It’s important to note that all of the courses above require some knowledge in programming languages, alongside basic and advanced mathematics. Deep learning lectures aren’t something you can jump into without the prerequisite experience—and while it’s admittedly as broad as the reach of artificial intelligence courses, it’s still a very technical field for you to take.