How to Learn Deep Learning: Understand How to Build Intuitive AI

The meteoric rise in our knowledge of artificial intelligence and deep learning over the past decade has been an awe-inspiring feat of scientific progress. We can now use artificial intelligence to complete daily functions like setting alarms, opening doors, and performing other basic tasks. However, what happens when we allow artificial intelligence to learn at a deeper level? 

This idea is called deep learning. It’s a subset of machine learning, which is itself a subset of AI, that provides machines a more nuanced way of learning processes.

Should you learn deep learning for your career in artificial intelligence? Let’s see what deep learning is all about, the methods behind deep learning, and where you can get the best deep learning training.

What Is Deep Learning?

Deep learning is a subset of machine learning. Deep learning aims to copy the learning processes of the human brain in machines. Through deep neural networks, machines will ideally be able to learn more complex procedures on their own. 

What Is Deep Learning Used For?

Deep learning is a fascinating advancement in artificial intelligence, and it also has quite a few cutting-edge, real-world uses.

• Facial recognition. The foundation of deep learning is machines sifting through images and pixels to recognize shapes, lines, and faces. Through successful analysis, machines can learn to recognize faces and other more complex imagery. 

Where regular machine learning would perhaps recognize pixels, machines engaging in deep learning will understand more complex imagery, like the details in a human face. This ties in with computer vision, which is the development of a computer’s ability to discern images.

• Speech recognition. Another great stride in machine learning is the role of deep learning in speech recognition. This allows computers and artificial intelligence to listen to speech and translate it into text. This is extremely helpful to those who are hard of hearing and serves as an example of the positive uses of deep learning.

• Natural language processing. This is a branch of artificial intelligence that attempts to bridge the gap in understanding between humans and machines regarding natural language. Ultimately, we want machines to recognize our languages and be able to respond to them.

Types of Deep Learning

As with many aspects of technology, deep learning has subsets. Let’s take a look at supervised, unsupervised, and semi-supervised learning. 

Supervised Deep Learning

Supervised learning is the most common form of deep learning. In supervised learning, humans input data they wish to analyze, and the machine acts under human supervision. 

For example, if you have ever marked an email as spam, this communicates to the machine that this email and others like it fall into the spam category. Through continuous labeling, the machine can learn to become more helpful, like by automatically filtering out your spam mail.

Unsupervised Deep Learning

Unsupervised deep learning refers to machines learning entirely on their own. Unsupervised machines can parse through big data that do not have labels previously set by humans. This is more useful than supervised learning, as big data can often be too expansive for humans to handle.

This type of deep learning is perfect for extrapolating data, as it involves no preconceived notions, biases, or other human errors. Supervised learning lets machines find patterns all on their own. 

Semi-supervised Deep Learning

As you can guess, semi-supervised deep learning is the best of both worlds. With this type of deep learning, the human feeds the machine only a small amount of labeled data, with a much larger unlabeled data portion. This mixture of supervised and unsupervised analysis allows the computer to recognize patterns with minimal human interaction, while also extrapolating their own patterns. 

The machine will ultimately pare the big data down to a more manageable data set.

Learning Deep Learning

So, where do you begin? Deep learning is an ever-evolving and constantly expanding field. If you’re interested in getting into the field, you should strike while the iron is hot. 

Let’s lay out a few of the basics before you start your education in deep learning. We’ll look at how long it takes to learn deep learning and some deep learning classes.

How Long Does It Take to Learn Deep Learning?

If you are familiar with artificial intelligence or computer science, it can take as little as a few weeks. But your success will depend entirely on your skills and determination to learn. For someone whose only experience with artificial intelligence is Siri, it could take several years.

Deep learning takes quite a bit of time to learn if you don’t have any prior data science or computer science knowledge. We’re talking about building neural networks, not merely programming with Python. If you learn machine learning by enrolling in a ML bootcamp, you can master the subject in three to six months.

How to Learn Deep Learning: Step-by-Step

Regardless of what your projected deep learning timeline looks like, there are some essential steps you should take.

1. Nail down the basics. Deep learning is a more complicated subset of machine learning, so it would be ideal to have a background in programming, artificial intelligence, or any other facet of computer science. Familiarity with big data and statistics is also a plus. 
2. Take part in successful machine learning projects. Working on a project will not only boost your knowledge in a specific field of computer science but will also help you further your education and add to your portfolio. 
3. Learn a programming language, preferably Python. Programming languages are fantastic ways to become familiar with deep learning, machine learning, and computer science. Learning Python will undoubtedly help you in your deep learning journey.
4. Take a deep learning class. You shouldn’t squander away your hours confused in front of a computer screen. There are hundreds, if not thousands of deep learning courses, classes, and workshops to choose from. Many are taught by computer science and deep learning experts. You should choose a class that fits with your knowledge level, whether beginner or advanced.
5. Get certified. Getting certified in deep learning is an impressive accomplishment. Deep learning certifications communicate to the world that you have put in the time and effort to become proficient in one aspect of deep learning. Certifications might cover deep neural networks, computer vision, or big data.

The Best Deep Learning Courses and Training

Instead of getting involved in a learning process that you find confusing, look at the deep learning courses and training resources below to find the best one for you. 

Best Online Deep Learning Courses

You don’t have to enroll in a university program and pay a steep tuition rate to master deep learning. Instead, for a more affordable price, you can join some of these online deep learning courses.

Deep Learning Specialization with Andrew Ng

• Provider: Coursera, deeplearning.ai
• Time: Four months
• Prerequisites: None

This great specialization from Andrew Ng, Stanford professor and CEO of Landing AI, gives curious learners the chance to jump right into the world of advanced artificial intelligence. 

This specialized course is divided into five fundamental deep learning classes covering the basics and advanced concepts in the field.

Financial aid is available, and you can also get a certificate upon completing the class. Students will take part in a hands-on project at the end of the course. You don’t need to take all five courses, but once you sign up, you have the option to take all five. 

Machine Learning A-Z™: Hands-On Python & R In Data Science

• Provider: Udemy
• Time: About 45 hours
• Prerequisites: none
• Price: $95

Udemy is a great resource for courses in nearly any subject, so it makes sense that they have some great classes in artificial intelligence. 

This class is a great general study of deep learning. In it, you will review the fundamentals and also learn some of the more complicated aspects of deep neural networks and computer vision.

This course is jam-packed with learning materials. It consists of 322 lectures, 44 hours of high-quality videos, 75 articles covering each part of deep learning, and 38 downloadable resources. All you need to succeed in this Udemy course is an understanding of high school level math and the determination to succeed. 

Best Free Deep Learning Courses

What if you want to get familiar with the foundations of deep learning without the financial burden? We have good news, as there are quite a few free deep learning courses offered by some impressive institutions.

Machine Learning with Python: from Linear Models to Deep Learning

• Provider: edX, MIT
• Time: 15 weeks
• Prerequisites: none
• Price: Free

The tech masters at the Massachusetts Institute of Technology have put together this free deep learning class, available to all. Students will learn about big data, artificial intelligence, clusters, linear models, kernel machines, and deep neural networks. This online deep learning class caters mostly to intermediate computer science students.

You also have the option to earn a certificate at the end of the course for $300.

Natural Language Processing in TensorFlow

• Provider: Coursera
• Time: Four weeks
• Prerequisites: None
• Price: Free

This is a great course in deep learning as it relates to software development. Specifically, the course will teach you how to use the popular open framework TensorFlow for all of your artificial intelligence endeavors. 

Taught by Laurence Moroney, this free class allows students to learn about natural language processing. 

While the class is free, you can opt to receive a certificate for $300 upon completion. 

Deep Learning Certifications

If you really want to prove that you’re ready to proceed toward a career in deep learning, then getting a certification is a critical step. Browse these certificates below from accredited institutions. 

Machine Learning Certification from Stanford University

Taught by AI master Andrew Ng, this artificial intelligence certification program covers deep learning, and all the vital concepts of artificial intelligence and machine learning. 

In more than 60 hours of high-quality videos, Andrew Ng will ensure that participants will be ready to receive their certificate. Over 3 million students have enrolled in this certification program. 

Professional Certificate in Deep Learning via IBM

This excellent certificate program in deep learning is offered by IBM, the artificial intelligence masters behind the supercomputer Deep Blue.

The program takes students through the basic concepts of deep learning, including supervised and unsupervised learning, deep neural networks, recurrent networks, and autoencoders.

Participants will also apply their newfound knowledge to real-world applications to get a better understanding of deep learning. This is where you will explore facial recognition and natural language processing. You will also learn how to use things like TensorFlow to assist you.

Online Deep Learning Resources

Have you finished a deep learning course or certificate program but want to learn more? Or are you not into taking online classes, and would rather find a few resources on the Internet that can fill you in about deep learning? Luckily, there is no shortage of deep learning resources online. 

Sentdex’s Youtube Channel

Sentdex’s YouTube channel has many great resources for deep learning and computer science. In his deep learning video series, he explains everything you need to know about TensorFlow, Python, and neural networks. 

He is excellent at making complex concepts digestible. He also relays step-by-step processes that are perfect for visual learners and those who want a great walkthrough of the deep learning process.

Datacamp’s Artificial Intelligence Courses

Datacamp is a highly useful resource for computer and data science in general. Datacamp offers courses taught by talented instructors in nearly any topic related to data science. There are tracks dedicated to big data, network analysis, and supervised machine learning.

Datacamp’s Introduction to Deep Learning is the place you’ll want to begin. It might also help you to take some of the basic machine learning classes, as you will need to know a lot about the topic to master the more advanced concepts of deep learning.

Should You Study Deep Learning?

So, should you learn deep learning? It depends on what interests you. If you want to take part in the highly lucrative and inventive world of artificial intelligence, and be on the frontier of human ingenuity, then learning deep learning is an advisable career move.

If you are willing to put in the time and effort it takes to receive the proper training and master critical concepts, then you should push ahead with your deep learning progress.