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A Comprehensive Learning Path for Deep Learning in 2020

Introduction

What a time to be working in the deep learning space! 2019 was chock full of deep learning-powered developments and breakthroughs – it was a major leap forward as state-of-the-art frameworks were released at an unprecedented pace.

Deep learning is ubiquitous right now. From the top research labs in the world to startups looking to design solutions, deep learning is at the heart of the current technological revolution. We are living in a deep learning wonderland!

Whether it’s Computer Vision applications or breakthroughs in the field of Natural Language Processing (NLP), organizations are looking for a piece of the deep learning pie.

Now here’s the issue – there are way too many resources out there to learn anything these days. The last thing you want is to study in a scattered and unstructured manner.

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That’s one of the primary reasons we put together a structured and comprehensive learning path for beginners in this field. It can appear like a complex and often daunting field for newcomers/freshers. But don’t worry – follow our learning path for deep learning and you’ll be a master by the end of 2020!

So without any further ado – here is your comprehensive learning path to become a deep learning expert in 2020!

You will need to register on the Courses portal to enroll yourself. This will enable you to track your progress as you progress through your deep learning journey. So you can keep saving your progress and pick up from where you left off – a very handy feature!

This year, based on the wonderful reception we received last year, we have expanded the scope of these learning paths. We are releasing 4 different learning paths, each focused on where you stand in your learning journey:

 

Who is this Learning Path for?

“Should I try deep learning? I’m a beginner in this field and deep learning seems quite daunting.”

This is one of the most common questions we receive regularly. We have good news for you!

This learning path is designed for anyone who wants to learn Deep Learning, regardless of your level.

Yes! We have put together this structured month-by-month learning path for everyone. So you can start from scratch and become uber familiar with Deep Learning by the end of 2020.

Or if you want to build on your existing deep learning skills or enhance them with advanced concepts – this learning path will guide you through that journey too.

 

Summary – Learning Path for Deep Learning

Here’s a broad summary of the various deep learning concepts we cover in this learning path:

  • Getting Started: There are a few key deep learning components that you need to cover before launching into the field. These are essential prerequisites to get you started from scratch. You would be introduced to the world of deep learning in the first month, in addition to starting with Python, statistics, and probability
  • Introduction to Machine Learning: The second month of the learning path is all about building on the above step. We will cover inferential statistics, linear algebra, machine learning algorithms like linear and logistic regression, among other things. We’ll top this off with your first project of the year – a classification based problem
  • The Deep Learning Journey Begins: And off we go! Your first taste of deep learning begins here. We’ll first finish off linear algebra basics before understanding the awesome concept of neural networks. This is a HUGE learning month. You would also pick up Keras, the deep learning tool of choice for many top experts. And of course, we will work on a deep learning project in Keras here
  • Deep Dive into Neural Networks: This section is all about building on your recently acquired neural network expertise. Learn the different regularization techniques, how to perform hyperparameter tuning to improve your model’s performance, the art of transfer learning, and much more. This will be followed by a computer vision project!
  • Convolutional Neural Networks (CNNs): Ah, CNNs. The building blocks of recent deep learning breakthroughs. They power the majority of the state-of-the-art computer vision applications we come across and are a necessary addition to your deep learning skillset
  • Debugging your Deep Learning Models: Debugging is among the least enjoyable aspects of a data scientist’s role (programmers will know this pain!). How about visualizing your deep learning model to understand it’s performance and pinpoint the issues? I personally love visualization so this is quite a welcome addition to my portfolio
  • Sequence Models: These models include techniques like Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTMs), and Gated Recurrent Unit (GRU). Consider this the “moving” month in your deep learning journey
  • Deep Learning for NLP: Natural Language Processing (NLP) is easily the biggest beneficiary of the deep learning revolution. All the recent state-of-the-art frameworks we’ve covered, including Google’s BERT, OpenAI’s GPT-2, etc. – all of them have deep learning algorithms at their core. So in this section of the learning path, you will learn about various NLP concepts, such as word embeddings and attention models
  • Unsupervised Deep Learning: This section continues our deep dive into deep learning. Get started with the concept of autoencoders and apply all that you have learned on an unsupervised deep learning project. You should also start picking up another deep learning framework, such as TensorFlow or PyTorch
  • Generative Adversarial Networks (GANs): A wonderfully creative branch of computer vision and deep learning. GANs have blossomed in recent years and 2020 figures to be no different. It’s not only a useful framework to learn – but a highly entertaining one to work on

 

So are you ready to begin your deep learning journey? Enroll here TODAY and take your first step towards your dream role!

As always, we have converted this learning path into an illustrated infographic just for you. This is a month-on-month graphic that doubles as a checklist for your deep learning journey. Tick off the concepts as you progress and achieve deep learning nirvana by the end of 2020!

You can download a high-resolution version of this infographic here for printing purposes.

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