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If there is any one area in Data Science that has led to the progress of artificial intelligence (AI) and machine learning (ML) in the last few years it is deep learning. It has been around for a while but what makes it so buzzing topic in the last few years?

Well, deep learning turned out to be the headlines in 2016 when Google’s AlphaGo program crushed Lee Sedol, one of the top ranking Go players across the globe. Before this, there was not any good way to train deep learning neural networks, but with the advancements in machine learning algorithms and deep learning chipsets, deep learning can be implemented even more actively and easily now.

Deep learning is ubiquitous, be it a computer vision application and breakthroughs in the field of Natural Language Processing – we are living in a deep learning-fueled world. All thanks to the rapid advances in this technology, more and more people are able to leverage the power of deep learning. At the same time, it is considered a complex field and can be daunting for newcomers.

What is Deep Learning and How to Learn About It?

This learning path is designed for anyone who wants to learn deep learning regardless of your level. This structured path will be useful for folks who are looking forward to diving deep into deep learning. No matter whether you are a fresher or transitioning fields, or else looking to upskill yourself – this learning path guides you in the right direction.

Here is the high overview of the basic concepts you should be starting off with:

• Summary: Deep learning is a vast field consisting of several components. Hence, to kick off your learning journey, we recommend you to begin from ground zero which states to initiate learning by covering basic descriptive statistics, probability concepts, and learning Python.

• Machine Learning Basics: Next comes the role of ML which includes linear regression, logistic regression, and some regularization methods. Remember that deep learning cannot be grasped until you know the basic of linear algebra and calculus.

• Intro to Keras and Neural Network: Start exploring various frameworks in deep learning and start to code in one to gain a practical understanding of these concepts. After you have built the model and tested it out, learn how to fine-tune by handling or preprocessing the image data and understanding hyperparameter tuning.

• Understanding CNN: Convolutional Neural Networks are becoming one of the most common use cases of deep learning in real-world scenarios. It is mandatory to know what CNN is and how they tune the internal hyperparameters to extract the maximum output out of them.

• Sequence Model: This includes Recurrent Neural Networks, Long Short Term Memory, and Gated Recurrent Unit which really makes you dive into deep learning. This is the point where you should start differentiating yourself from the herd by applying these concepts on practical projects.

• Natural Language Processing: Deep learning has changed the scope of NLP in remarkable ways due to the flexibility of transfer learning; NLP is becoming a wholly new beast. If this is the field which interests you the most, we surely encourage you to stay ahead of the game by learning various methods of how deep learning can be used on text data. Also, understanding the word embeddings will immensely help at the least.
• Unsupervised Deep Learning and GAN: Data scientists have been using plenty of algorithms to extract the actionable insights but the majority of these issues are of a supervised learning nature. Unsupervised learning can be a challenging field when it comes to deep learning as it has numerous advantages and potentially groundbreaking. Generative Adversarial Networks is one of the favorite deep learning concepts behind all the creative AI developments including essay writings, network generation and much more.

Summing Up!

So, that was our take on the deep learning path which we hope puts you in the fast lane to earn those skills and some extra pennies. Keep Learning!