Learning Path for Deep Learning in 2019

Learning Path for Deep Learning in 2019

Here is a high-level overview of the core concepts you should and master in the deep learning sphere:

• Getting Started: Deep learning is a vast field made up of several components. So to kick off your learning journey, it is recommended to start from the ground up. The first month will be all about understanding what deep learning means, covering basic descriptive statistics and probability concepts, and learning Python!

• Machine Learning Basics: The logical next step in our learning path takes you into the world of machine learning. This includes techniques like linear regression, logistic regression, and regularization methods. Deep learning cannot be truly grasped until you know the core concepts of linear algebra and calculus, so complement your skill-set with an introduction to matrices, vectors and derivatives.

• Introduction to Deep Learning and Keras: This is what you’ve been waiting for! March is when we recommend introducing yourself to neural networks. Additionally, you should start exploring the different frameworks in deep learning and start coding on one (it is recommended to use Keras in this learning path). There are a lots of resources online to have hands-on project to help you gain a practical understanding of these concepts.

• Fine-Tuning your Neural Network: You’ve built your model and tested it out. What’s next? Models don’t usually give the best result in the first iteration, so knowing how to fine-tune and improve them is a critical skill any deep learning expert should know. Handling/preprocessing image data, understanding hyper-parameter tuning and transfer learning, etc. are all a part of improving your deep learning model.

• Understanding CNNs: Convolutional Neural Networks (CNNs) have become one of the most common use cases of deep learning in real-world scenarios. It is considered mandatory to know what CNNs are and how you can tune the internal hyper-parameters to extract the maximum results out of them.

• Debugging your Deep Learning Model: Ask any programmer, and they’ll tell you debugging is the least enjoyable part of their work. But how about the ability to visualize your deep learning model to understand where it’s going wrong? Yes, it’s now possible to analyze errors visually – a really cool and helpful skill we have highlighted in the learning path.

The Ultimate Learning Path

• Sequence Models: This is where we really take a deep dive into deep learning. Sequence models include techniques like Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTMs), and Gated Recurrent Unit (GRU). This is also the point where you should start differentiating yourself from the herd by applying these concepts on practical projects.

• Deep Learning for NLP: Deep learning has changed the scope of NLP to a remarkable degree. With the flexibility of transfer learning, NLP has become a whole different beast. If this is your field of interest, we encourage you to stay on top of the game by learning the various methods of how deep learning can be used on text data. At the very least, an understanding of word embedding will help immensely.

• Unsupervised Deep Learning: Data scientists use a variety of algorithms to extract actionable insights. But the majority of these problems are of a supervised learning nature. Unsupervised learning is a challenging field, doubly so when it comes to deep learning. But it’s advantages are numerous and potentially ground-breaking. Hop on to this part of your learning path once you have a solid grasp on the above mentioned concepts.

• GANs: One of the favorite deep learning concepts of any typical Deep Learning enthusiast– Generative Adversarial Networks (GANs). They are behind all the creative AI developments we see regularly, including creating essays, writing poems, generating artwork, etc.

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