Learning Path For Deep Learning

In this article let us see different paths we can take upon in deep learning.

Deep learning is ubiquitous – whether it is Computer Vision applications or Natural Language Processing or powerful recommender systems. We are living in a powered by deep learning world.

Machine Learning Basics:

The logical first step towards understanding deep learning is machine learning. Machine learning provides applications the ability to learn automatically and improve from previous experience without being explicitly programmed.  It includes techniques like linear regression, logistic regression, and regularization methods.

Deep learning cannot be understood until you know the concepts of linear algebra and calculus, so complement your skillset with introduction to matrices, vectors and derivatives.

Introduction to Deep Learning:

Next you can start exploring different frameworks in deep learning. These frameworks like tensorflow or keras help our development faster. PyTorch is another framework that has become very popular among researchers.

Fine-Tuning your Neural Network: 

We need to fine tune our neural network model to improve them, since without optimal hyper parameter the model training may not be successful. Hence fine tuning the model is a major task during model training process. Handling/preprocessing dataset, understanding hyperparameter and transfer learning are all important.

Understanding CNNs:

Convolutional Neural Networks (CNNs) is a very common use cases of deep learning in real-world scenarios. It is very important to know CNNs for all the computer vision tasks and how you can tune the internal hyperparameters to extract the maximum results out of them.

Sequence Models:

Sequence models include techniques like Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTMs), and Gated Recurrent Unit (GRU).

Sequence models are used for usecases like speech recognition, text analysis, named entity recognition, speech generation etc. Most of the nlp taks use sequence models.

Deep learning and sequence model have taken scope of NLP to another level.


Generative Adversarial Networks (GANs) have been the biggest breakthrough in deep learning in recent time. They are behind many creative AI developments we see, that includes creating essays, writing poems, generating artwork, etc.

Thanks for reading, you can also check out our post on:
10 Free courses for machine learning and data science.

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