About the course

The most common question we get from beginners in the field of Deep Learning is - Where to begin? The journey to becoming a Deep Learning expert can be difficult if one does not have the right resources to follow. There are a million resources to refer and it is tough to decide where to start from.

We are here to help you take your first steps into the world of Deep Learning. Here is a free learning path for people who want to become a Deep Learning expert in 2019. We have arranged the best resources in a logical manner along with exercises to make sure that you only need to follow one single source to become a data scientist.

Why take this course?

The course is ideal for beginners in the field of Deep Learning. Several features which make it exciting are:

Beginner friendly course
The course assumes no prerequisites and is meant for beginners

Curated list of resources to follow
All the necessary topics are covered in the course, in an orderly manner with links to relevant resources and hackathons.

Pre-requisites

This is a beginner friendly course and has no prerequisites.

Course curriculum

  • 1
    January 2019
    • Getting Started
    • Overview of the Learning Path
    • Month on Month plan
    • Introduction to Deep Learning
    • Applications of Deep Learning
    • Setting up your System
    • Descriptive Statistics and Probability
    • Python
    • Exercise : Python
  • 2
    February 2019
    • Start engaging in data science / deep learning communities
    • Inferential Statistics
    • Exercise : Statistics
    • Partial Derivative
    • Linear Algebra - Part 1
    • Linear Regression
    • Logistic Regression
    • Exercise : Linear and Logistic Regression
    • Regularization Techniques (Ridge and Lasso)
    • Project
  • 3
    March 2019
    • Start building your GitHub profile
    • Start building your GitHub profile
    • Linear Algebra - Part 2
    • Getting Started with Neural Networks
    • Understanding Forward Propagation
    • Understanding Back Propagation
    • Exercise : Understanding Neural Networks
    • Build your first Neural Network in Numpy
    • Frameworks for Deep Learning
    • Introduction to Keras
    • Build your first Neural Network in Keras
    • Project
  • 4
    April 2019
    • Start Participating in Competitions
    • Handling / Pre-processing Images
    • Exercise
    • Hyperparameter Tuning
    • Regularization Techniques
    • Optimization Algorithms
    • Exercise
    • Transfer Learning
    • Data Augmentation
    • Project
  • 5
    May 2019
    • Understanding Convolutional Neural Networks (CNNs)
    • Exercise : CNNs
    • Hyperparameter Tuning
    • Transfer Learning
    • Project
  • 6
    June 2019
    • Build your resume and apply for Internships
    • Visualizing Convolutional Neural Networks
    • Visualizing Convolutional Neural Networks
    • Visualizing Convolutional Neural Networks
    • Project 3 on CV
    • Project 4 on CV
  • 7
    July 2019
    • Start writing articles
    • Handling / Pre-processing Text Data
    • Exercise
    • Recurrent Neural Networks (RNNs)
    • RNNs - Video
    • LSTM
    • GRUs
    • Project 1 on NLP
  • 8
    August 2019
    • Word Embeddings
    • Exercise
    • Project 2 on NLP
  • 9
    September 2019
    • Attention Models
    • Attention Models - Text
    • Project on Attention Models
  • 10
    October 2019
    • Unsupervised Deep Learning
    • Project on Unsupervised Deep Learning
  • 11
    November 2019
    • GANs : Video
    • GANs
    • Project o