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About This Course

Deep Learning is the most exciting sub-field of machine learning. Many state of the art results in computer vision are obtained using a Deep Neural Network.

In this course, we will start with a theoretical understanding of simple neural nets and gradually move to Deep Neural Nets and Convolutional Neural Networks.

Not only will we go over Deep Net architectures used for solving various computer vision problems, we will also go over practical considerations needed to successfully train Deep Neural Networks. These include how to prepare datasets, how to perform sanity checks before embarking on training that can take hours, how to use visualization tools to debug the training process, what workflows to use when the results are not satisfactory, and finally how to deploy your network on the cloud.


Working knowledge of Python and Numpy is sufficient to start the course.

Course Staff

Satya Mallick Profile

Satya Mallick

Course Director and Chief Instructor
Owner, Big Vision LLC

Vikas Gupta Profile

Vikas Gupta

Lead Instructor
Senior AI Consultant, Big Vision LLC

Anastasia Murzova Profile

Anastasia Murzova

Deep Learning Engineer,

Anna Petrovicheva Profile

Anna Petrovicheva

Deep Learning Expert,

Grigory Serebryakov Profile

Grigory Serebryakov

Deep Learning Expert,

Pavel Semkin Profile

Pavel Semkin

Junior Deep Learning Engineer,

Prakash Chandra Profile

Prakash Chandra

AI Consultant, Big Vision LLC

Sergei Belousov Profile

Sergei Belousov

Deep Learning Expert, Intel Corporation

Tatiana Khanova Profile

Tatiana Khanova

Deep Learning Expert,

Valeriia Koriukina Profile

Valeriia Koriukina

Senior Deep Learning Engineer,