Computer Vision using Python & Deep Learning

Ever been fascinated by how autonomous vehicles or robots identify humans? How face recognition systems work? This workshop will help you learn the power of learning on images!

Open to practitioners from any background who have an interest to learn computer vision and deep learning.

Basic knowledge of Python Programming and machine learning is required to attend this workshop

What you'll learn

The workshop will provide an overview of Image Processing & Computer Vision using Python. It will cover the basics of how to deal with images, different operations on images and also how TensorFlow and deep learning can be leveraged for computer vision applications.

About this course

With the basic knowledge of python and a love for images, learn how coding, machine and deep learning put together can help you build technologies like face detection, object recognition and many others.

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Course Duration

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Course Plan

Basics of Image Processing

Understanding Image Features

Introduction to functions of OpenCV using Python and its application to computer vision

Object Segmentation and Detection

Labs using OpenCV

To introduce the key concepts of behind deep learning and introduction to the basic mathematics involved

Basics of Deep Learning Techniques

Basics of Neural Networks

Back Propagation

Activation Functions, Loss Functions, Optimization Functions

Parts of a model, Hidden layers

Convolutional Neural Networks

Basics of different layers, pooling layers, dropouts, and combining all the layers

This module covers the basics of tensorflow and how it can be used to build neural network models from scratch for various use cases. It also includes the use of labs.

This module introduces how deep learning algorithms can be used to model various Computer Vision cases such as Object Recognition and Segmentation.

Basic introduction to Computer vision

Introduction to Deep Neural Networks

Building and Classifying Images with DNNs

Incorporating DNNs for object detection

Optimizing performance of Deep Neural Networks


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