This course would be available from 1st June 2020

Become a Computer Vision Expert

A Comprehensive Computer Vision using Deep Learning Course in PyTorch

Do you want to learn cutting-edge computer vision and deep learning frameworks, architectures and techniques?

You’ve come to the right place! From building image classification models using computer vision to detecting the pose of a person, you are one step closer to mastering computer vision using deep learning.

There is an undeniable demand for people who have knowledge in the computer vision and deep learning domain, so that they can bring about disruptive solutions in any industry possible. And this course, along with the real-world project you’ll work on, is a must-have on your resume. 

Computer Vision systems deal with high variety and volume of data, specifically images or videos. It is represented as bits and blobs which are hard to explain to a machine. As a result, these systems need intricate techniques to make sense of the data and then make data driven decisions.

Check out these popular computer vision using deep learning use cases you’ll be working with:

  • Image Classification
  • Object detection
  • Face detection
  • Image Segmentation
  • Image generation, and many others!

Why PyTorch for Computer Vision and Deep Learning?

Every once in a while, there comes a library or framework that reshapes and reimagines how we look at the field of deep learning.

We can safely say that PyTorch is on that list of deep learning frameworks. It has helped accelerate the research that goes into deep learning models by making them computationally faster and less expensive (a data scientist’s dream!).

In fact, PyTorch was voted as the most popular deep learning framework for researchers in 2019. And it’s only getting better in 2020!

You will love working with PyTorch for computer vision tasks like image classification, object detection, pose detection and much more. You will quickly find yourself leaning on PyTorch’s flexibility and efficiency for computer vision.

What do I need to start with Computer Vision using Deep Learning (PyTorch) course?

  • A working laptop / desktop with 8 GB RAM
  • A working Internet connection
  • Basic knowledge of Machine Learning
  • Basic knowledge of Python - check out this Course
  • first, if you are new to Python

This is all it takes for you to learn Computer Vision using Deep Learning using PyTorch.
What are you waiting for?
What do I need to start with Computer Vision using Deep Learning (PyTorch) course?

Course Curriculum

Computer Vision using Deep Learning (PyTorch)

Introduction to Computer Vision

You will get familiar with the world of Computer Vision. We will also understand how this field has emerged in the past few years.


Horizons of Computer Vision

You will get an overview of different types of problems that can be solved using computer vision like Image Classification, localization and object detection, Face detection, segmentation, image generation including many others.


Understanding different Pre-trained models

We will explore different pre-trained models that are commonly used to solve image classification problems. We will implement all these models and compare the results on Emergency vs Non-Emergency vehicle classification problem


Image Localization and Object Detection

You will understand how to detect single objects as well as multiple objects in an image. We will also cover different architectures like RCNN, Fast RCNN, and State-of-Art Deep Learning models like YOLO, SSD, Retinanet along with other pre-trained models for detection.


Pose Detection

In this project, we will build a model that can localize human joints (elbows, wrists, etc) in images.


Face Detection

In this project, you will discover how to utilize State-of-Art Deep Learning techniques for Detecting human faces in images.


Image Segmentation

You will learn how to segment objects in an image. You will understand different approaches that can be used to solve segmentation tasks along with State-of-Art Deep Learning models like Mask-RCNN. In this module, we will also solve a lane segmentation problem. One of the prime applications of lane segmentation is in self driving cars.


Image Generation

Generating images is one of the most interesting applications of computer vision and has recently gained interest among researchers. Here we will cover different types of generative models like Pixel RNN, Pixel CNN, Variational Autoencoders (VAE) and then will see how to use GANs for generating realistic images.

Project - Classify Emergency Vehicles from Non-Emergency Vehicles (In-class)

Fatalities due to traffic delays of emergency vehicles such as ambulance & fire brigade is a huge problem. In daily life, we often see that an emergency vehicles face difficulty in passing through traffic. So differentiating a vehicle into an emergency and non emergency category can be an important component in traffic monitoring as well as self drive car systems as reaching on time to their destination is critical for these services. In this project, you will get to design a computer vision system that can do just this.

Project - Identify the Location of Red Blood Cells (In-class)

The analysis of blood cells allows the evaluation and diagnosis of a vast number of diseases. But this is generally done manually by skilled operators. In practice, we can automate a part of this process by identifying individual blood cell from a microscopic image. The task of this project will challenge you to find the locations of red blood cells through Deep Learning

FAQ

  • Who should take this course?

    This course is for people who are looking to get into the field of Computer Vision and start building their own Computer Vision applications using Deep Learning.

  • I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me?

    The course does not assume any prior background in Machine Learning. So you are welcome to follow through the course.

  • What is the refund policy?

    The fee for this course is non-refundable.

  • Do I need to take the modules in a specific order?

    We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

  • Do I get a certificate upon completion of the course?

    Yes, you will be given a certificate upon satisfactory completion of the course.

  • What is the fee for this course?

    The price of this course is INR 10,999/-

  • How long I can access the course?

    You will be able to access the course material for six months since the start of the course.

  • When will the classes be held in this course?

    This is an online self-paced course, which you can take any time at your convenience over the 6 months after your purchase.

  • How many hours per week should I dedicate to complete the course?

    If you can put between 6 to 8 hours a week, you should be able to finish the course in 8 to 10 weeks.

Support for Computer Vision using Deep Learning (PyTorch)