About the course
There has been a tremendous boom in the applications of Computer Vision now a days.
The applications of Computer Vision range from understanding the environment in a Self  Driving Car to build Facial Recognition based Attention Systems for classrooms in Education Industry.
A question you might ask is: why would I even want to know about Computer Vision ? As a matter of fact, there is an undeniable demand for people who have knowledge in this domain, so that they can bring about disruptive solutions in any industry possible.
Computer Vision systems deal with high variety and volume of data, specifically images or videos.It is represented as bits and blobs which is 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.
This course is designed to give you a taste of how the underlying techniques work in current State  of the  Art Computer Vision systems, and walks you through a few of the remarkable Computer Vision applications in a hands  on manner so that you can create such solutions on your own.
Prerequisites
This is a beginner friendly course, so it does not assume any familiarity with Computer Vision or Deep Learning algorithms. But, this course assumes that you are comfortable with Python programming.
Course curriculum

1
Welcome to the Course
 DataHack Summit 2019  India’s largest Applied Artificial Intelligence and Machine Learning Conference

2
Course Handouts
 Course Handouts

3
Introduction to computer vision
 Welcome to Computer Vision
 Knowing each other
 Documentary on Computer Vision FREE PREVIEW
 Exercise1
 Applications of Computer Vision
 Exercise2
 Why Computer Vision is more in Demand?
 Exercise3
 Understand your course content
 Exercise 4

4
Getting ready for the course
 Getting ready for the course
 System Requirements
 Setting up the System on Cloud
 Setting up locally
 Accessing the course material
 Prereads for the next modules

5
Introduction to Neural Network
 Understanding the problem
 Getting started with Neural Network
 Exercise : Getting started with Neural Network
 Independent and dependent variables
 Understanding Forward Propogation
 Exercise : Forward Propogation
 Math Behind Forward Propagation
 Exercise : Math Behind Forward Propagation
 Error and Reason for Error
 Exercise : Error and Reason for Error
 Gradient Descent Intuition
 Understanding Math Behind Gradient Descent
 Exercise : Gradient Descent
 Optimizer
 Exercise : Optimizer
 Back Propagation
 Exercise : Back Propagation
 Why Numpy?
 Exercise : Why Numpy?
 Understanding the Steps in Numpy FREE PREVIEW
 Exercise : Understanding the Steps in Numpy
 Defining Parameters in Numpy
 Exercise : Defining Parameters in Numpy
 Implementing Forward Propagation
 Exercise : Implementing Forward Propagation
 Implementing Backward Propagation
 Exercise : Implementing Backward Propagation
 Neural network from scratch (Notebook)
 Why Keras?
 Exercise : Why Keras?
 Neural Networks in Keras
 Exercise : Neural Network in Keras
 How to handle image data
 Exercise : How to handle Image data
 Exploring the Emergency Classification Dataset
 Exercise : Exploring the Emergency Classification Dataset
 Loading and PreProcessing Dataset
 Exercise : Loading and PreProcessing Dataset
 Solving the challenge
 Exercise : Solving the challenge
 Hyperparameter Tuning
 Exercise : Hyperparameter Tuning
 Simple Neural Network using keras (Notebook)
 Summary of the Module
 Assignment: Share your learning and build your profile

6
Project I
 Project I

7
Introduction to Convolutional Neural Network
 Why CNN?
 Exercise : Why CNN?
 Understanding the working of CNN Filters
 Exercise : Understanding the working of CNN Filters
 Introduction to Padding
 Exercise : Introduction to Padding
 Padding Strategies
 Exercise : Padding Strategies
 Padding Strategies in Keras
 Exercise : Padding Strategies in Keras
 Introduction to Pooling
 Exercise : Introduction to Pooling
 CNN architecture and its working
 Exercise : CNN architecture and its working
 Loading and PreProcessing Dataset
 Solving the challenge
 Exercise : Solving the challenge
 Hyperparameter Tuning
 Exercise : Hyperparameter Tuning
 Convolutional Neural Network in keras (Notebook)
 Summary of the Module
 Exercise : Gender Classification using CNN
 Assignment: Share your learning and build your profile

8
Introduction to Transfer Learning
 Introduction to Transfer Learning FREE PREVIEW
 How to select right pretrained model?
 Exercise : How to select right pretrained model?
 Steps to solve emergency vs non emergency vehicle classification challenge using transfer learning
 Exercise : Steps to build the model using transfer learning
 Solving the challenge using Transfer Learning (Part I)
 Exercise
 Solving the challenge using Transfer Learning (Part II)
 Exercise
 Solving the challenge using Transfer Learning (Part III)
 Exercise : Solving the challenge using transfer learning
 Solving the challenge using transfer learning (Notebook)
 Different fine tuning techniques
 Advantages of transfer learning
 Exercise : Gender Classification using transfer learning

9
Tips and Tricks to Improve Model Performance
 Understanding areas of improvement
 Exercise : Understanding areas of improvement
 Problem  Less Data to train the model
 Exercise : Less Data to train the model
 Solution  Less Data to train the model
 Exercise : Solution  Less Data to train the model
 Problem  High Variation in Data
 Exercise : High Variation in Data
 Solution  High Variation in Data
 Exercise : Solution  High Variation in Data
 Problem: Overfitting
 Exercise : Overfitting
 Solution  Overfitting
 Exercise : Solution  Overfitting
 Problem  Underfitting
 Exercise : Underfitting
 Problem  Too high training time
 Exercise : Too high training time
 Solution  Too high training time
 Problem  No Appropriate Architecture
 Exercise : No Appropriate Architecture
 Solution  No Appropriate Architecture
 Exercise : Solution  No Appropriate Architecture
 Problem  Wrong Evaluation Metric  Part I
 Exercise : Wrong Evaluation Metric  Part I
 Problem  Wrong Evaluation Metric  Part II
 Exercise : Wrong Evaluation Metric : Part II
 Combining Tips and Tricks
 Tips and tricks implementation (Notebook)
 Exercise : Combining the Trips and Tricks
 Exercise : Improving the model performance on Gender Classification
 Assignment: Share your learning and build your profile

10
Horizon of Computer Vision and Case Studies
 Understanding the Types of Computer Vision Problems
 Exercise : Understanding the type of problems
 Approach to solve different type of problems
 Exercise : Approach to solve different type of problems
 Understanding the Regression Problem
 Exploring Facial Keypoint Identification Problem
 Exercise : Exploring Facial Keypoint Identification Problem
 Loading and PreProcessing Dataset
 Solving the Regression Challenge
 Exercise : Solving the Regression Challenge
 Implementation : Facial Keypoint Identification (Notebook)
 Project II
 Understanding Object Detection Problem
 Exercise : Understanding Object Detection Problem Statement
 Naïve Appoach to solve detection problem
 Exercise : Naïve Appoach to solve detection problem
 Exploring Blood Cell Detection Problem
 Loading and PreProcessing Dataset
 Building the model
 Implementation : Blood cell detection  Part I (Notebook)
 Other Approaches to solve Detection Problem
 Exercise : Other Approaches to solve Detection Problem
 Implementing Stateoftheart Model on Blood Detection
 Understanding Stateoftheart Model
 Implementation : Blood cell detection  Part II (Notebook)
 Exercise : Understanding Stateoftheart Model
 Project III
 Assignment: Share your learning and build your profile

11
Where to go from here?
 What did we discuss?
 Where to go from here?

12
Bonus Material
 Visualization of Learning and Localization of Convolutional Neural Networks by Sunil Kumar Vuppala
 Generative Adversarial Networks by Keshav Dhandhania
 Attentionbased Deep Learning Models to Extract Details from Images by Vijay Gabale
 Diagnosing your Model : Learning How To Debug Deep Learning Models Using Visualisation For Medical Images  By Rohit Ghosh
 Hack Session: Understanding the Building Blocks of Deep Learning using PyTorch  By Vishnu Subramanian
 Failing Fast with Deep Learning  By Jaidev Deshpande
Project1
Classify Emergency Vehicles from NonEmergency Vehicles (Inclass)
Project2
Age Prediction of People from closeups of Facial Images
Project3
Identify the Location of Red Blood Cells (Inclass)
Instructor(s)

Faizan Shaikh
Faizan is working as a data scientist at Analytics Vidhya. Being a Deep Learning enthusiast, he aims to utilize his skills to push the boundaries of AI research. Faizan is an avid blogger on Analytics Vidhya, and has contributed to many articles to explain complex concepts of Deep Learning in a simple manner. He will be your instructor for the course. 
Neeraj Singh Sarwan
Neeraj is working as a data scientist at Analytics Vidhya. He has extensive experience in converting business problems to data problems. He has previously taken several corporate trainings and is also an avid blogger. He's a graduate of IITBHU and will be your instructor for the Python and Modeling modules.
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 nonrefundable.

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 selfpaced 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 2.0
Support for Computer Vision using Deep Learning 2.0 course can be availed through any of the following channels:
 Phone  10 AM  6 p.m. (IST) on Weekdays Monday  Friday on +918368253068
 Email training_support@analyticsvidhya.com (revert in 1 working day)
 Live interactive chat sessions (https://support.analyticsvidhya.com/ ), Monday to Friday between 7 PM to 8 PM IST.