Deep Learning : Computer Vision Beginner to Advanced Pytorch
4.2 (101 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
7,797 students enrolled

Deep Learning : Computer Vision Beginner to Advanced Pytorch

Go Beginner to Pro in Computer Vision in Pytorch / Python with Expert Tips Convolutional Neural Network Deep Learning
4.2 (101 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
7,797 students enrolled
Last updated 1/2020
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 7.5 hours on-demand video
  • 8 articles
  • 5 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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What you'll learn
  • Master how to Perform Computer Vision Task with Deep Learning
  • Learn to Work with PyTorch
  • Convolutional Neural Networks with Torch Library
  • Build Intuition on Convolution Operation on Images
  • Learn to Implement LeNet Architecture on CIFAR10 dataset which has 60000 images
Course content
Expand all 74 lectures 07:18:04
+ Introduction
8 lectures 35:57
Tensor Slicing and Reshape
03:25
Mathematical Operations on Tensors
02:15
Numpy in Pytorch
04:35
What is CUDA
04:04
Pytorch on GPU
07:12
Download Materials
00:01
Execute the Assignment as mentioned, and submit the answers
Assignment on Pytorch Basics
8 questions
+ AutoGrad in Pytorch
3 lectures 16:56
Autograd in Pytorch
12:04
Implementing Gradient Descent using Autograd
04:51
Download Materials
00:01
Execute the Assignments in your local systems and Submit the Code to complete the assignment
Assignment on Autograd
1 question
+ Creating Deep Neural Networks in Pytorch
4 lectures 18:48
Building first neural network
08:11
Writing Deep neural network
04:26
Writing Custom NN module
06:10
Download Materials
00:01
Perform the code execution locally and answer the questions
Assignment on Deep Neural Networks
2 questions
+ CNN on Pytorch
6 lectures 34:35
Data Loading - CIFAR10
10:53
Data Visualization
04:35
CNN Recap
03:44
First CNN
07:45
CNN Deep layers
07:37
Download Materials
00:01
+ LeNet Architecture in Pytorch
4 lectures 24:02
LeNet Overview
03:46
LeNet Model in Pytorch
11:25
Preparation & Evaluation
08:50
Download Materials
00:01
+ Optional Learning- Python Basics
21 lectures 02:08:55
Why Computer Programming Language
05:45
Why Python?
02:38
Getting System Ready - Installing Jup[yter Notebook
07:19
Jupyter Notebook - Tips & Tricks
05:56
What is Covered in this section
01:52
Variables in Python
08:36
Print Function
03:27
Numeric Data Type
05:02
String Data Type
03:48
Boolean Data Type
01:55
Type Conversion & Type Casting
06:18
Adding Comments in Python Programming Language
02:05
Data Structures in Python
09:11
Tuples & Sets in Python
08:25
Python Dictionaries
05:22
Conditional Statements in Python - if
11:16
Conditional Statements in Python - While
06:08
Inbuilt Functions in Python - range & input
07:43
For Loops
04:36
Functions in Python
09:16
Classes in Python
12:17
+ Mini Project with Python Basics
6 lectures 42:46
Mini Project - Hangman
06:18
Writing a class
07:54
Mini Project - Continued
05:22
Logic Building
06:18
Logic for Single Letter input
10:06
Final Testing
06:48
+ Python for Data Science - Numpy
6 lectures 51:20
Why Numpy?
00:25
Numpy
16:21
Resize & Reshape of Arrays
08:42
Slicing
06:04
Broadcasting
12:35
Mathematical Operations & Functions in Numpy
07:13
+ Python for Data Science - Pandas
6 lectures 45:59
Pandas Library
16:17
Pandas Dataframe
05:28
Pandas Dataframe - Load from External file
08:28
Working with null values
06:20
Slicing Pandas Dataframe
05:42
Imputation
03:44
Requirements
  • Basic Machine learning with Python Programming Language
Description

With the Deep learning making the breakthrough in all the fields of science and technology, Deep Learning Computer Vision is the field which is picking up at the faster rate where we see the applications in most of the applications out there.

Be it, Facebook's image tagging feature, Google Photo's People Recognition along with Scenery detection, Fraud detection, Facial Recognition, We are seeing the Deep Learning Computer Vision Applications out there.

A typical task in Deep Learning Computer vision task will include the methods for acquiring, processing, analyzing and understanding digital images, and extraction of these high-dimensional data from the real world in order to produce numerical or symbolic information, with which we can form decisions.

A typical & basic operation we perform is - Convolution Operations on Images, where we try to learn the representations of the image so that the computer can learn the most of the data from the input images.


In this course,

We will be learning one of the widely used Deep Learning Framework, i.e PyTorch

It is said as,

PyTorch to be Goto Tool for DeepLearning for Product Prototypes as well as Academia.


We are going to prefer learning - PyTorch for these Reasons:

  1. It is Pythonic

  2. Easy to Learn

  3. Higher Developer Productivity

  4. Dynamic Approach for Graph computation - AutoGrad

  5. GPU Support for computation, and much more...

In this course,  We are going to implement Step by Step approach of learning:

  1. Understand Basics of PyTorch

  2. Learn to Code in GPU & with guide to access free GPU for learning

  3. Learn Auto Grad feature of PyTorch

  4. Implement Deep Learning models in Pytorch

  5. Learn the Basics of Convolutional Neural Networks in PyTorch(CNN)

  6. Practical Application of CNN's on Real World Dataset

We believe that,

Learning will not be complete, untill you as a student has the confidence on the Subject.


So,

We have added Assignments at the end of each Section so that you can measure your progress along with learning.


We look forward to see you inside the course.

All the best.

- Manifold AI Learning ®

Who this course is for:
  • Software Developer
  • Machine Learning Practitioner
  • Data Scientist
  • Anyone interested to learn PyTorch
  • Anyone interested in Deep learning