What you'll learn

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

Requirements

Basic Machine learning with Python Programming Language

Description

With the Deep learning making the breakthrough in all the fields of science and technology, 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 Computer Vision Applications out there.

A typical task in 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:

It is Pythonic

Easy to Learn

Higher Developer Productivity

Dynamic Approach for Graph computation - AutoGrad

GPU Support for computation, and much more...

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

Understand Basics of PyTorch

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

Learn Auto Grad feature of PyTorch

Implement Deep Learning models in Pytorch

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

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.

Who this course is for:

Software Developer
Machine Learning Practitioner
Data Scientist
Anyone interested to learn PyTorch

Homepage: https://www.udemy.com/course/deep-learning-pytorch/