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

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

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, FacialRecognition, 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 GotoTool 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 toLearn

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 theBasics 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 AILearning ®