PyTorch Course Online | PyTorch Certification -Prologinfo

Course Overview

Prologinfo offers a PyTorch Course Online that will help in completely understanding the basic concepts of Python and Torch. During the PyTorch online training, you will learn about how to work with PyTorch with dynamic computation graphs, Torch sensors, and the autograd to compute gradients. At the start of online training, firstly, you will understand the fundamentals of training a neural network, gradient computation, and the forward and backward passes.

You will also learn about Tensors and datasets, Softmax Regression, Deep Networks, Peer review, an introduction to PyTorch, Linear Regressions, Multiple Input Output Linear Regression, Shallow Neural Networks, Convolutional Neural Networks, Peer Review, and Logistic Regression for classification, and many more throughout the online training. Our professional instructors also teach you several advanced topics such as linear regression Prediction, PyTorch Linear Regression training slope and bias, training parameters in PyTorch, Logistic Regression Cross Entropy, Cross-Entropy Loss, and many more. This online course is designed by our professional instructors is that a way that helps in passing several certification exams such as PyTorch Deep Learning Engineer (CNN) Engineer and many more. When you will get PyTorch certification, then you will get a job easily in a top company with exciting packages. Thus, this online course is good for your career growth in the future.

PyTorch Certification Key features

Learn the concept of PyTorch GitHub
Get a certificate after completing the PyTorch certification
Know the details of the PyTorch data loader
Learn how to Install PyTorch
Understand the concept of PyTorch lightning
Use mock test sessions to know about the types of questions that you will face in the real exam
Demo classes will also be provided before starting the course

Who should take PyTorch Course Online?

People who will be benefitted from this course can be experienced professionals. Freshers can also join the course. The candidates should have basic knowledge of Python and they should also know about using NumPy, Matplotlib, along with other libraries.

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Course curriculum / Syllabus

Classification

How to define Softmax Regression?
How to use Softmax in PyTorch Regression?
Training Softmax in PyTorch Regression

Neural Networks

Networks overview
Network Shape Depth vs Width
Concept of Back Propagation
Concept of Activation functions

Deep Networks

Understand the concept of Dropout
What is Initialization?
Batch normalization concepts
Other optimization methods

Computer Vision Networks

Understand the concept of Convolution
Define Max Polling
Know about Convolutional Networks
What are Pre-trained Networks?

Dimensionality reduction and autoencoders

Principle component analysis
Concept of Linear autoencoders
Concept of Autoencoders
Concept of Transfer learning
Concept of Deep Autoencoders

Independent Project

Independent Project

pytorch FAQ’s:

1.Will you teach the concept of deep learning?

Yes! Deep learning concepts will be included in the course.

2.Will we get any exercises regarding coding?

Yes! We will teach you the concept of coding and you will also be given practical exercises.

3.Which Python libraries will be included in the course?

The Python libraries that will be included in the course are

NumPy
PyTorch
Matplotlib

These are a few libraries. Besides these, other libraries will also be taught top you.

4.How will practical classes be arranged?

We will provide you the required software and access to server so that you can perform your practicals easily.

5.What can I do in the case of missed classes?

A video recording for each lecture will be available with us. We will provide you all those videos so that you can learn the concepts taught during missed classes.

6.Why Prolog Info?

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