PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. Like Python, PyTorch has a clean and simple API, which makes building neural networks faster and easier. 

Why should one take this certification?

In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts.

By the end of this course, you will be able to use PyTorch proficiently in your real-world projects.

Who will benefit from taking this certification?

Job seekers looking for employment in IT companies, IT departments of PSUs or MNCs, will find the certification of great help. Certification in Deep learning with Pytorch framework benefits Data Science professionals, students and professionals.


Companies that hire Vskills Deep Learning with PyTorch Professionals

IT companies, MNCs, Consultancies hire Pytorch professionals for Data Science related opportunities. Companies employing Data Science include Capgemini, JP Morgan Chase, TCS, Wipro, Zensar, Accenture etc.

Deep Learning with PyTorch Table of Contents

https://www.vskills.in/certification/deep-learning-with-pytorch-table-of-content


Apply for Deep Learning with PyTorch Certification

By Net banking / Credit Card/Debit Card

We accept Visa/Master/Amex cards and all Indian Banks Debit Cards. There is no need to fill application form in case you are paying online.

Please click buy now to proceed for online payments.

  • Visa Card
  • Master Card
  • American Express
Buy Now

TABLE OF CONTENT


Module 1

GETTING STARTED WITH PYTORCH

  • The Course Overview
  • Introduction to PyTorch
  • Installing PyTorch on Linux and Windows
  • Installing CUDA
  • Introduction to Tensors and Variables
  • Working with PyTorch and NumPy
  • Working with PyTorch and GPU
  • Handling Datasets in PyTorch
  • Deep Learning Using PyTorch

TRAINING YOUR FIRST NEURAL NETWORK

  • Building a Simple Neural Network
  • Loss Functions in PyTorch
  • Optimizers in PyTorch
  • Training the Neural Network
  • Saving and Loading a Trained Neural Network
  • Training the Neural Network on a GPU

COMPUTER VISION – CNN FOR DIGITS RECOGNITION

  • Computer Vision Motivation
  • Convolutional Neural Networks
  • The Convolution Operation
  • Concepts - Strides, Padding, and Pooling
  • Loading and Using MNIST Dataset
  • Building the Model
  • Training and Testing

SEQUENCE MODELS – RNN FOR TEXT GENERATION

  • Sequence Models Motivation
  • Word Embedding
  • Recurrent Neural Networks
  • Building a Text Generation Model in PyTorch
  • Training and Testing

AUTOENCODER - DENOISING IMAGES

  • Autoencoders Motivation
  • How Autoencoders Work
  • Types of Autoencoders
  • Building Denoising Autoencoder Using PyTorch
  • Training and Testing

REINFORCEMENT LEARNING – BALANCE CARTPOLE USING DQN

  • Reinforcement Learning Motivation
  • Reinforcement Learning Concepts
  • DQN, Experience Replay
  • The OpenAI Gym Environment
  • Building the Cartpole Agent Using DQN
  • Training and Testing

Module 2

FIRST STOP: A QUICK INTRODUCTION TO PYTORCH

  • The Course Overview
  • What Makes PyTorch Special?
  • Installing PyTorch

SLEEPING UNDER THE STARS: IT'S A BIRD...IT'S A PLANE...IT’S A CNN?

  • Problem: Detect a Specific Type of Object in an Image
  • Quick Win: Using a Pretrained AlexNet Model for Beaver Detection
  • Getting and Preparing Image Data
  • Building, Training, and Testing Your Model
  • Using Your Model to Detect Beavers and What’s Next?

GOING ABROAD: LANGUAGE DETECTION FOR FUN AND PROFIT WITH RNN

  • Problem: Recognize the Language of a Specific Text
  • Understanding and Preparing Language Data
  • Building, Training, and Testing Your Model for Language Detection
  • Using Your Model to Detect Languages and What’s Next?

MAKING FRIENDS: LOST IN TRANSLATION WITH LSTM

  • Problem: Translate a Specific Text from One Language to Another
  • Understanding and Preparing Dataset for Language Translation
  • Building, Training, and Testing Your Models for Language Translation
  • Using Your Models for Language Translation

GETTING SOME CULTURE: BECOMING A DEEP NEURAL PICASSO WITH DNN

  • Problem: Extract Key Style Features from One Image and Use It on Another One
  • Preparing Images for Style Transfer
  • Building and Training Style Transfer Model
“Exam scheduling to be done through user account” / “Exam once scheduled cannot be cancelled”
Date of Examination
05-Oct-2019
06-Oct-2019
19-Oct-2019
20-Oct-2019
02-Nov-2019
03-Nov-2019
16-Nov-2019
17-Nov-2019
07-Dec-2019
08-Dec-2019
21-Dec-2019
22-Dec-2019
Examination Time
01:00 PM - 02:00 PM
02:30 PM - 03:30 PM
04:00 PM - 05:00 PM
05:30 PM - 06:30 PM
10:00 AM - 11:00 AM
11:30 AM - 12:30 PM

Write a review


Your Name


Your Review Note: HTML is not translated!

Rating Bad           Good

Captcha

Write a review

Note: HTML is not translated!
    Bad           Good


Captcha

Tags: Deep Learning with PyTorch, PyTorch, Deep Learning, Data Science