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- Duration / Course length: 1 To 2 Months Start now
- Certificates:
- Course delivery: This course is delivered in video format
Course details
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 ;Note: Hard copy material is not applicable for this course.
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.
Benefits of Certification
- Government certification
- Certification valid for life
- Lifelong e-learning access
- Learning Hours: 12+ hrs
How It Works
- Select Certification & Register
- Receive Online e-Learning Access (LMS)
- Take exam online anywhere, anytime
- Get certified & Increase Employability
- Duration: 60 minutes
- No. of questions: 50
- Maximum marks: 50, Passing marks: 25 (50%).
- There is NO negative marking in this module.
- Online exam.
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
- 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 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 Motivation
- Word Embedding
- Recurrent Neural Networks
- Building a Text Generation Model in PyTorch
- Training and Testing
- Autoencoders Motivation
- How Autoencoders Work
- Types of Autoencoders
- Building Denoising Autoencoder Using PyTorch
- Training and Testing
- Reinforcement Learning Motivation
- Reinforcement Learning Concepts
- DQN, Experience Replay
- The OpenAI Gym Environment
- Building the Cartpole Agent Using DQN
- Training and Testing
FIRST STOP: A QUICK INTRODUCTION TO PYTORCH
- The Course Overview
- What Makes PyTorch Special?
- Installing PyTorch
- 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?
- 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?
- 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
- 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
Eligibility / Requirements
Anyone can apply for the online certification
Job roles this course is suitable for:
Deep Learning Engineer , AI Developer , Neural Network ArchitectAbout Vskills
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