Hands On Training

Convolutional Neural Networks with PyTorch

via

Cognitive Class

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Beginner

Teaching Type

Self Paced

Video Content

4 hours

Course Description

In this course, you'll learn how to solve the real world image processing and computer vision problems with PyTorch. Discover the potential that is Convolutional Neural Networks (CNNs) and learn the basics of convolution and max pooling as well as convolutional networks. Learn how to build your models using GPUs and use already-trained networks to transfer learning. . This course is part of the PyTorch Learning Path Check out the prerequisites Section.Throughout this course students will explore key subjects and gain hands-on experience to become proficient with CNNs. The course covers the following areas of importance: Note that this course is part of the PyTorch learning Path which includes the following. are necessary: or a thorough knowledge about PyTorch Tensors as well as DataSets, Linear Regression and Classification, Neural Networks Principles.

Course Overview

International Faculty

Post Course Interactions

Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Basic Linux Operating System knowledge.

Basic understanding of Apache Hadoop and Big Data.

Have taken the Hadoop Fundamentals learning path.

Have taken the Big Data Fundamentals learning path.

What You Will Learn

Explore various filter operations and learn to apply convolutions effectively to uncover valuable patterns.

Gain proficiency in incorporating max pooling layers within CNN architectures to enhance model performance.

Max Pooling: Delve into the concept of max pooling, a technique used to downsample feature maps and capture dominant features.

Understand the fundamental concept of convolution and its role in extracting meaningful features from images.

Course Content

Expand all sections

Module 1: Understand the fundamental concept of convolution and its role in extracting meaningful features from images.

Module 2: Explore various filter operations and learn to apply convolutions effectively to uncover valuable patterns.

Module 3: Max Pooling: Delve into the concept of max pooling, a technique used to downsample feature maps and capture dominant features.

Module 4: Gain proficiency in incorporating max pooling layers within CNN architectures to enhance model performance.

Course Instructors

Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math a...

Joseph Santarcangelo

PhD., Data Scientist

Joseph Santarcangelo is currently working as a Data Scientist at IBM. Joseph has a Ph.D. in Electrical Engineering. His research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition.