Create Your Own Deep Learning Projects Using Pytorch

Free Download What you'll learn
develop the right mentality to approach an advanced deep learning project
learn how to develop a deep learning projects and implement papers using pytorch and other libraries that are standard on the field
introduction to additional tools that are a standard on the field
Implementing a paper from scratch as an example.
Introduction to object detection
Requirements
basic python knowledge
knowledge about training neural networks on pytorch
basic numpy knowledge
Description
In this course you will learn the data representation skills and several other tools that you help you to:Implement research papers.Create projects that require a sophisticated data pipeline.Have a strong foundation to consume more advance educational content regarding deep learning or even data science.Though a practical project that consist on implementing an object detection paper from scratch some things that you need to know about the project:How only need to be familiar with image classification on PyTorch knowing about object detection is not a prerequisite.Having a high end GPU is not a requisite we can even get the job done with a google colab .We will not see any complex computer vision math equation.But what's object detection exactly:Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, animals,buildings, or cars) in digital images and videos. It is widely used in computer vision tasks such as image annotation, vehicle counting, activity recognition, face detection, face recognition, video object co-segmentation. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video.It is a very hot topic in deep learning and you will learn the basics object detection using convolutional neural networks (CNN) on top of everything else previously mentioned.
Overview
Section 1: Introduction
Lecture 1 Introduction to the course
Section 2: Introduction to the paper
Lecture 2 Introduction to the paper
Section 3: Data preparation
Lecture 3 Data preparation
Section 4: Building a dataset
Lecture 4 building dataset
Section 5: Building a loss function
Lecture 5 building loss function
Section 6: Building a model
Lecture 6 model
Section 7: Training a model
Lecture 7 training
Section 8: Making predictions
Lecture 8 inference
Beginner deep learning developers that want to get to the next level.


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