Autoencoder In PyTorch - Theory & Implementation

In this Deep Learning Tutorial we learn how Autoencoders work and how we can implement them in PyTorch.

Code: https://github.com/python-engineer/pytorch-examples

#pytorch #deep-learning

What is GEEK

Buddha Community

Implementing Real-time Object Detection System using PyTorch and OpenCV

Hands-On Guide to implement real-time object detection system using python

The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade.

Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together.

Python vs C++

Reading The Video Stream

Load the Model

Scoring a Single Frame

…

#artificial-intelligence #python #programming #implementing real-time object detection system #implementing real-time object detection system using pytorch and opencv #pytorch

Autoencoder In PyTorch - Theory & Implementation

In this Deep Learning Tutorial we learn how Autoencoders work and how we can implement them in PyTorch.

Code: https://github.com/python-engineer/pytorch-examples

#pytorch #deep-learning

PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

Facebook Gives Away This PyTorch Library For Differential Privacy

Recently, Facebook AI open-sourced a new high-speed library for training PyTorch models with differential privacy (DP) known as Opacus. The library is claimed to be more scalable than existing state-of-the-art methods.

According to the developers at the social media giant, differential privacy is a mathematically rigorous framework for quantifying the anonymisation of sensitive data. With the growing interest in the machine learning (ML) community, this framework is often used in analytics and computations.

Differential privacy constitutes a strong standard for privacy guarantees for algorithms on aggregate databases. It is usually defined in terms of the application-specific concept of adjacent databases. The framework has several properties that make it particularly useful in applications, such as group privacy, robustness to auxiliary information, among others.

#developers corner #differential privacy #facebook ai research #facebook differential privacy #opacus #pytorch #pytorch library #pytorch library opacus

Complete Guide to build an AutoEncoder in Pytorch and Keras

This article is continuation of my previous article which is complete guide to build CNN using pytorch and keras.

Taking input from standard datasets or custom datasets is already mentioned in complete guide to CNN using pytorch and keras. So we can start with necessary introduction to AutoEncoders and then implement one.

AutoEncoders

Auto Encoder is a neural network that learns encoding data with minimal loss of information.

There are many variants of above network. Some of them are:

Sparse AutoEncoder

This auto-encoder reduces overfitting by regularizing activation function hidden nodes.

Denoising AutoEncoder

This auto-encoder is trained by adding noise to input. This will remove noise from input at evaluation.

#keras #variational-autoencoder #pytorch