Master PyTorch

#001 PyTorch – How to Install PyTorch with Anaconda?

A brief step by step tutorial on how to install PyTorch with Anaconda

#002 PyTorch - What are Tensors in PyTorch 1.3?

Learn what are tensors - the main data structure of PyTorch

#003 PyTorch – How to implement Linear Regression in PyTorch

Learn what Linear regression is and how to create a linear regression model in Python using PyTorch

#004 PyTorch – Computational graph and Autograd with Pytorch

Learn to decompose complex computations into several sequences by using computation graphs

#005 PyTorch – Logistic Regression in PyTorch

Learn how to solve the binary classification problem using Logistic regression

#006 PyTorch – Solving the famous XOR problem using Linear Classifiers with PyTorch

In this post, we study the expressiveness and limitations of Linear Classifiers, and learn how to solve the XOR problem

#007 PyTorch – Linear Classifiers in PyTorch – Experiments and Intuition

Learn to solve image classification problems using Linear Classifiers

#008 PyTorch – DataLoaders with Pytorch

Learn how to create and use PyTorch Dataset and DataLoader objects

#009 PyTorch – How to apply Backpropagation With Vectors And Tensors

Learn to calculate the loss using backpropagation with vectors, matrices, and tensors of higher ranks

#010 PyTorch – Artificial Neural Network with Perceprton on CIFAR10 using PyTorch

Learn to build the Fully Connescted Neural Network with a Multilayer Perceptron cllas in Python using PyTorch

#011 Pytorch – RNN with PyTorch

RNN/LSTM model implemented with PyTorchc

#012 PyTorch – Shallow Neural Network in PyTorch 1.5

Learn how to train a neural network model to make accurate predictions in PyTorch

#013 PyTorch – Shallow Neural Network on MNIST dataset in PyTorch

Learn how to build efficient Neural Networks using nn module with MNIST dataset

#014 PyTorch – Convolutional Neural Network on MNIST in PyTorch

Learn to build efficient Convolutional Neural Networks using the nn module with MNIST dataset

#015 PyTorch – Building A Smile Detector with PyTorch

Learn to build a Smile Detection model in PyTorch on the CelebA dataset using the LeNet-5 architecture

#016 PyTorch – Three hacks for improving the performance of Deep Neural Networks

Three very popular deep learning techniques that you can apply to improve the performance of our deep learning model

#017 PyTorch – How to apply Batch Normalization in PyTorch

Batch normalization is a technique for training deep neural networks that standardizes the inputs to a layer for each mini-batch

#018 PyTorch – Popular techniques to prevent the Overfitting in a Neural Networks

Learn to reduce overfitting - one of the most common problems that arise during the training of deep neural networks

#019 Siamese Network in PyTorch with application to face similarity

When we want to see how similar two pictures are. One solution that was developed to solve this problem are Siamese Neural Networks

#020 Overview of Semantic Segmentation methods

The goal of Semantic Segmentation is to label each pixel of an image with a corresponding class

#022 PyTorch – DeepLab v2 Semantic Segmentation in PyTorch

Overview of the DeepLab v2 algorithm that introduced three famous advancements in the field of semantic segmentation

#023 PyTorch – DeepLab v3+ for Semantic Segmentation in PyTorch

Learn about the novelties of the DeepLab v3 algorithm that utilizes the model developed in Version 2

#025 FaceNet: A Unified Embedding for Face Recognition and Clustering in PyTorch

FaceNet, paper ntroduced a lot of novelties and significantly improved the performance of face recognition, verification, and clustering tasks

#026 VGGFace: Deep Face Recognition in PyTorch by Oxford VGG

VGGFace paper presents interesting novelties in the Face Recognition area

#027 R-CNN, Fast R-CNN, and Faster R-CNN explained

An overview of R-CNN, one of the most influential families of object detection algorithms

#028 PyTorch – Visualization and understanding of Convolutional Neural Networks in PyTorch

Learn about the importance of visualization of Convolutional Neural Network