PyTorch / Neural Networks and Deep Learning

PyTorch vs TensorFlow in 2026: Differences, Code Comparison and Which to Choose

30 October 2026

PyTorch vs TensorFlow in 2026: Differences, Code Comparison and Which to Choose

For years “PyTorch or TensorFlow?” was the first question in any deep learning course. In 2026 the answer has become much clearer. PyTorch is the default framework for research, large…

Epoch vs Batch Size vs Iteration in Machine Learning

28 October 2026

Epoch vs Batch Size vs Iteration in Machine Learning

If you have trained a neural network, you have written something like model.fit(X, y, epochs=10, batch_size=32) and wondered what exactly the numbers mean and how they relate to the “steps”…

Recurrent Neural Network (RNN): How RNNs Work, Types and Examples

28 October 2026

Recurrent Neural Network (RNN): How RNNs Work, Types and Examples

A recurrent neural network (RNN) is a neural network for sequential data: text, speech, time series, video. Instead of looking at each input separately, it reads a sequence one element…

LSTM (Long Short-Term Memory): How LSTM Networks Work

27 October 2026

LSTM (Long Short-Term Memory): How LSTM Networks Work

Long short-term memory (LSTM) is a type of recurrent neural network designed to remember information over long sequences. An ordinary recurrent network forgets quickly: the influence of an input fades…

Generative Adversarial Networks (GANs): How They Work, With PyTorch Code

26 October 2026

генеративно-состязательная нейросеть GAN

Generative Adversarial Networks (GANs): How They Work, With PyTorch Code

A generative adversarial network (GAN) is a generative model made of two neural networks trained against each other. The generator turns random noise into fake data, such as images; the…

Variational Autoencoder (VAE): How It Works, With PyTorch Code

25 October 2026

Variational Autoencoder (VAE): How It Works, With PyTorch Code

A variational autoencoder (VAE) is a generative neural network that learns to compress data into a small, smooth latent space and to generate new data from it. Like an ordinary…

Convolutional Neural Networks (CNN): How They Work, Step by Step

25 October 2026

Convolutional Neural Networks (CNN): How They Work, Step by Step

A convolutional neural network (CNN, or ConvNet) is a type of neural network designed for data with a grid structure, above all images. Instead of connecting every input pixel to…

Backpropagation Explained: Algorithm, Example and Python Code

24 October 2026

Backpropagation Explained: Algorithm, Example and Python Code

Backpropagation (backward propagation of errors) is the algorithm that computes how much each weight in a neural network contributed to the error, so that gradient descent can adjust it. It…

Activation Functions in Neural Networks: ReLU, Sigmoid, GELU and More

23 October 2026

Activation Functions in Neural Networks: ReLU, Sigmoid, GELU and More

An activation function is the small nonlinear function a neuron applies to its weighted input before passing the result on: a = f(w · x + b). Without it, a…

Gradient Descent Explained: Algorithm, Types and Python Code

23 October 2026

Gradient Descent Explained: Algorithm, Types and Python Code

Gradient descent is the algorithm that trains almost every machine learning model: it repeatedly nudges the model’s parameters in the direction that lowers the loss, using the update rule θ…

GraphGallery: a library for graph neural networks on PyTorch and TensorFlow

2 March 2021

2 March 2021

GraphGallery: a library for graph neural networks on PyTorch and TensorFlow

2 March 2021

GraphGallery is a library for training and testing graph neural networks. GraphGallery implements adversarial attacks on graph neural networks. The library is compatible with PyTorch, TensorFlow 2.x, Pytorch Geometric (PyG),…

FaceX-Zoo: PyTorch library for face recognition in images

29 January 2021

29 January 2021

FaceX-Zoo: PyTorch library for face recognition in images

29 January 2021

FaceX-Zoo is an open-source library on PyTorch for recognizing faces in images. The library provides a module for training models with different configurations of error functionality and basic architecture. In…

PyTorch

PyTorch vs TensorFlow in 2026: Differences, Code Comparison and Which to Choose

PyTorch vs TensorFlow in 2026: Differences, Code Comparison and Which to Choose

Epoch vs Batch Size vs Iteration in Machine Learning

Epoch vs Batch Size vs Iteration in Machine Learning

Recurrent Neural Network (RNN): How RNNs Work, Types and Examples

Recurrent Neural Network (RNN): How RNNs Work, Types and Examples

LSTM (Long Short-Term Memory): How LSTM Networks Work

LSTM (Long Short-Term Memory): How LSTM Networks Work

Generative Adversarial Networks (GANs): How They Work, With PyTorch Code

Generative Adversarial Networks (GANs): How They Work, With PyTorch Code

Variational Autoencoder (VAE): How It Works, With PyTorch Code

Variational Autoencoder (VAE): How It Works, With PyTorch Code

Convolutional Neural Networks (CNN): How They Work, Step by Step

Convolutional Neural Networks (CNN): How They Work, Step by Step

Backpropagation Explained: Algorithm, Example and Python Code

Backpropagation Explained: Algorithm, Example and Python Code

Activation Functions in Neural Networks: ReLU, Sigmoid, GELU and More

Activation Functions in Neural Networks: ReLU, Sigmoid, GELU and More

Gradient Descent Explained: Algorithm, Types and Python Code

Gradient Descent Explained: Algorithm, Types and Python Code

GraphGallery: a library for graph neural networks on PyTorch and TensorFlow

GraphGallery: a library for graph neural networks on PyTorch and TensorFlow

FaceX-Zoo: PyTorch library for face recognition in images

FaceX-Zoo: PyTorch library for face recognition in images

Depth Estimation Using Encoder-Decoder Networks and Self-Supervised Learning

Depth Estimation Using Encoder-Decoder Networks and Self-Supervised Learning

“Seeing Beyond Walls” — Human Pose Estimation Under Occlusions

“Seeing Beyond Walls” — Human Pose Estimation Under Occlusions

Image Inpainting for Irregular Holes Using Partial Convolutions

Image Inpainting for Irregular Holes Using Partial Convolutions