Guides / 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…

Best GPU for AI in 2026: How to Choose a GPU for Deep Learning and LLMs

28 October 2026

Illustration of a GPU chip with memory stacks and circuit traces

Best GPU for AI in 2026: How to Choose a GPU for Deep Learning and LLMs

Deep learning runs on GPUs, and the right GPU decides whether an experiment takes minutes or days, and whether a model fits on your machine at all. In 2026 the…

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”…

Perceptron: What It Is, How It Learns, and the Multilayer Perceptron

28 October 2026

Perceptron: What It Is, How It Learns, and the Multilayer Perceptron

A perceptron is the simplest neural network: a single artificial neuron that takes several inputs, multiplies each by a weight, adds them up with a bias and outputs 1 or…

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…

Supervised vs Unsupervised vs Reinforcement Learning: Differences and Examples

27 October 2026

Supervised vs Unsupervised vs Reinforcement Learning: Differences and Examples

Machine learning algorithms are usually grouped by how they learn, and in particular by what kind of feedback they get during training. In supervised learning the model learns from examples…

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…

Linear Regression in Machine Learning: How It Works, With Python Examples

25 October 2026

Linear Regression in Machine Learning: How It Works, With Python Examples

Linear regression is a machine learning model that predicts a numeric value as a weighted sum of input features plus a constant: ŷ = w₁x₁ + w₂x₂ + … +…

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…

Decision Trees in Machine Learning: How They Work, With Python Examples

24 October 2026

Decision Trees in Machine Learning: How They Work, With Python Examples

A decision tree is a machine learning model that makes predictions by asking a sequence of yes/no questions about the input, such as “Is the departure time before 8:15?” or…

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…

Gradient Boosting Explained: Algorithm, Python Code, XGBoost vs LightGBM

24 October 2026

Gradient Boosting Explained: Algorithm, Python Code, XGBoost vs LightGBM

Gradient boosting is a machine learning technique that builds a strong model from many weak ones, usually small decision trees, by adding them one at a time. Each new tree…

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…

Bayes’ Theorem Explained: Formula, Examples and Python Code

23 October 2026

Bayes’ Theorem Explained: Formula, Examples and Python Code

Bayes’ theorem tells you how to update a probability when new evidence arrives: the probability of a hypothesis given the evidence equals the probability of the evidence given the hypothesis,…

How an AI Talking Photo Generator Works in Production: Input Specs and Governance

21 October 2026

How an AI Talking Photo Generator Works in Production: Input Specs and Governance

Deploying an ai talking photo generator inside an enterprise content or technical documentation pipeline requires much more than uploading a random headshot and typing a script. In consumer demos, any…

Brand Consistency in AI Image Generation: a Review System for Campaign Teams

14 October 2026

Brand Consistency in AI Image Generation: a Review System for Campaign Teams

Brand consistency in AI image generation depends less on the model you pick than on what you fix before generating. Teams that lock a small set of visual rules (palette,…

Building a Private RAG Pipeline with a Local LLM: A Practical Guide for Developers (2026)

13 October 2026

Building a Private RAG Pipeline with a Local LLM: A Practical Guide for Developers (2026)

To build a private Retrieval-Augmented Generation (RAG) pipeline, generate vector embeddings locally using an open-source model, store them in an on-premise vector database, and query a quantized local LLM over…

OpenAI Codex Beginner’s Guide: Setup, Workflows, and Pricing

18 May 2026

OpenAI Codex Beginner’s Guide: Setup, Workflows, and Pricing

AI coding tools have changed dramatically over the past two years. Early assistants like GitHub Copilot mostly worked as advanced autocomplete systems — useful for speeding up repetitive coding, but…