Machine Learning / Neural Networks and Deep Learning

Data Science Interview Questions: 60 Questions With Answers and Code

14 November 2026

Data Science Interview Questions: 60 Questions With Answers and Code

Data science interviews test a wide mix of skills: statistics and probability, machine learning, SQL and Python, experiment design, product thinking and communication. The questions differ by company and role,…

Kaggle Titanic Tutorial: Predicting Survival With Python, Step by Step

8 November 2026

Kaggle Titanic Tutorial: Predicting Survival With Python, Step by Step

The Titanic competition is the classic first project on Kaggle: given data about 891 passengers, predict who survived the sinking of the Titanic in April 1912. It is small enough…

Artificial Intelligence: How AI Works and How to Create Your Own AI Model

7 November 2026

Artificial Intelligence: How AI Works and How to Create Your Own AI Model

Artificial intelligence (AI) is the field of computer science that builds machines able to do things we normally associate with human intelligence: understanding language, recognising images, solving problems, making decisions…

Cloud GPU Pricing in 2026: What It Costs to Rent an H100, B200 or RTX 5090

5 November 2026

Illustration: GPU chips connected to a cloud, with a rising price line

Cloud GPU Pricing in 2026: What It Costs to Rent an H100, B200 or RTX 5090

Renting a GPU in the cloud is the fastest way to get serious compute for deep learning: no hardware to buy, no drivers to fight with on your own machine,…

PyTorch Tutorial for Beginners: From Installation to Your First Neural Network

3 November 2026

PyTorch Tutorial for Beginners: From Installation to Your First Neural Network

PyTorch is the most widely used deep learning framework in 2026: it powers most research papers, almost all open large language models and the tools around them, from Hugging Face…

Deep Learning: How Many-Layered Neural Networks Learn From Raw Data

3 November 2026

Deep Learning: How Many-Layered Neural Networks Learn From Raw Data

Deep learning is the part of machine learning that trains artificial neural networks with many layers to learn directly from raw data: pixels, sound waves, text. Instead of a programmer…

Recommender Systems: How Recommendation Engines Work, With Python Code

2 November 2026

Recommender Systems: How Recommendation Engines Work, With Python Code

A recommender system is software that predicts what a person is likely to want next and shows it to them: the next video on YouTube, the “customers also bought” row…

How Neural Networks Work: Neurons, Layers and Training, Step by Step

1 November 2026

How Neural Networks Work: Neurons, Layers and Training, Step by Step

Neural networks recognize faces in photos, translate text, recommend videos and power chatbots such as ChatGPT and Claude. Under the hood, every one of them runs the same simple idea:…

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…

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 + … +…

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…