DynaFlow: A Main Agent Writes Code for Its Team of Sub-Agents and Solves Every AIME 2024-2026 Problem
29 October 2026
DynaFlow: A Main Agent Writes Code for Its Team of Sub-Agents and Solves Every AIME 2024-2026 Problem
Hard tasks are increasingly handled not by a single neural network but by a team of AI agents: one searches for information, another does the work, and a third checks…
Best GPU for AI in 2026: How to Choose a GPU for Deep Learning and LLMs
28 October 2026
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…
5 AI Strategy Programs for Marketing Leaders Without a Technical Background
26 October 2026
5 AI Strategy Programs for Marketing Leaders Without a Technical Background
Marketing teams have plenty of AI tools to choose from. The difficult part is knowing which ones deserve a place in the budget. Can an automated campaign improve conversions? Is…
StreamEval: The Best Streaming Video Generation Model Completes Only 15% of Reference Edits
26 October 2026
StreamEval: The Best Streaming Video Generation Model Completes Only 15% of Reference Edits
Researchers from Tsinghua University, NVIDIA, CMU, UC Berkeley, Princeton, NUS and Reactor released StreamEval, a benchmark that measures how well streaming video generation handles real-time interaction: can a model execute…
Generative Adversarial Networks (GANs): How They Work, With PyTorch Code
26 October 2026
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…
From Video Storage to Video Intelligence: How AI Is Transforming Cloud Surveillance Systems
26 October 2026
From Video Storage to Video Intelligence: How AI Is Transforming Cloud Surveillance Systems
Security cameras have become excellent at recording events. Finding useful information in those recordings, however, remains a challenge. Consider a business operating 100 security cameras at 2 Mbps each. Recording…
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…