The ccv library, created in 2010 and still actively maintained, offers a highly portable C‑based computer‑vision toolkit with minimal dependencies, a built‑in cache for preprocessing, a full libnnc neural‑network runtime, and easy builds via Bazel, Make, or Swift Package Manager.
This article walks through twenty essential PyTorch concepts—from basic Tensor creation and manipulation, through autograd and neural‑network construction, to data loading, GPU acceleration, model saving, and practical training tricks—providing concrete code examples and clear explanations for developers eager to build and deploy AI models.
This article provides a comprehensive, step‑by‑step explanation of the Transformer architecture that underpins large language models, covering tokenization, embeddings, positional encoding, attention mechanisms, feed‑forward networks, layer stacking, a detailed translation example, visualized attention weights, and a survey of recent open‑source LLM designs such as DeepSeek V3, OLMo 2, and Gemma 3.
Learn the fundamentals of deep learning by building, training, and evaluating a TensorFlow model that recognizes handwritten digits from the MNIST dataset, covering data preparation, network architecture, activation functions, optimizer choices, model compilation, training loops, evaluation metrics, and visualization of predictions.
This tutorial walks you through building a digit classifier using PyTorch and the MNIST dataset, covering environment setup, data loading, model construction, training, evaluation, and model persistence while explaining core deep‑learning concepts.
This tutorial walks Java developers through the complete workflow of building, training, and evaluating a neural network in PyTorch, covering network definition, data iteration, forward and backward passes, loss calculation, and parameter updates with detailed code examples and Java‑centric analogies.
This tutorial introduces the fundamentals of deep learning, covering neural network basics, Keras fundamentals, and provides a step‑by‑step Python example that loads the Iris dataset, preprocesses data, builds, compiles, trains, evaluates, visualizes, and predicts with a simple neural network model.
This article presents the SCNN model for multivariate time series forecasting, explains its decomposition into long‑term, seasonal, short‑term, and co‑evolving components, details the neural‑network‑based fusion and loss design, provides Python code snippets, and demonstrates its practical deployment for business volume prediction at Ctrip.
This article walks through constructing a basic neural network using only Python and NumPy, explains the underlying concepts such as neurons, training cycles, sigmoid activation, and weight‑adjustment formulas, and provides complete, runnable code with sample inputs and outputs.
This article explains the principle of spatial attention in convolutional neural networks, details the underlying algorithmic steps, and provides a complete PyTorch implementation including the attention module, full network architecture, and practical considerations for integrating spatial attention into deep learning models.
This article explains the fundamental architecture of large language models, from the dual file nature of parameters and code, through neural network basics, perceptrons, and weight training, to the Transformer’s tokenization, positional encoding, self‑attention, and inference processes, illustrated with diagrams and examples.
This article uses a narrative of an AI neuron to explain fundamental concepts of neural networks, including neuron structure, weighted sums, activation functions, loss functions, gradient descent, and learning rate, making complex AI topics accessible to beginners.
This article uses a blindfolded mountain‑climbing analogy to explain how gradient descent trains neural networks, covering cost functions, learning rates, iterative updates, and provides a Python implementation for a simple three‑layer network example.
Airbnb upgraded its neural‑network ranking system by adding a similarity network that penalizes duplicate‑like listings, enabling the algorithm to present a more diverse set of options, which boosted booking rates, value, and five‑star ratings, demonstrating that reduced result similarity improves overall search quality.
This tutorial demonstrates how to download the Fashion‑MNIST dataset, build a four‑layer fully connected neural network with PyTorch, and train it using loss functions, Adam optimizer, learning‑rate strategies, and Dropout to achieve high‑accuracy multi‑class image classification.
This article explains the inner workings of ChatGPT, covering its probabilistic token generation, transformer architecture, attention mechanisms, embeddings, training process, and the mathematical principles that enable a massive neural network to produce coherent, human‑like text.
This paper presents a neural‑enhanced camera fingerprint framework combined with zero‑knowledge proof and digital signature schemes to reliably trace the originating device of photos, offering high‑accuracy identification, privacy preservation, and resistance to forgery across various application scenarios.
This article provides a comprehensive, step‑by‑step explanation of how ChatGPT generates text, covering token probabilities, n‑gram models, embeddings, attention mechanisms, and the Transformer architecture, while illustrating concepts with Wolfram‑language examples and visualizations.
This article demonstrates how a machine‑learning enthusiast used ChatGPT to generate, modify, and refine Keras code for training, evaluating, visualizing, and deploying a neural‑network model that classifies handwritten digits from the classic MNIST dataset, showcasing the full development workflow.
Vivo’s frontend team built a self‑attention‑based machine‑learning model that predicts web‑page layout types (column, row, or absolute) from node dimensions and positions, solving parent‑child and sibling relationships for design‑to‑code conversion, achieving 99.4% accuracy using over 20 k labeled, crawled, and generated samples, while outlining further enhancements.
This article walks through implementing a basic multi‑layer neural network using only NumPy, covering terminology, network architecture, forward and backward propagation, activation functions, loss calculation, parameter updates with SGD, and compares the custom model with a Keras implementation.
This article explains the role of activation functions in deep learning, covering their definition, why they are needed, the main categories—including linear, binary step, and various non‑linear functions such as Sigmoid, TanH, ReLU, Leaky ReLU, ELU, Softmax and Swish—along with each function's mathematical form, advantages, disadvantages, and practical usage recommendations.
The article demonstrates how to build a digital twin for lithium‑ion batteries in Python by constructing a physics‑based model, augmenting it with experimental data using a simple Keras neural network, and visualizing predictions, illustrating the hybrid approach’s improved accuracy over purely empirical methods.
This article explains how to generate low‑quality audio samples from clean speech by randomly inserting noise at various SNR levels, compute objective PESQ scores as ground‑truth, and use these paired data to train a neural‑network model for reference‑free audio quality assessment.
NetEase Cloud Communication’s Audio Lab presents a low‑complexity neural‑network denoising algorithm that effectively suppresses both stationary and transient noises while preserving speech quality, detailing its mathematical model, feature design, loss function, GRU‑based architecture, real‑time performance, and comparative evaluation against state‑of‑the‑art methods.
This article describes how to integrate and extend the open‑source HanziWriter library for Android, iOS and web, extracting TrueType glyph data, segmenting strokes with corner detection and neural‑network scoring, generating stroke medians via Voronoi diagrams, and determining correct stroke order using a Hungarian‑algorithm match.
This guide explains the fundamentals of tensors in TensorFlow.js—including scalars, vectors, and higher‑dimensional tensors—demonstrates how to convert real‑world data such as the Titanic dataset into tensors, and shows how to build, compile, and train a simple neural network model using appropriate layers, loss functions, and optimizers.
By extracting hour‑level activity logs and training supervised models—including CART, GBDT, and neural networks—on user tags, the team simulated short‑term metrics for new reward campaigns, enabling earlier prediction of next‑day retention and shortening experiment cycles despite delayed T+1 data.
This article explains the backpropagation training algorithm for fully connected artificial neural networks, detailing its gradient‑descent basis, mathematical derivation, matrix formulation, and provides a complete Python implementation with mini‑batch stochastic gradient descent, momentum, learning‑rate decay, and experimental results.
The article details a contestant's end‑to‑end approach for an advertising competition, covering data analysis, rule‑based preprocessing, a three‑layer neural network architecture, model‑rule ensemble weighting, self‑correction strategies for the B phase, and final model‑only solutions that achieved top scores.
The article describes an intelligent cover‑image selection system for NetEase News that automatically filters unsuitable illustrations, assesses image quality with a pairwise‑trained deep model across clarity, color and composition, and smartly crops images using aspect‑ratio‑aware object detection, dramatically cutting manual editing and enabling confidence‑based automatic publishing.
This article walks through creating a Python‑based neural network to predict breast cancer using the Wisconsin dataset, covering network architecture, weight and bias initialization, back‑propagation, gradient descent, and the role of activation functions such as sigmoid, tanh, ReLU and Leaky ReLU.
Tencent’s industrial practice shows how a large‑scale offline‑nearline‑online “Shield” recommendation architecture, powered by the DeepR framework built on RCaffe, uses deep semantic embeddings, massive neural networks and reinforcement‑learning decisions to handle billions of daily requests, demonstrating that data richness and engineering capability, not model depth alone, drive performance in big‑data recommendation systems.
This tutorial explains neural network fundamentals by defining layers, weights, biases, and sigmoid activation, then walks through building a Python class that implements forward propagation, a sum‑of‑squared‑error loss, and backpropagation using the chain rule and gradient descent to train a simple two‑layer network.
