This article explains how deep learning models such as RNN, LSTM, GRU and Transformer enable NLP tasks like machine translation, text classification, question answering and text generation, outlines their advantages over traditional methods, and provides a Keras code example for text classification.
This article walks through the essential steps of turning raw natural language into machine‑readable numbers, covering categorical vs. numerical features, one‑hot encoding of categorical data, tokenization, building vocabularies, and using word embeddings, illustrated with an IMDB sentiment‑analysis example in Keras.
This article explains how to train a simple fully‑connected autoencoder on defect‑free images, use reconstruction error to highlight anomalies in industrial parts, and convert the error into a single metric that cleanly separates good from defective components.
This article explains the challenges of running large language models on mobile devices, reviews recent industry efforts, and provides a step‑by‑step guide—including code snippets—for integrating a distilled GPT‑2 model with Sohu's Hybrid AI Engine using TensorFlow Lite and Keras‑NLP for on‑device inference.
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 article walks through practical Keras tutorials for image‑based traffic‑sign classification and text‑based sentiment analysis, covering data preparation, preprocessing, model construction, training, evaluation, deployment, and a concise comparison of Keras with TensorFlow and PyTorch.
This article compares Keras and PyTorch for beginners, explaining their differences, showing simple digit‑recognition code examples, and offering practical advice on how to select and transition between the two deep‑learning frameworks.
This tutorial demonstrates how to apply an LSTM deep‑learning model in Python to forecast stock closing prices, covering data acquisition, preprocessing, model construction, training, evaluation, and visualization of results for time‑series prediction.
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.
The article outlines how Sohu’s Hybrid AI Engine enables on‑device deployment of a distilled GPT‑2 model by converting it to TensorFlow Lite, detailing the setup, customization with Keras, inference workflow, and core SDK calls, and argues that this approach offers fast, private, and cost‑effective AI for mobile devices despite typical LLM constraints.
This article explains the challenges of processing sequential data, introduces LSTM as a solution to long‑term dependency problems in RNNs, details its cell state and gate mechanisms, showcases its architecture, and provides Python code examples for time‑series forecasting using Keras.
This article explains the fundamentals of Recurrent Neural Networks and Long Short‑Term Memory units, their gating mechanisms, and demonstrates a practical Python Keras example that predicts future PM2.5 concentrations using an LSTM model.
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.
This article introduces key Python libraries—including NumPy, Pandas, Matplotlib, Statsmodels, Scikit‑Learn, and Keras—detailing their core functionalities for data handling, statistical modeling, and machine‑learning tasks, and provides concise usage insights for each package.
This tutorial explains how to build, train, and evaluate a multivariate LSTM model using Keras for hourly air‑pollution forecasting, covering data preparation, model design, prediction, and inverse scaling back to original units.
This article introduces recurrent neural networks (RNNs) and their ability to handle sequential data, explains the limitations of vanilla RNNs, presents the LSTM architecture with its gates, and provides complete Keras code for data loading, model building, and training both vanilla RNN and LSTM models.
This article introduces the fundamentals of convolutional neural networks (CNN), explains key layers such as convolution, pooling, and fully connected layers, and provides a step‑by‑step Python implementation using Keras to load data, construct, compile, train, and evaluate a CNN model on the digits dataset.
Learn how to load the California housing dataset, preprocess features, construct a Keras sequential regression MLP, train it with SGD, evaluate performance, and make predictions, all illustrated with concise Python code snippets.
This tutorial walks through using Keras to create, compile, train, and evaluate a multi‑layer perceptron for image classification on the Fashion MNIST dataset, covering data loading, model construction with the Sequential API, hyperparameter choices, and prediction of new samples.
This tutorial walks through loading Google stock data, preprocessing it with scaling, constructing past‑window features, building and tuning an LSTM model using GridSearchCV, evaluating predictions, and finally forecasting future values, all illustrated with complete Python code.
This article explains how to train a convolutional neural network on a remote GPU for image classification, covering convolution, padding, activation, pooling, dropout, flattening, fully‑connected layers, dataset preparation, model definition, training, and prediction using TensorFlow/Keras.
This article provides an in‑depth overview of the TensorFlow ecosystem, covering Keras modeling productivity tools, classic model examples, AutoKeras and KerasTuner for automated search, data preprocessing pipelines, performance profiling, model optimization, and multiple deployment strategies for servers, browsers, and edge devices.
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 walks through building an image‑captioning system using a TensorFlow 2.0 encoder‑decoder with Bahdanau attention, covering dataset preparation, feature extraction with InceptionV3, model architecture, training with teacher forcing, and inference on the Flickr8K dataset.
This article presents an automated neural‑network framework based on a deep Siamese architecture to learn similarity representations between ECG recordings, covering ECG fundamentals, exploratory data analysis, signal preprocessing, model construction with Keras, and demonstrates how the trained network yields similarity scores applicable to broader signal‑matching tasks.
This article explains four Keras utilities—text_to_word_sequence, hashing_trick, one_hot, and Tokenizer—showing how each converts raw text into token lists, hash indices, integer encodings, or document matrices, with code examples and sample outputs.
This article explains how optimizers and learning‑rate schedulers work, how to configure their hyperparameters and parameter groups, and how to apply differential learning rates and adaptive schedules in PyTorch and Keras to improve model training and transfer‑learning performance.
An overview of ten widely used Python machine‑learning libraries—including TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano, and Pandas—detailing their core features, typical applications, and why they are essential tools for data scientists and AI developers.
This article explains what hook functions are, how they work as programmable insertion points in a program flow, provides a simple Python implementation example, and demonstrates their use in popular deep‑learning frameworks such as Keras and mmdetection.
This article introduces artificial neural networks, explains various activation functions, describes common ANN models such as BP, RBF, FNN and LM, and provides step‑by‑step implementation of BP neural networks for classification and regression using Keras Sequential and scikit‑learn’s MLPClassifier/MLPRegressor.
This step‑by‑step tutorial shows developers how to use Huawei Cloud ModelArts to set up a notebook environment, install TensorFlow and Keras, load pre‑trained poetry models, and generate AI‑crafted love poems for the Qixi festival, complete with all required configurations and commands.
This article explains why visualizing CNN models is crucial for understanding and debugging deep learning systems, outlines three main visualization approaches—basic architecture, activation‑based, and gradient‑based methods—and provides step‑by‑step Keras code examples, including model summary, filter visualization, occlusion mapping, saliency maps, and class activation maps.
This tutorial shows how to collect Shiba Inu and Akita images, train a Keras‑based CNN model on TensorFlow, and deploy it as a serverless inference service using Alibaba Cloud Function Compute and the Funcraft tool, turning a simple experiment into a functional breed‑identification web app.
This guide explains how Keras ImageDataGenerator performs on‑the‑fly image augmentation—covering rotation, shifts, brightness, shear, zoom, channel shifts, flips, and fill‑mode options—with concise Python code examples and visual results to help prevent overfitting in deep‑learning models.
This article introduces ten essential Python libraries—TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano, and Pandas—detailing their features, typical use cases, and adoption in machine‑learning and data‑science projects, while highlighting each library's performance advantages, community support, and integration capabilities to help developers choose the right tool for their AI workflows.
This tutorial walks readers through the fundamentals of artificial neural networks, perceptrons, logistic regression, activation and loss functions, gradient descent, and provides end‑to‑end Python implementations using NumPy, TensorFlow, and Keras to build and evaluate a cat‑vs‑non‑cat classifier, complete with code snippets, visual explanations, and performance analysis.
The article details a practical workflow for Chinese sentiment classification in Tencent’s Goose Man product, covering data preparation, word‑segmentation challenges, a six‑layer multi‑LSTM architecture with word embeddings, training results achieving roughly 96 % accuracy, and its deployment for automatic detection of misleading and high‑impact user reviews.
FireEye is an AI‑driven automated UI testing framework that ingests simulated and real screenshots, preprocesses images and OCR text, and employs a CNN for page anomalies, an SSD detector for control anomalies, and an LSTM‑based classifier for text anomalies, with Jenkins‑triggered retraining, cloud model storage, and API serving, aiming to simplify testing and enable future AutoML enhancements.
The article introduces convolutional neural networks, explains core concepts such as convolution, padding, stride, and pooling, demonstrates how to calculate output dimensions, and provides a step‑by‑step Keras example that builds, compiles, and trains a multi‑layer CNN for image classification.
This report details the analysis of a JD security CTF challenge that uses a Keras deep‑learning model to detect function entry points in binary code, describing the environment, manual data processing, model inspection, training adjustments, and optimization steps to achieve correct detection.
This article introduces Python’s essential features for data analysis and mining, then reviews the most widely used libraries—NumPy, SciPy, Matplotlib, Pandas, Scikit‑Learn, Keras, and Gensim—each accompanied by concise code examples that demonstrate their core capabilities.
In this interview, Microsoft chief data scientist Xie Liang shares how his economics background led him to machine learning, describes practical AI applications in Azure cloud services, discusses challenges and advantages for economists entering the field, and outlines his upcoming Keras‑focused talk and book.
This article surveys recent breakthroughs in deep learning for computer vision, explains what constitutes an advanced architecture, outlines common vision tasks, and provides concise overviews plus paper and Keras implementation links for ten influential models such as AlexNet, VGG, ResNet, and GAN.
This article surveys recent progress in deep learning by presenting key computer‑vision architectures such as AlexNet, VGG, GoogleNet, ResNet, ResNeXt, RCNN, YOLO, SqueezeNet, SegNet and GANs, providing brief descriptions, their advantages, and links to original papers and Keras implementations.
This article presents a deep‑learning approach for recognizing seven basic human facial expressions using a balanced FER2013 dataset, describes the CNN architecture built with Keras and OpenCV preprocessing, reports training on AWS GPU, and analyzes validation results and visualizations.
