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Keras

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Python Programming Learning Circle
Python Programming Learning Circle
Jan 9, 2025 · Artificial Intelligence

Choosing Between Keras and PyTorch: A Guide for Deep Learning Beginners

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.

KerasPyTorchPython
0 likes · 6 min read
Choosing Between Keras and PyTorch: A Guide for Deep Learning Beginners
Test Development Learning Exchange
Test Development Learning Exchange
Nov 29, 2024 · Artificial Intelligence

Using LSTM Networks for Stock Price Time Series Prediction with Keras

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.

AIKerasLSTM
0 likes · 8 min read
Using LSTM Networks for Stock Price Time Series Prediction with Keras
Test Development Learning Exchange
Test Development Learning Exchange
Nov 28, 2024 · Artificial Intelligence

Introduction to Deep Learning with Keras: Building and Training a Simple Neural Network

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.

AIKerasPython
0 likes · 7 min read
Introduction to Deep Learning with Keras: Building and Training a Simple Neural Network
Sohu Tech Products
Sohu Tech Products
Mar 6, 2024 · Mobile Development

On‑Device Deployment of Large Language Models Using Sohu’s Hybrid AI Engine and GPT‑2

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.

GPT-2Hybrid AIKeras
0 likes · 9 min read
On‑Device Deployment of Large Language Models Using Sohu’s Hybrid AI Engine and GPT‑2
Model Perspective
Model Perspective
Aug 1, 2023 · Artificial Intelligence

Mastering LSTM: How Long Short-Term Memory Networks Capture Long-Term Dependencies

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.

KerasLSTMPython
0 likes · 9 min read
Mastering LSTM: How Long Short-Term Memory Networks Capture Long-Term Dependencies
Model Perspective
Model Perspective
Mar 2, 2023 · Artificial Intelligence

Understanding RNNs and LSTM: Theory and Python Keras Implementation

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.

KerasLSTMPython
0 likes · 7 min read
Understanding RNNs and LSTM: Theory and Python Keras Implementation
Sohu Tech Products
Sohu Tech Products
Feb 1, 2023 · Artificial Intelligence

ChatGPT Writes AI: Building an MNIST Classifier with Keras Using ChatGPT

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.

ChatGPTKerasMNIST
0 likes · 4 min read
ChatGPT Writes AI: Building an MNIST Classifier with Keras Using ChatGPT
Model Perspective
Model Perspective
Oct 24, 2022 · Artificial Intelligence

Essential Python Packages for Data Analysis, Statistics, and Machine Learning

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.

KerasPythondata analysis
0 likes · 4 min read
Essential Python Packages for Data Analysis, Statistics, and Machine Learning
Model Perspective
Model Perspective
Oct 10, 2022 · Artificial Intelligence

Predict Air Pollution with Multivariate LSTM in Keras: A Step‑by‑Step Guide

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.

KerasLSTMPython
0 likes · 13 min read
Predict Air Pollution with Multivariate LSTM in Keras: A Step‑by‑Step Guide
Model Perspective
Model Perspective
Aug 15, 2022 · Artificial Intelligence

Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras

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.

KerasLSTMRNN
0 likes · 5 min read
Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras
Model Perspective
Model Perspective
Aug 10, 2022 · Artificial Intelligence

Master CNN Basics: Build, Train, and Evaluate a Convolutional Neural Network

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.

CNNKerasPython
0 likes · 5 min read
Master CNN Basics: Build, Train, and Evaluate a Convolutional Neural Network
Model Perspective
Model Perspective
Aug 9, 2022 · Artificial Intelligence

Build a Regression MLP with Keras: Predict California Housing Prices

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.

California HousingKerasMLP
0 likes · 3 min read
Build a Regression MLP with Keras: Predict California Housing Prices
Model Perspective
Model Perspective
Aug 8, 2022 · Artificial Intelligence

Build a Multi‑Layer Perceptron with Keras: Step‑by‑Step Guide

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.

Fashion MNISTKerasMLP
0 likes · 16 min read
Build a Multi‑Layer Perceptron with Keras: Step‑by‑Step Guide
Python Programming Learning Circle
Python Programming Learning Circle
Jul 14, 2022 · Artificial Intelligence

End‑to‑End Time Series Forecasting with LSTM in Python

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.

KerasLSTMPython
0 likes · 14 min read
End‑to‑End Time Series Forecasting with LSTM in Python
DataFunTalk
DataFunTalk
Mar 31, 2022 · Artificial Intelligence

Comprehensive Guide to TensorFlow: Modeling, Deployment, and Operations

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.

AutoMLKerasModel Deployment
0 likes · 20 min read
Comprehensive Guide to TensorFlow: Modeling, Deployment, and Operations
Python Programming Learning Circle
Python Programming Learning Circle
Mar 19, 2022 · Artificial Intelligence

Building a Simple Digital Twin for Lithium‑Ion Batteries Using Python and Neural Networks

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.

Kerasdigital twinlithium-ion battery
0 likes · 9 min read
Building a Simple Digital Twin for Lithium‑Ion Batteries Using Python and Neural Networks
Python Programming Learning Circle
Python Programming Learning Circle
Mar 9, 2022 · Artificial Intelligence

Top Python Machine Learning Libraries in 2021 and Their Key Features

This article introduces the most important Python machine‑learning libraries of 2021—including TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano and Pandas—explaining their purposes, distinctive characteristics, and why they are essential tools for modern AI development.

KerasLightGBMPyTorch
0 likes · 12 min read
Top Python Machine Learning Libraries in 2021 and Their Key Features
Python Programming Learning Circle
Python Programming Learning Circle
Aug 24, 2021 · Artificial Intelligence

Top 10 Python Libraries for Machine Learning

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.

KerasLibrariesNumPy
0 likes · 15 min read
Top 10 Python Libraries for Machine Learning
Python Programming Learning Circle
Python Programming Learning Circle
Dec 9, 2020 · Artificial Intelligence

Introduction to Artificial Neural Networks and BP Neural Network Implementation with Keras and Scikit-learn

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.

BP Neural NetworkKerasactivation functions
0 likes · 6 min read
Introduction to Artificial Neural Networks and BP Neural Network Implementation with Keras and Scikit-learn
Python Programming Learning Circle
Python Programming Learning Circle
Jun 12, 2020 · Artificial Intelligence

Visualizing Convolutional Neural Networks: Methods and Practical Examples

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.

CNNKerasPython
0 likes · 13 min read
Visualizing Convolutional Neural Networks: Methods and Practical Examples