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gradient descent

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Cognitive Technology Team
Cognitive Technology Team
Apr 9, 2025 · Artificial Intelligence

How Neural Networks Learn: Gradient Descent and Loss Functions

This article explains how neural networks learn by using labeled training data, describing the role of weights, biases, activation functions, and how gradient descent iteratively adjusts parameters to minimize loss, illustrated with the MNIST digit‑recognition example.

MNISTdeep learninggradient descent
0 likes · 16 min read
How Neural Networks Learn: Gradient Descent and Loss Functions
IT Services Circle
IT Services Circle
Dec 31, 2024 · Artificial Intelligence

Understanding Linear Regression, Loss Functions, and Gradient Descent: A Conversational Guide

This article uses a dialogue format to introduce the fundamentals of linear regression, explain how loss functions such as mean squared error quantify prediction errors, and describe gradient descent as an iterative optimization technique for finding the best model parameters, illustrated with simple numeric examples and visual aids.

AI Basicsgradient descentlinear regression
0 likes · 13 min read
Understanding Linear Regression, Loss Functions, and Gradient Descent: A Conversational Guide
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
May 5, 2024 · Artificial Intelligence

Comprehensive Guide to Neural Network Algorithms: Definitions, Structure, Implementation, and Training

This article provides an in‑depth tutorial on neural network algorithms, covering their biological inspiration, significance, advantages and drawbacks, detailed architecture, data preparation, one‑hot encoding, weight initialization, forward and backward propagation, cost functions, regularization, gradient checking, and complete Python code examples.

AIbackpropagationgradient descent
0 likes · 37 min read
Comprehensive Guide to Neural Network Algorithms: Definitions, Structure, Implementation, and Training
DaTaobao Tech
DaTaobao Tech
Apr 22, 2024 · Artificial Intelligence

Neural Networks and Deep Learning: Principles and MNIST Example

The article reviews recent generative‑AI breakthroughs such as GPT‑5 and AI software engineers, explains that AI systems are deterministic rather than black boxes, and then teaches neural‑network fundamentals—including activation functions, back‑propagation, and a hands‑on MNIST digit‑recognition example with discussion of overfitting and regularization.

MNISTactivation functionsdeep learning
0 likes · 17 min read
Neural Networks and Deep Learning: Principles and MNIST Example
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Apr 7, 2024 · Artificial Intelligence

Logistic Regression: Definition, Purpose, Structure, Implementation, and Regularization

This article explains logistic regression as a classification algorithm, covering its definition, purpose, mathematical structure, data preparation, core functions such as sigmoid, cost, gradient descent, prediction, model evaluation, decision boundary visualization, feature mapping, and regularization techniques, all illustrated with Python code examples.

classificationgradient descentlogistic regression
0 likes · 33 min read
Logistic Regression: Definition, Purpose, Structure, Implementation, and Regularization
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Apr 5, 2024 · Artificial Intelligence

Linear Regression Algorithm: Definition, Structure, Implementation, Cost Function, Gradient Descent, and Regularization

This article provides a comprehensive overview of linear regression, covering its definition, purpose, algorithmic steps, data preparation, feature scaling, parameter initialization, cost function computation, gradient descent optimization, visualization, normal equation solution, and regularization, accompanied by detailed Python code examples.

NumPycost functiongradient descent
0 likes · 19 min read
Linear Regression Algorithm: Definition, Structure, Implementation, Cost Function, Gradient Descent, and Regularization
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jan 10, 2024 · Artificial Intelligence

Understanding Backpropagation: From Simple to Advanced Neural Network Implementations in Python

This article explains the back‑propagation algorithm in neural networks, starting with a simple single‑neuron example using ReLU, Sigmoid and MSE, then extending to multi‑layer matrix‑based networks, providing detailed Python code, gradient calculations, and comparisons with TensorFlow implementations.

backpropagationgradient descentmachine learning
0 likes · 21 min read
Understanding Backpropagation: From Simple to Advanced Neural Network Implementations in Python
Model Perspective
Model Perspective
Aug 30, 2023 · Artificial Intelligence

How Gradient Descent Trains Neural Networks: A Blind Hiker’s Journey

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.

AIbackpropagationgradient descent
0 likes · 10 min read
How Gradient Descent Trains Neural Networks: A Blind Hiker’s Journey
Model Perspective
Model Perspective
Mar 7, 2023 · Fundamentals

Mastering Nonlinear Programming with Python: Two Practical Optimization Cases

This article introduces nonlinear programming, explains its challenges compared to linear programming, and demonstrates two concrete optimization examples solved with Python's SciPy library using gradient‑descent and SLSQP algorithms, complete with code and result interpretation.

OptimizationSciPygradient descent
0 likes · 5 min read
Mastering Nonlinear Programming with Python: Two Practical Optimization Cases
Model Perspective
Model Perspective
Oct 7, 2022 · Artificial Intelligence

Master Gradient Descent: From Intuition to Advanced Variants

This comprehensive guide explains the mathematical foundation, intuitive intuition, algorithmic steps, tuning strategies, and variants of gradient descent, comparing it with other optimization methods and illustrating its use in machine‑learning models such as linear regression.

AlgorithmOptimizationgradient descent
0 likes · 14 min read
Master Gradient Descent: From Intuition to Advanced Variants
Model Perspective
Model Perspective
Aug 4, 2022 · Artificial Intelligence

How Supervised Learning Predicts House Prices – A Hands‑On Guide

Using a real‑world housing example, this article explains supervised and unsupervised learning, walks through building a price‑prediction function, introduces gradient descent for optimizing weights, and highlights pitfalls like overfitting, offering a practical introduction to core machine‑learning concepts.

gradient descentlinear regressionmachine learning
0 likes · 13 min read
How Supervised Learning Predicts House Prices – A Hands‑On Guide
Model Perspective
Model Perspective
Jul 6, 2022 · Fundamentals

Mastering Gradient Descent: A Practical Guide to Numerical Optimization

This article explains why algebraic methods often fail to locate critical points in real‑world problems and introduces the steepest descent (gradient descent) algorithm as a simple iterative numerical technique, detailing its initialization, update rules, step‑size considerations, and a concrete example.

critical pointsgradient descentnumerical optimization
0 likes · 3 min read
Mastering Gradient Descent: A Practical Guide to Numerical Optimization
Python Programming Learning Circle
Python Programming Learning Circle
Jun 2, 2021 · Artificial Intelligence

Implementing Linear Regression from Scratch in Python

This tutorial walks through the complete process of building a linear regression model in Python from loading a housing price dataset, normalizing features, defining hypothesis, cost and gradient‑descent functions, visualising data and cost convergence, and testing predictions, with full source code provided.

data preprocessinggradient descentlinear regression
0 likes · 12 min read
Implementing Linear Regression from Scratch in Python
Python Programming Learning Circle
Python Programming Learning Circle
Dec 14, 2020 · Artificial Intelligence

Understanding Gradient Descent: Concept and Implementation

This article explains the intuition behind gradient descent, describing how the cost function surface is visualized, how the learning rate controls step size, and why updating parameters in the correct order is essential for converging to the optimal solution.

Optimizationcost functiongradient descent
0 likes · 4 min read
Understanding Gradient Descent: Concept and Implementation
Python Programming Learning Circle
Python Programming Learning Circle
Mar 26, 2020 · Artificial Intelligence

Understanding Gradient Descent for Linear Regression with a Python Implementation

This article explains the concept of loss functions and gradient descent, illustrates how to find the global optimum for linear regression, discusses the role of learning rate, and provides a complete Python example that generates data, applies gradient descent, and visualizes the results.

Optimizationgradient descentlinear regression
0 likes · 6 min read
Understanding Gradient Descent for Linear Regression with a Python Implementation
Python Programming Learning Circle
Python Programming Learning Circle
Feb 8, 2020 · Artificial Intelligence

Neural Network Construction Example with Python Implementation

This article presents a comprehensive tutorial on building and training a multi‑layer neural network in Python, covering data preprocessing, model architecture definition, parameter initialization, forward and backward propagation, cost computation, and parameter updates with code examples for activation functions and optimization techniques.

backpropagationdeep learninggradient descent
0 likes · 13 min read
Neural Network Construction Example with Python Implementation
360 Tech Engineering
360 Tech Engineering
Sep 16, 2019 · Artificial Intelligence

Backpropagation Algorithm for Fully Connected Neural Networks with Python Implementation

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.

SGDbackpropagationgradient descent
0 likes · 14 min read
Backpropagation Algorithm for Fully Connected Neural Networks with Python Implementation
360 Tech Engineering
360 Tech Engineering
Aug 28, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP

This article explains how Huajiao Live leverages Spark for data preprocessing and TensorFlow (augmented by the TensorSlow project) for distributed deep‑learning training, detailing computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and a step‑by‑step Python implementation of a multi‑layer perceptron.

MLPTensorFlowcomputational graph
0 likes · 14 min read
Understanding TensorFlow Internals with TensorSlow: Computational Graph, Forward/Backward Propagation, and Building an MLP
360 Tech Engineering
360 Tech Engineering
Jul 2, 2019 · Artificial Intelligence

Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide

This article explains how TensorFlow powers Huajiao Live's recommendation system, introduces the TensorSlow project for demystifying TensorFlow's core, and walks through deep‑learning fundamentals, computational‑graph concepts, forward and backward propagation, loss construction, gradient‑descent optimization, and building a multi‑layer perceptron with Python code examples.

MLPTensorFlowcomputational graph
0 likes · 13 min read
Understanding TensorFlow Internals with TensorSlow: A Deep Learning Guide
Huajiao Technology
Huajiao Technology
Jul 2, 2019 · Artificial Intelligence

Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation

This article introduces deep learning applications at Huajiao Live, explains TensorFlow's computational graph architecture, details core concepts such as placeholders, variables, operations, forward and backward propagation, and provides complete Python-like code examples for building and training a multi-layer perceptron.

MLPTensorFlowcomputational graph
0 likes · 14 min read
Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation