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Tencent Cloud Developer
Tencent Cloud Developer
Nov 4, 2025 · Artificial Intelligence

From Functions to Transformers: Mastering Neural Networks Step by Step

This article walks you through the evolution from basic mathematical functions to modern large‑scale models, explaining activation functions, forward and backward propagation, loss calculation, gradient descent, regularization, dropout, word embeddings, RNNs, and the core mechanics of the Transformer architecture.

Attention MechanismDeep LearningNeural Networks
0 likes · 15 min read
From Functions to Transformers: Mastering Neural Networks Step by Step
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.

Deep LearningMNISTNeural Networks
0 likes · 16 min read
How Neural Networks Learn: Gradient Descent and Loss Functions
AI Code to Success
AI Code to Success
Feb 24, 2025 · Artificial Intelligence

Master Linear Regression: Concepts, Math, and Python Implementation

This comprehensive guide explores linear regression from its fundamental concepts and mathematical foundations to practical Python implementation with scikit‑learn, covering single‑ and multiple‑variable models, assumptions, loss functions, OLS and gradient‑descent solutions, evaluation metrics, advantages, limitations, and real‑world case studies.

Model EvaluationPythongradient descent
0 likes · 21 min read
Master Linear Regression: Concepts, Math, and Python Implementation
AI Cyberspace
AI Cyberspace
Jan 28, 2025 · Artificial Intelligence

From Biological Neurons to Deep Learning: How MP Models Evolve

This article explains the structure of biological neurons, introduces the McCulloch‑Pitts (MP) mathematical model, shows how manual weight adjustments work, and walks through the development from single‑layer perceptrons to two‑layer networks and modern deep learning techniques, covering activation functions, training algorithms, and practical examples.

BackpropagationDeep LearningMP model
0 likes · 30 min read
From Biological Neurons to Deep Learning: How MP Models Evolve
AI Large Model Application Practice
AI Large Model Application Practice
Jan 9, 2025 · Artificial Intelligence

How Does Gradient Descent Train a Neural Network? A Step‑by‑Step Guide

This article walks through the complete training cycle of a simple neural network—from random weight initialization and forward propagation with labeled data, through loss calculation and gradient‑based weight updates, to iterative epochs, average loss, and practical issues like gradient explosion and vanishing.

AIModel TrainingNeural Networks
0 likes · 11 min read
How Does Gradient Descent Train a Neural Network? A Step‑by‑Step Guide
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
Ops Development & AI Practice
Ops Development & AI Practice
Jul 6, 2024 · Artificial Intelligence

How Backpropagation Powers Modern Deep Learning: A Deep Dive

This article explains the backpropagation algorithm—its origins, mathematical basis, step‑by‑step workflow, importance for efficient neural network training, and widespread applications in image recognition, natural language processing, and recommendation systems.

BackpropagationDeep LearningNeural Networks
0 likes · 6 min read
How Backpropagation Powers Modern Deep Learning: A Deep Dive
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.

AIBackpropagationNeural Networks
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.

Deep LearningMNISTNeural Networks
0 likes · 17 min read
Neural Networks and Deep Learning: Principles and MNIST Example
Liangxu Linux
Liangxu Linux
Mar 23, 2024 · Artificial Intelligence

Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks

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.

AI basicsNeural Networkactivation function
0 likes · 9 min read
Understanding AI Neurons: A Storytelling Guide to Basics of Neural Networks
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.

BackpropagationPythongradient descent
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.

AIBackpropagationNeural Network
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.

gradient descentnonlinear programmingoptimization
0 likes · 5 min read
Mastering Nonlinear Programming with Python: Two Practical Optimization Cases
MaGe Linux Operations
MaGe Linux Operations
Nov 26, 2022 · Artificial Intelligence

The Timeless Foundations of Machine Learning: 6 Core Algorithms Explained

Andrew Ng’s latest AI newsletter article revisits six foundational machine‑learning algorithms—linear regression, logistic regression, gradient descent, neural networks, decision trees, and k‑means clustering—tracing their historical origins, core concepts, and lasting impact on modern AI applications.

Decision TreesNeural Networksgradient descent
0 likes · 20 min read
The Timeless Foundations of Machine Learning: 6 Core Algorithms Explained
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.

gradient descentlearning ratelinear regression
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.

Pythongradient descentlinear regression
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
Code DAO
Code DAO
Dec 6, 2021 · Artificial Intelligence

Why So Many Optimizers? Core Algorithms Behind Neural Network Training

This article explains the fundamental gradient‑descent optimizers used in neural networks—SGD, Momentum, RMSProp, Adam and their variants—illustrates loss‑surface challenges such as local minima, saddle points and ravines, and shows how techniques like mini‑batching, momentum, adaptive learning rates and scheduling address these issues.

AdamDeep LearningMomentum
0 likes · 11 min read
Why So Many Optimizers? Core Algorithms Behind Neural Network Training
Code DAO
Code DAO
Dec 5, 2021 · Artificial Intelligence

Why Neural Networks Need Batch Normalization: Principles and Mechanics

The article explains the principle behind Batch Normalization, why it is essential for training deep neural networks, how it standardizes activations, the role of learnable scale and shift parameters, the computation steps during training and inference, and discusses placement strategies within a model.

Batch NormalizationDeep LearningNeural Networks
0 likes · 9 min read
Why Neural Networks Need Batch Normalization: Principles and Mechanics
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.

Pythongradient descentlinear regression
0 likes · 12 min read
Implementing Linear Regression from Scratch in Python
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.

Pythongradient descentlinear regression
0 likes · 6 min read
Understanding Gradient Descent for Linear Regression with a 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.

BackpropagationMini-BatchNeural Network
0 likes · 14 min read
Backpropagation Algorithm for Fully Connected Neural Networks with Python Implementation
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.

Computational GraphDeep LearningMLP
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.

Computational GraphDeep LearningMLP
0 likes · 14 min read
Understanding Deep Learning with TensorFlow: Applications, Computational Graphs, and MLP Implementation
MaGe Linux Operations
MaGe Linux Operations
Apr 15, 2019 · Artificial Intelligence

How to Build a Breast Cancer Prediction Neural Network from Scratch in Python

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.

Deep LearningNeural NetworkPython
0 likes · 13 min read
How to Build a Breast Cancer Prediction Neural Network from Scratch in Python
Tencent Cloud Developer
Tencent Cloud Developer
Oct 15, 2018 · Artificial Intelligence

Neural Network Fundamentals: Building Your Own Neural Network from Scratch in Python

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.

BackpropagationNeural NetworkPython
0 likes · 8 min read
Neural Network Fundamentals: Building Your Own Neural Network from Scratch in Python
Bitu Technology
Bitu Technology
Jul 19, 2018 · Artificial Intelligence

Introduction to Deep Learning: Concepts, Examples, and Learning Resources

This article provides a comprehensive overview of deep learning, covering its definition, fundamental machine‑learning components, illustrative examples such as hot‑dog classification and house‑price prediction, the mathematics of cost functions and gradient descent, back‑propagation via the chain rule, and practical resources and code snippets using Torch.

BackpropagationCode ExamplesNeural Networks
0 likes · 11 min read
Introduction to Deep Learning: Concepts, Examples, and Learning Resources
Hulu Beijing
Hulu Beijing
Jan 2, 2018 · Fundamentals

Master Classic Optimization Algorithms: Direct vs Iterative Methods Explained

This article introduces classic optimization algorithms, distinguishing direct methods that require convexity and closed‑form solutions from iterative first‑ and second‑order methods, and explains their applicability, underlying theory, and key references for solving smooth unconstrained problems.

Newton's methodalgorithm fundamentalsconvex optimization
0 likes · 8 min read
Master Classic Optimization Algorithms: Direct vs Iterative Methods Explained