backpropagation

Cognitive Technology Team

Apr 12, 2025 · Artificial Intelligence

Analyzing a Trained Neural Network: Visualizing Hidden Layers and Understanding Its Limitations

This article walks through an interactive exploration of a simple two‑hidden‑layer neural network, showing how real‑time visualizations reveal its learned representations, accuracy limits, and why constrained training leads to over‑confident yet unintelligent predictions before introducing backpropagation.

backpropagationdeep learninghidden layers

0 likes · 10 min read

Analyzing a Trained Neural Network: Visualizing Hidden Layers and Understanding Its Limitations

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

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

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

Python Programming Learning Circle

Jul 23, 2021 · Artificial Intelligence

Understanding PyTorch's Backward Propagation Engine (BP Engine)

This article explains how PyTorch's BP Engine dynamically builds the computation graph for back‑propagation, detailing its C++ class structure, thread management, task queues, and key functions such as start_threads, compute_dependencies, execute, and evaluate_function, with illustrative code examples.

C++GraphTaskPyTorch

0 likes · 17 min read

Understanding PyTorch's Backward Propagation Engine (BP Engine)

DataFunTalk

Oct 20, 2020 · Artificial Intelligence

From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation

This article traces the evolution of artificial neural networks from their biological inspiration, explains the McCulloch‑Pitts neuron model, details perceptron architecture and learning rule with a Scikit‑Learn example, and introduces multilayer perceptrons and the back‑propagation algorithm together with common activation functions.

AIbackpropagationdeep learning

0 likes · 19 min read

From Biological Neurons to Artificial Neural Networks: Perceptrons, Multilayer Perceptrons, and Backpropagation

Python Programming Learning Circle

Apr 26, 2020 · Artificial Intelligence

Understanding PyTorch Autograd: Tensors, Gradients, and Backpropagation

This article explains PyTorch's autograd system, covering tensor creation, the requires_grad flag, detaching tensors, disabling gradient tracking with no_grad, the Function class and computational graph, and demonstrates forward and backward passes with code examples illustrating gradient computation and Jacobian‑vector products.

PyTorchautogradbackpropagation

0 likes · 6 min read

Understanding PyTorch Autograd: Tensors, Gradients, and Backpropagation

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

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

Tencent Cloud Developer

Nov 9, 2018 · Artificial Intelligence

Demystifying Neural Networks: A Mathematical Approach

The article explains how basic mathematical principles—starting with simple predictors and linear classifiers, then extending to multi‑classifier systems, activation functions, and weight adjustments—underpin neural network architecture, illustrating each step with concrete examples to show how mathematics drives AI model training and performance.

XOR problemactivation functionbackpropagation

0 likes · 15 min read

Demystifying Neural Networks: A Mathematical Approach

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.

activation functionbackpropagationdeep learning

0 likes · 8 min read

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

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.

Torchbackpropagationcode examples

0 likes · 11 min read

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

Ctrip Technology

Sep 10, 2016 · Artificial Intelligence

Deep Learning Anti‑Scam Guide: An Informal Introduction to Neural Networks, Training, and Practical Applications

This article provides a light‑hearted yet thorough overview of deep learning, covering neural network fundamentals, layer construction, back‑propagation, ResNet shortcuts, encoder‑decoder structures, PU‑learning for unlabeled data, GPU acceleration, and practical advice on data size, frameworks, and deployment in financial scenarios.

Big DataGPUPu-Learning

0 likes · 27 min read

Deep Learning Anti‑Scam Guide: An Informal Introduction to Neural Networks, Training, and Practical Applications