4 repositorios
Awesome GitHub RepositoriesGradient Checking
The use of numerical approximation to verify the correctness of backpropagation derivatives.
Distinct from Backpropagation: Focuses specifically on the verification of the gradient implementation rather than the gradient descent algorithm itself.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Gradient Checking. Refine with filters or upvote what's useful.
Awesome Gradient Checking GitHub Repositories
Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
- rasbt/python-machine-learning-book
rasbt/python-machine-learning-book
12,614Ver en GitHubThis project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ
Implements gradient checking using numerical approximations to verify the correctness of backpropagation derivatives.
Jupyter Notebook - lawlite19/machinelearning_python
lawlite19/MachineLearning_Python
8,526Ver en GitHubThis is a Python machine learning library featuring a collection of core algorithms implemented from scratch to demonstrate foundational AI concepts. It provides a comprehensive toolkit for supervised learning, unsupervised learning, and neural network development. The project is distinguished by its custom implementation of a neural network framework, which includes multi-layer perceptrons with backpropagation, gradient descent, and weight regularization. It also includes a specialized anomaly detection toolkit that identifies outliers and rare events using Gaussian probability distributions
Validates backpropagation gradients by comparing analytic results against numerical approximations.
Python - afshinea/stanford-cs-230-deep-learning
afshinea/stanford-cs-230-deep-learning
7,028Ver en GitHubThis repository collects illustrated single-page cheat sheets that compress the core topics of Stanford's CS 230 deep learning course into visual reference summaries. The collection covers convolutional neural networks, recurrent neural networks, and practical training techniques, pairing schematic diagrams with mathematical notation to bridge intuition and formal understanding. The cheat sheets are organized by subject area and link related concepts across topics, such as connecting vanishing gradients to LSTM gates, to reinforce the full deep learning workflow. Practical training advice on
Covers gradient checking to verify backpropagation correctness in the training guide.
cheatsheetconvolutional-neural-networksdata-science - ashishpatel26/andrew-ng-notes
ashishpatel26/Andrew-NG-Notes
3,594Ver en GitHubThis project is a collection of structured study notes and notebooks serving as an educational resource for deep learning and neural network fundamentals. It provides a technical reference for implementing machine learning theory, covering everything from basic network design to the construction of advanced architectures. The material specifically focuses on the implementation of convolutional neural networks for computer vision and sequence models for natural language processing. It includes detailed guidance on building object detection systems, face recognition, and speech transcription mo
Implements numerical gradient checking to detect bugs in manual backpropagation implementations.
Jupyter Notebookandrew-ngandrew-ng-courseandrew-ng-machine-learning
