8 个仓库
Awesome GitHub RepositoriesSequential Learning
Methods for training models on time-series or ordered data sequences.
Distinguishing note: Focuses on temporal dependency capture in sequential data.
Explore 8 awesome GitHub repositories matching artificial intelligence & ml · Sequential Learning. Refine with filters or upvote what's useful.
Awesome Sequential Learning GitHub Repositories
用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
- eriklindernoren/ml-from-scratch
eriklindernoren/ML-From-Scratch
31,918在 GitHub 上查看This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models. The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base
Processes time-series data by using recurrent neural networks to capture temporal dependencies.
Pythondata-miningdata-sciencedeep-learning - d2l-ai/d2l-en
d2l-ai/d2l-en
29,001在 GitHub 上查看This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
Models temporal dependencies in input sequences by maintaining internal states through recurrent connections.
Pythonbookcomputer-visiondata-science - accumulatemore/cv
AccumulateMore/CV
21,907在 GitHub 上查看This project is a comprehensive deep learning framework and educational platform designed for constructing, training, and evaluating neural network architectures. It provides a modular environment for building models through tensor operations and automatic differentiation, supporting a wide range of tasks from image classification and object detection to sequential data processing. Beyond its core technical capabilities, the project distinguishes itself by integrating professional career development resources directly into its learning ecosystem. It offers structured guidance, resume reviews,
Implements recurrent neural networks and sequence models to analyze time-series data and capture temporal dependencies.
Jupyter Notebookagentagentsbook - fengdu78/deeplearning_ai_books
fengdu78/deeplearning_ai_books
20,250在 GitHub 上查看This repository serves as a comprehensive educational resource and study guide for mastering deep learning principles and neural network architectures. It provides a structured curriculum that covers the fundamental components of artificial intelligence, including backpropagation, optimization algorithms, and model performance tuning. The collection distinguishes itself by offering curated academic materials and practical implementation examples that bridge the gap between theoretical concepts and hands-on application. It includes specialized instructional guides for developing models capable
Provides instructional materials for developing models to interpret sequential data and time-series patterns.
HTMLdeeplearning-ai - lazyprogrammer/machine_learning_examples
lazyprogrammer/machine_learning_examples
8,823在 GitHub 上查看This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg
Provides methods for training models on sequential data using sliding window transformations for self-supervised learning.
Pythondata-sciencedeep-learningmachine-learning - rasbt/python-machine-learning-book-2nd-edition
rasbt/python-machine-learning-book-2nd-edition
7,194在 GitHub 上查看This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms. The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks. The content covers a broad range of capabili
Implements recurrent neural networks to process and model ordered sequences such as text.
Jupyter Notebookdata-sciencedeep-learningmachine-learning - haifengl/smile
haifengl/smile
6,387在 GitHub 上查看Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin
Provides methods for training models on ordered data sequences, including HMMs and CRFs for sequence labeling.
Java - nyandwi/machine_learning_complete
Nyandwi/machine_learning_complete
4,983在 GitHub 上查看This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi
Implements methods for training models on time-series or ordered data sequences to capture temporal dependencies.
Jupyter Notebookcomputer-visiondata-analysisdata-science
