Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Awesome-Machine-Learning
Learning tutorial for machine learning beginners
https://github.com/Billy1900/Awesome-Machine-Learning
- CS221: Artificial Intelligence: Principles and Techniques
- Machine Learning from Andrew Ng - parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
- CS229: Machine Learning - parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
- Deep Learning
- Neural Networks and Deep Learning
- Mathematics for Machine Learning
- Foundations of machine learning - level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
- Understanding Machine Learning: From Theory to Algorithms
- Probabilistic Machine Learning: An Introduction
- AI 算法工程师手册
- Convex Optimization I - convex goals, it helps to understand the form behind the problem of manageable optimizations.
- CS 228: Probabilistic Graphical Models - making under uncertainty.
- CS246: Mining Massive Data Sets
- Blog GNN-Learning
- pandas tutorial authorized by Datawhale - source standard for analytic apps in Python.
- numpy-ml - ml is a growing collection of machine learning models, algorithms, and tools written exclusively in NumPy and the Python standard library. The purpose of the project is to provide reference implementations of common machine learning components for rapid prototyping and experimentation. With that in mind, don’t just read the docs – read the source!
- Machine learning algorithms
- Awesome Machine Learning
- Surprise
- Recommenders
- Real-Time Voice Cloning - stage deep learning framework that allows to create a numerical representation of a voice from a few seconds of audio, and to use it to condition a text-to-speech model trained to generalize to new voices.
- DouZero_For_Happy_DouDiZhu
Programming Languages
Keywords
machine-learning
4
pytorch
2
deep-learning
2
neural-networks
2
python
2
tts
1
tensorflow
1
systems
1
svd
1
recommender
1
recommendation
1
matrix
1
factorization
1
machine-learning-algorithms
1
graph-neural-networks
1
graph-convolution
1
gnn-learning
1
gnn
1
word2vec
1
wgan-gp
1
wavenet
1
vae
1
topic-modeling
1
resnet
1
reinforcement-learning
1
mfcc
1
lstm
1
knn
1
hidden-markov-models
1
gradient-boosting
1
good-turing-smoothing
1
gaussian-processes
1
gaussian-mixture-models
1
bayesian-inference
1
attention
1
voice-cloning
1
batch-size
1