kasikasi2014,github

30-seconds-of-python

Short Python code snippets for all your development needs

I recently attended the Geekbang "Large Language Models Application Development Practice Camp", where I learned about the application development of Large Language Models. Below is a summary of the after-class exercises that I participated in the camp.

alphago

A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search," details of which can be found on their website.

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

apidoc

RESTful web API Documentation Generator.

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

badou-jingpingban

bayareadlschool

Slides and exercises for the Theano tutorial at the Deep Learning School in Stanford, September 24-25, 2016

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

bisheng

Bisheng is an open LLM devops platform for next generation AI applications.

caffe

Caffe: a fast open framework for deep learning.

cgt

Computation Graph Toolkit

coding

data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

deeplearning-models

A collection of various deep learning architectures, models, and tips

deeplearningtutorials

Deep Learning Tutorial notes and code. See the wiki for more info.

deepnlp-models-pytorch

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

docker-library

kubernetes 相关 images 同步

filterpy

Kalman filtering and optimal estimation library in Python. Kalman filter, Extended Kalman filter, Unscented Kalman filter, g-h, least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.