| donnemartin/data-science-ipython-notebooks |
25,668 |
|
0 |
0 |
over 2 years ago |
0 |
|
34 |
other |
Python |
| Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. |
| horovod/horovod |
13,755 |
|
20 |
16 |
about 2 years ago |
77 |
June 12, 2023 |
372 |
other |
Python |
| Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. |
| XiangLinPro/IT_book |
8,543 |
|
0 |
0 |
over 4 years ago |
0 |
|
7 |
|
|
| 本项目收藏这些年来看过或者听过的一些不错的常用的上千本书籍，没准你想找的书就在这里呢，包含了互联网行业大多数书籍和面试经验题目等等。有人工智能系列（常用深度学习框架TensorFlow、pytorch、keras。NLP、机器学习，深度学习等等），大数据系列(Spark,Hadoop,Scala,kafka等)，程序员必修系列（C、C++、java、数据结构、linux，设计模式、数据库等等） |
| Alluxio/alluxio |
6,544 |
|
31 |
53 |
about 2 years ago |
73 |
November 29, 2023 |
969 |
apache-2.0 |
Java |
| Alluxio, data orchestration for analytics and machine learning in the cloud |
| intel-analytics/BigDL |
4,728 |
|
0 |
10 |
about 2 years ago |
16 |
April 19, 2021 |
958 |
apache-2.0 |
Jupyter Notebook |
| Accelerate LLM with low-bit (FP4 / INT4 / FP8 / INT8) optimizations using bigdl-llm |
| PipelineAI/pipeline |
4,158 |
|
0 |
0 |
over 3 years ago |
85 |
July 18, 2017 |
1 |
apache-2.0 |
Jsonnet |
| PipelineAI |
| yahoo/TensorFlowOnSpark |
3,851 |
|
5 |
0 |
almost 3 years ago |
32 |
April 21, 2022 |
13 |
apache-2.0 |
Python |
| TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters. |
| JohnSnowLabs/spark-nlp |
3,578 |
|
0 |
30 |
about 2 years ago |
134 |
December 08, 2023 |
43 |
apache-2.0 |
Scala |
| State of the Art Natural Language Processing |
| intel-analytics/analytics-zoo |
2,592 |
|
0 |
3 |
over 2 years ago |
1 |
July 29, 2022 |
533 |
apache-2.0 |
Jupyter Notebook |
| Distributed Tensorflow, Keras and PyTorch on Apache Spark/Flink & Ray |
| uber/petastorm |
1,693 |
|
0 |
8 |
over 2 years ago |
86 |
February 03, 2023 |
174 |
apache-2.0 |
Python |
| Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. |
