All Projects → jakevdp → 2013_fall_astr599

jakevdp / 2013_fall_astr599

Licence: apache-2.0
Content for my Astronomy 599 Course: Intro to scientific computing in Python

Projects that are alternatives of or similar to 2013 fall astr599

Chinese Chatbot
中文聊天机器人,基于10万组对白训练而成,采用注意力机制,对一般问题都会生成一个有意义的答复。已上传模型,可直接运行,跑不起来直播吃键盘。
Stars: ✭ 124 (-3.12%)
Mutual labels:  jupyter-notebook
Thepythonmegacourse
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Griffon Vm
Griffon Data Science Virtual Machine
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Rasa Ptbr Boilerplate
Um template para criar um FAQ chatbot usando Rasa, Rocket.chat, elastic search
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Cuxfilter
GPU accelerated cross filtering with cuDF.
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Ccm Site
NYU PSYCH-GA 3405.002 / DS-GS 3001.006 : Computational cognitive modeling
Stars: ✭ 127 (-0.78%)
Mutual labels:  jupyter-notebook
Focal Loss Pytorch
全中文注释.(The loss function of retinanet based on pytorch).(You can use it on one-stage detection task or classifical task, to solve data imbalance influence).用于one-stage目标检测算法,提升检测效果.你也可以在分类任务中使用该损失函数,解决数据不平衡问题.
Stars: ✭ 126 (-1.56%)
Mutual labels:  jupyter-notebook
Pydata Chicago2016 Ml Tutorial
Machine learning with scikit-learn tutorial at PyData Chicago 2016
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Ipyexperiments
jupyter/ipython experiment containers for GPU and general RAM re-use
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Abstractive Summarization
Implementation of abstractive summarization using LSTM in the encoder-decoder architecture with local attention.
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Insightface Just Works
Insightface face detection and recognition model that just works out of the box.
Stars: ✭ 127 (-0.78%)
Mutual labels:  jupyter-notebook
Ncnet
PyTorch code for Neighbourhood Consensus Networks
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Byu econ applied machine learning
The course work for the applied machine learning course I am teaching at BYU
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Doodlenet
A doodle classifier(CNN), trained on all 345 categories from Quickdraw dataset.
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Gumbel
Gumbel-Softmax Variational Autoencoder with Keras
Stars: ✭ 127 (-0.78%)
Mutual labels:  jupyter-notebook
Robust Detection Benchmark
Code, data and benchmark from the paper "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" (NeurIPS 2019 ML4AD)
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Slicerjupyter
Extension for 3D Slicer that allows the application to be used from Jupyter notebook
Stars: ✭ 127 (-0.78%)
Mutual labels:  jupyter-notebook
Cmaps
user defined colormaps in matplotlib.
Stars: ✭ 126 (-1.56%)
Mutual labels:  jupyter-notebook
Pyvi
Python Vietnamese Core NLP Toolkit
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook
Rsis
Recurrent Neural Networks for Semantic Instance Segmentation
Stars: ✭ 128 (+0%)
Mutual labels:  jupyter-notebook

Astronomy 599: Introduction to Scientific Computing in Python

Jake Vanderplas

University of Washington

Fall, 2013

This repository will contain all the curriculum and materials for the Astronomy 599 seminar, Fall 2013.

See the Course Website for more information.

Instructor

Jake Vanderplas is an NSF post-doctoral fellow at UW working jointly between the Computer Science department's Database Research Group and the Astronomy department's Survey Science Group. He received his PhD in Astronomy from the University of Washington in 2012. He is active in the open-source Scientific Python community, and actively contributes to and helps maintain several of the core packages in the SciPy ecosystem.

Class Location

PAB 356 / 356A

Meeting times

  • Two-day “Boot Camp”: September 19-20 / 23-24 9:00am-4:00pm (dates TBD)
  • Weekly meetings: Mondays, 3:00-4:00pm throughout Fall quarter
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].