Intersting links about deep learning
some useful & interesting links about neural networks
general
- http://jalammar.github.io/
 - https://lilianweng.github.io/lil-log/
 - http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
 - fast.ai updated for 2020 an V2
 - fastai book
 - dive into deep learning
 - Yes you should understand backprop
 - CS 329S: Machine Learning Systems Design
 
critique
- Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
 - A Recipe for Training Neural Networks
 
generic ML useful tools
constraints
graph neural networks
- https://medium.com/dair-ai/an-illustrated-guide-to-graph-neural-networks-d5564a551783
 - [node classification with pytorch geometric] (https://colab.research.google.com/drive/14OvFnAXggxB8vM4e8vSURUp1TaKnovzX?usp=sharing#scrollTo=dszt2RUHE7lW)
 
frameworks
graphs
time-series
- sktime and sktime-dl
 - pytorch-forecasting
 - A Multivariate Deep Learning for Time Series Forecasting Library
 - tsai state of the art sequence modeling
 - GluonTS - Probabilistic Time Series Modeling in Python
 - Awesome python time-series tooling not so much neural networks. But still very useful feature generators.
 - ROCKET transforms time series using random convolutional kernels
 - Orion - multivariate time-series anomaly detection
 - How to prepare Time Series Data for LSTM Networks
 - neural prophet
 
Varous time-series handling approaches:
- transformer https://www.youtube.com/watch?v=GiD87DeadYY
 - 1D CNN, convert to images, convert to text https://www.youtube.com/watch?v=WoLlZLdoEQk
 - Attention & time-series review https://towardsdatascience.com/attention-for-time-series-classification-and-forecasting-261723e0006d
 - transformer:
 - classificaton:
 
self supervised learing
anomaly detection
- Deep Few-shot Anomaly Detection https://towardsdatascience.com/deep-few-shot-anomaly-detection-b33f130d1f80