Linking pages
- GitHub - guillaume-chevalier/How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks: Growing the code out of your notebooks - the right way. https://github.com/guillaume-chevalier/How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks 101 comments
- GitHub - jtoy/awesome-tensorflow: TensorFlow - A curated list of dedicated resources http://tensorflow.org https://github.com/jtoy/awesome-tensorflow 5 comments
- GitHub - guillaume-chevalier/Awesome-Deep-Learning-Resources: Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier https://github.com/guillaume-chevalier/awesome-deep-learning-resources 1 comment
- GitHub - r0f1/datascience: Curated list of Python resources for data science. https://github.com/r0f1/datascience 0 comments
- GitHub - ujjwalkarn/Machine-Learning-Tutorials: machine learning and deep learning tutorials, articles and other resources https://github.com/ujjwalkarn/Machine-Learning-Tutorials 0 comments
- GitHub - guillaume-chevalier/GloVe-as-a-TensorFlow-Embedding-Layer: Taking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer **inside the GPU**. Therefore, you only need to send the index of the words through the GPU data transfer bus, reducing data transfer overhead. https://github.com/guillaume-chevalier/GloVe-as-a-TensorFlow-Embedding-Layer 0 comments
- GitHub - LukeTonin/keras-seq-2-seq-signal-prediction: An implementation of a sequence to sequence neural network using an encoder-decoder https://github.com/LukeTonin/keras-seq-2-seq-signal-prediction 0 comments
- GitHub - asetinUL/Awesome-Asetin https://github.com/asetinUL/Awesome-Asetin 0 comments
- GitHub - guillaume-chevalier/Awesome-Deep-Learning-Resources: Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier https://github.com/guillaume-chevalier/favorite-deep-learning-papers 0 comments
Linked pages
- Why do 87% of data science projects never make it into production? | VentureBeat https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/ 14 comments
- A Rant on Kaggle Competition Code (and Most Research Code) – Neuraxio https://www.neuraxio.com/en/blog/clean-code/2019/12/26/machine-learning-competition-code.html 14 comments
- GitHub - guillaume-chevalier/LSTM-Human-Activity-Recognition: Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition 8 comments
- What's Wrong with Scikit-Learn Pipelines? – Neuraxio https://www.neuraxio.com/en/blog/scikit-learn/2020/01/03/what-is-wrong-with-scikit-learn.html 3 comments
- GitHub - guillaume-chevalier/Awesome-Deep-Learning-Resources: Rough list of my favorite deep learning resources, useful for revisiting topics or for reference. I have got through all of the content listed there, carefully. - Guillaume Chevalier https://github.com/guillaume-chevalier/awesome-deep-learning-resources 1 comment
- How to Code Neat Machine Learning Pipelines – Neuraxio https://www.neuraxio.com/en/blog/neuraxle/2019/10/26/neat-machine-learning-pipelines.html 1 comment
- GitHub - guillaume-chevalier/ReuBERT: A question-answering chatbot, simply. https://github.com/guillaume-chevalier/ReuBERT 0 comments