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

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

seq2seq-signal-prediction. Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

How-to-Grow-Neat-Software-Architecture-out-of-Jupyter-Notebooks. Growing the code out of your notebooks - the right way.

HAR-stacked-residual-bidir-LSTMs. Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.

Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP. What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.

Linear-Attention-Recurrent-Neural-Network. A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

Hyperopt-Keras-CNN-CIFAR-100. Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

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.

filtering-stft-and-laplace-transform. Simple demo of filtering signal with an LPF and plotting its Short-Time Fourier Transform (STFT) and Laplace transform, in Python.

ReuBERT. A question-answering chatbot, simply.

PyTorch-Dynamic-RNN-Attention-Decoder-Tree. This is code I wrote within less than an hour so as to very roughly draft how I would code a Dynamic RNN Attention Decoder Tree with PyTorch.

LinkedIn-Connections-Growth-Analysis. Assessing personal growth on LinkedIn with charts. Plot LinkedIn connections over time. Discover what your connections most do and where they most work.

Predict-if-salary-is-over-50k-with-Keras. Predict whether income exceeds $50K/yr based on census data of the "Adult Dataset". Also known as "Census Income" dataset.

SGNN-Self-Governing-Neural-Networks-Projection-Layer. Attempt at reproducing a SGNN's projection layer, but with word n-grams instead of skip-grams. Paper and more: http://aclweb.org/anthology/D18-1105

AI-Planning-Solver-Shakeys-World-PDDL. Solving a planning problem (Shakey's World) with the FF and IPP planners, the PDDL language and some Python meta-programming to glue things together.

caffe-cifar-10-and-cifar-100-datasets-preprocessed-to-HDF5. Both deep learning datasets can be imported in python directly with h5py (HDF5 format). The datasets can be directly imported or converted with a python script.

EDA-for-Cybersecurity-Intrusion-Detection-KDD-Cup-99. Jupyter Notebook

Machine-Learning-Tutorials. machine learning and deep learning tutorials, articles and other resources

python-caffe-custom-cifar-100-conv-net. Custom convolutional neural network on cifar-100 dataset for image classification. Images and their labels are processed to HDF5 data format for use in Caffe.

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

python-conv-lib. A lightweight library to do for-loop-styled convolution passes on your iterable objects (e.g.: on a list). Note: this is not a convolution, it is about exposing what would the kernel pass on in the first place in your loops.

awesome-jupyter. A curated list of awesome Jupyter projects, libraries and resources

CS-Games-2018-Google-Challenge-TensorFlow. Jupyter Notebook

awesome-deep-learning. A curated list of awesome Deep Learning tutorials, projects and communities.

algorithms. Minimal examples of data structures and algorithms in Python

SGNN-Transformer-Sentence-Model-SimilarityBXEnt. SGNN-Transformer Sentence Model trained by the paragraph-skip-gram-like SimilarityBXENT. Also see: https://github.com/guillaume-chevalier/SGNN-Self-Governing-Neural-Networks-Projection-Layer

awesome-analytics. A curated list of analytics frameworks, software and other tools.

awesome-rnn. Recurrent Neural Network - A curated list of resources dedicated to RNN

guillaume-chevalier.

CSGames-2019-AI. Our team's solution for the Artificial Competition of the Computer Science Games 2019 (a programming contest). Deep Reinforcement Learning.

scikit-learn-digit-recognition. Digit Recognition with scikit-learn's Bernoulli RBM and Logistic Classifier

awesome-deeplearning-resources. Deep Learning and deep reinforcement learning research papers and some codes

DataSciencePython. common data analysis and machine learning tasks using python

XGBoost-Beer-Recommendation. Small scholar project for the CS Games 2019 qualification. An afternoon of coding. Data loading and validation code could be better.

Sentiment-Classification-and-Language-Detection. Jupyter Notebook

dotfiles. Shell

NanoBot. [2013] A game for an introductory programming course. I coded a 2D car side scrolling physic engine from scratch.

Wikipedia-XML-Markup-Code-to-Plain-Text-Parser-of-Hell. Parsing a Wikipedia XML file of all articles to lots of raw txt files, and remove most of wiki markup (not perfect: see issues first). For more info on wiki markup, see: https://en.wikipedia.org/wiki/Wikipedia:Tutorial/Formatting#Wiki_markup

python-github-projects. Collect and classify python projects on Github

scikit-learn. scikit-learn: machine learning in Python

Multi-Layer-Perceptron. Python