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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
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Thank you guillaume-chevalier & contributors
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Posts and Articles
Librairies and Implementations
A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.
An Open Source Machine Learning Framework for Everyone
Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
"Neural Turing Machine" in Tensorflow
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
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.
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye.
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
A coding exercise: let's convert dirty machine learning code into clean code using a Pipeline - which is the Pipe and Filter Design Pattern applied to Machine Learning.
A topic-centric list of HQ open datasets.
Gradient Descent Algorithms & Optimization Theory
Complex Numbers & Digital Signal Processing
Simple demo of filtering signal with an LPF and plotting its Short-Time Fourier Transform (STFT) and Laplace transform, in Python.
Hann-Poisson window is specially interesting for greedy hill-climbing algorithms (like gradient descent for example).