Deep Neural Networks implemented from scratch. Convolutional Neural Networks (CNN) implemented using Tensowflow.
Collection of Deep Learning approaches, starting from simple Neural Network architectures and pursuing with contemporary and state-of-the-art practices and models.
| Notebook | Description |
|---|---|
| NeuralNetwork_MNIST | NNet class implementation from scratch, allowing for flexible definition and training/evaluation of conventional neural networks (MLP). |
| ConvolutionalNN_MNIST | CNNet function implementation based on Tensorflow, implementing a generalized model to use such library as well as the LeNet5 model. |
| LSTMNetwork | RNN Recurrent Neural Network using Long-Short-Term-Memories to predict the next word in a text, and eventually create stories from a single sentence |
All notebooks follow a general structure:
Deep Learning is a new approach in Machine Learning which allows to build models that have shown superior performance fora wide range of applications, in particular Computer Vision and Natural Language Processing. Thanks to the joint availability of large data corpus and affordable processing power, Deep Learning has revived the old field of Artificial Neural Networks and provoked the “Renaissance” of AI (Artificial Intelligence). The present notebooks will address this topic, implementing from scratch and using well known libraries, Deep Neural Networks.
The initial assignments come from the course in DeepLearning taught in EURECOM by Bernoit Huet
The notebooks have been crafted mainly by their authors, following well known open source documentation for the different libraries used and the material from the course.
The notebooks are based on publicly available data.
MIT Free software