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Created over 7 years ago
Updated over 7 years ago

rodgzilla

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A training of various neural networks to approximate the mandelbrot fractal image.

machine_learning_adversarial_examples

An implementation of the 'fast gradient sign method' from the paper 'Explaining and Harnessing Adversarial Examples'

Jupyter Notebook

machine_learning_denoising

A Keras implementation of the "Deep Image Prior" paper.

Jupyter Notebook

teaching

Python

NVSM_pytorch

PoC of the "Neural Vector Space Model" on a bunch of wikipedia articles.

Jupyter Notebook

talk-slides

Slides (with LaTeX files) of the various talks I give (ML / DL / fun with coding)

TeX

buddhabrot

Basic buddhabrot fractal rendering in Python using multiprocessing and Python Image Library

Python

machine_learning_image_anomaly_detection

Program that tries do detect outliers in an image dataset

Jupyter Notebook

machine_learning_toy_autoencoder

A simple autoencoder trained on the MNIST dataset and a visualization of the latent code space.

Jupyter Notebook

machine_learning_car_counter

A small deep learning notebook based on the object detection model of Tensorflow that counts the number of cars on a picture.

Jupyter Notebook

machine_learning_deep_knn

Experiments with deep learning interpretability based on "Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning" (https://arxiv.org/abs/1803.04765)

Jupyter Notebook

fractal_GAN

A deep learning project where the goal is to train a generator to produce plausible fractal images.

Jupyter Notebook

keras_neural_style_transfer

Keras implementation of the original neural style transfer paper.

Jupyter Notebook

machine_learning_used_cars

Data visualization and predictions on a kaggle used car data set.

Jupyter Notebook

machine_learning_intent_classification

Jupyter Notebook

machine_learning_pokemon

Trying to predict the type of pokemon knowing its characteristics (based on a Kaggle dataset).

Jupyter Notebook

machine_learning_pytorch_deep_image_prior

Implementation of the denoising part of the "Deep Image Prior" paper in PyTorch. This implementation is also available as a Google Colaboratory notebook.

Jupyter Notebook

machine_learning_pytorch_simple_cnn_1d_nlp

The purpose of this notebook is to demonstrate how to build a simple one dimensionnal CNN to do text classification. The dataset used in this notebook is the "Twitter Sentiment Analysis" dataset. The goal of the network is to be able to predict whether a given has a positive or a negative sentiment.

Jupyter Notebook

machine_learning_fractal_super_resolution

Using deep learning to produce zooms on the Mandelbrot set using super-resolution networks.

Jupyter Notebook

random_projection_experiment

Experiments with random projection for similarity measurement and Johnson-Linderstrauss lemma.

Jupyter Notebook

rodgzilla/machine_learning_mandelbrot_neural_network

rodgzilla

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