rodgzilla/machine_learning_used_cars

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Created almost 8 years ago
Updated almost 8 years ago

rodgzilla/machine_learning_used_cars

rodgzilla

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

machine_learning_adversarial_examples

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

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machine_learning_denoising

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

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machine_learning_mandelbrot_neural_network

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

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teaching

NVSM_pytorch

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

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talk-slides

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

buddhabrot

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

machine_learning_image_anomaly_detection

Program that tries do detect outliers in an image dataset

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machine_learning_toy_autoencoder

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

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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.

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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)

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fractal_GAN

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

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keras_neural_style_transfer

Keras implementation of the original neural style transfer paper.

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machine_learning_intent_classification

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machine_learning_pokemon

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

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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.

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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.

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machine_learning_fractal_super_resolution

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

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random_projection_experiment

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

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