cdemutiis/CNN_CIFAR10

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Created almost 7 years ago
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cdemutiis/CNN_CIFAR10

cdemutiis

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Use a convolutional neural network to classify the dataset CIFAR 10

Vehicle_Detection_and_Tracking

Use a Histogram of Oriented Gradients (HOG), Spatial Binning of Color, Histograms of Color, a Linear Support Vector Machine and multi-scale sliding windows for vehicle detection and tracking

Jupyter Notebook

Traffic_Sign_Classifier

Traffic Sign Classifier using a convolutional neural network

Jupyter Notebook

highway_lanes_detection

Detecting highway lane lines on a video stream with a Hough Transform and a Canny edge detector

Jupyter Notebook

EM_LEARNING

EM learning for a mixture of K multivariate Bernoullis with binary images

Model_Predictive_Control

Using Model Predictive Control to drive a car around the track on the Udacity Simulator

LSTM_RNN_Text_generation

Using the book Anna Karenina by Leo Tolstoy as a training source for text generation using RNN and LSTM

Jupyter Notebook

NLP_regression

First stage of a biomedical event extractor

Jupyter Notebook

Behavioral-Cloning

Teach a convolutional neural network (NVIDIA architecture) how to drive using the Udacity self-driving car simulator

PID_controller

Using a PID controller to drive a car around the track on the Udacity Simulator

Boston_housing_prices

Predicting Boston Housing Prices with Decision Trees

Kidnapped_Vehicle

Using a 2-dimensional Particle Filter to localize a vehicle

linear_gp_regression

Bayesian linear and Gaussian process regression to predict CO2 concentration as a function of time

Extended_Kalman_Filter

Using a Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements

transfer_learning

Transfer Learning

Unscented_Kalman_Filter

Using an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements

Local-Odometry-Techniques-MIRTO-

- Provided the MIRTO robot, designed and built by a team led by Dr. Franco Raimondi ([email protected]) at Middlesex University London, with autonomous navigation planning capabilities. - Wrote a library of high-level odometrical functionalities (i.e. Java methods) to allow MIRTO to perform actions such as rotating, translating and moving towards a specific point in space while avoiding all obstacles on the way. - Developed a navigation algorithm that only makes use of MIRTO's wheels' encoders and bumpers sensors. - Used MIRTO to test the newly developed navigation algorithm.