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Vehicle-Detection-using-Faster-R-CNN
Vehicle Detection using state-of-the-art architecture Faster R-CNN with Pretrained models like VGG16 and ResNet50Clustering-Analysis-on-customers-of-a-wholesale-distributor
Project in which unsupervised learning techniques are applied on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.Bank-Marketing-Classification
Bank Marketing Classification using scikit-learn library to train and validate classification models like Logistic Regression, Decision Tree, Random Forest, NaΓ―ve Bayes, Neural Network and Support Vector Machine. Also conducted comparative study on the above models when applied on different feature sets obtained via feature selection (Chi-Square Test), feature transformation (Principal Component Analysis) and feature elimination.Batch-Normalization
Batch Normalization using tf.layers and tf.nn libraries. Also comparing the performances with and without batch normalization.General-Adversarial-Network-MNIST
Building our own GAN and train it on MNIST dataset. Built the generator and discriminator networks using Tensorflow.Year-Prediction-using-Regression
Predicting Release Year of a Song using different Regression algorithms implemented from scratch using numpyYoutube-Video-Label-Classification
Youtube Video Label Classification using Single Frame model and Long-term Recurrent Convolutional Networks (LRCN) modelDeep-Q-Learning
Tensorflow implementation of a deep neural network that can learn to play games via reinforcement learning. For this notebook the game used is the Cart-Pole game that is available in the OpenAI Gym library. Also the deep neural network can be used for other games as well. In order to run this on your system you will also need to clone the OpenAI gym repository.Monte-Carlo-Methods-for-Reinforcement-Learning
Implementations of many Monte Carlo (MC) algorithms for updating policies of an environment using action values, greedy and epsilon-greedy procedures. Environment used for this notebook is the BlackJack Environment (can be seen in the OpenAI Gym library) and these functions can be used for other environments as well.Love Open Source and this site? Check out how you can help us