Pinned Repositories
Deep-Learning
Implemented the deep learning techniques using Google Tensorflow that cover deep neural networks with a fully connected network using SGD and ReLUs; Regularization with a multi-layer neural network using ReLUs, L2-regularization, and dropout, to prevent overfitting; Convolutional Neural Networks (CNNs) with learning rate decay and dropout; and Recurrent Neural Networks (RNNs) for text and sequences with Long Short-Term Memory (LSTM) networks.
feature-selection-for-machine-learning
feature_engine
Feature engineering package with sklearn like functionality
HeteroArchGen4M2S
HeteroArchGen4M2S: An automatic software for configuring and running heterogeneous CPU-GPU architectures on Multi2Sim simulator. This tool is built on top of M2S simulator, it allows us to configure various heterogeneous CPU-GPU architectures (e.g., number of CPU cores, GPU cores, L1$, L2$, memory (size and latency (via CACTI 6.5)), network topologies (currently support 2D-Mesh, customized 2D-Mesh, and Torus networks)...). The output files include the results of network throughput and latency, caches/memory access time, and dynamic power of the cores (can be collected after running McPAT).
Machine-Learning
Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.
RLE-NOC
SDC-term1-Advanced-Lane-Finding
Detected highway lane boundaries on a video stream with OpenCV image analysis techniques, including camera calibration matrix, distortion correction, color transforms, gradients, etc., to create a thresholded binary image, a perspective transform to rectify binary image ("birds-eye view"). Detected lane pixels and fit to find the lane boundary, determined the curvature of the lane and vehicle position with respect to center. Warped the detected lane boundaries back onto the original image.
SDC-term1-Behavioral-Cloning
Built and trained a convolutional neural network to drive the car itself autonomously in a simulator using Tensorflow (backend) and Keras. Experimented with a modified Nvidia architecture. Performed image processing with brightness, shadow augmentation, and flipped images. Used dropout and Adam optimizer to generalize the network for driving multiple tracks. The datasets are used via Udacity's source for training the model. Trained the model on Amazon AWS EC2 platform with GPU instances.
SDC-term1-Traffic-Sign-Classifier
Built and trained a deep neural network to classify traffic signs, using TensorFlow. Experimented with different network architectures. Performed image pre-processing and validation to guard against overfitting. The datasets are collected from the German Traffic Sign for training and random traffic signs downloaded from internet for testing.
Statistical-Learning
ttungl's Repositories
ttungl/Wireless-network-802-11-DCF-MAC
Implemented a 802.11 DCF MAC Protocol operation with Gillbert-Elliot channel model, RTS/CTS exchange, in different network topologies. Used C++ for implementation.
ttungl/Java-Multithreading
Implemented the basis of java multithreading, including the basic threads synchronization, multiple locks using Synchronized Code Blocks, thread pools, countdown latches, Producer-Consumer, Wait and Notify, Low-level Synchronization, Re-entrant Locks, Deadlock, Semaphores, Callable and Future, Interrupting Threads, and Multithreading in Swing with SwingWorker.
ttungl/Pingoin
Pingo'in [android app] is created by using a Google maps API. You can build your list of points of interest (POI) on the Googlemap, then the application will scan your map in the preset radius, if your POIs are within this radius, they will be displayed on your screen. Used Java, Eclipse for building the app, and used SVN for merging the code project.
ttungl/Price-Dropping-Looker-v1.0
A tool for looking into the price dropped of the Amazon's items. Your "wishlist" items on Amazon will be alerted via your email if those prices are dropped below your expected price, ratings and reviews also are taken into account.
ttungl/Verilog-VHDL-ALU-16bit
ALU 16-bit design with LCD display VHDL coding on Spartan 3E FPGA Starter kit.
ttungl/30daysofcode_hackerrank
Hackerrank provides a lot of interviewing questions that could help to prepare for the coding interviews.
ttungl/analytics_pipeline
Code to build a simple analytics data pipeline with Python
ttungl/aws-mobile-chatbot-vehicle
AWS Lambda function for validating and fulfilling user input for Amazon Lex VehicleValue Chatbot
ttungl/bias-variance-tradeoff
Sandbox for generating visualizations of the bias-variance tradeoff for Machine Learning at Berkeley's blog.
ttungl/CarND-Behavioral-Cloning
Deep neural network used to clone human driving behavior for end-to-end autonomous driving
ttungl/CarND-TensorFlow-Lab
TensorFlow Lab for Self-Driving Car ND
ttungl/CarND-Vehicle-Detection
My solution to the Udacity Self-Driving Car Engineer Nanodegree Vehicle Detection and Tracking project.
ttungl/Cinema-Booking-System-RDBMS
ttungl/coding_interviews
ttungl/Convolutional-Neural-Networks-for-Visual-Recognition
Train convolutional neural networks to recognize the objects.
ttungl/DeepLearningBook
MIT Deep Learning Book in PDF format
ttungl/DeepLearningTutorials
Deep Learning Tutorial notes and code. See the wiki for more info.
ttungl/interviews
Everything you need to know to get the job.
ttungl/keras
Deep Learning library for Python. Runs on TensorFlow, Theano, or CNTK.
ttungl/leetcode
hack the leetcode problems: 1-3; 65; 466;
ttungl/Multi-class-inheritance-in-Scheme
Used Scheme (Dr. Racket) to modify the interpreter for creating new functions of a language. In this work, multi-class inheritance is created. A new instance generated is inherited to all the methods from the joined classes. Used Scheme language for implementation.
ttungl/multi2sim-5.0
ttungl/neuraltalk
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
ttungl/Pokemon-Go-Random-Characters
This add-on extension randomly displays Pokemon GO characters on Chrome browser. Available on Chrome Web Store at http://goo.gl/DlqG3k
ttungl/Random-Happy-Face
Random change happy face icon every 1 second, repeat forever. This add-ons app currently only supports Google Chrome Browser. Enjoy your happy day! Available on Chrome Web Store: http://goo.gl/ILp7b3
ttungl/resize
Pure golang image resizing
ttungl/scala.epfl.ch
Website for Scala@EPFL
ttungl/simple-reinforcement-learning
ttungl/tensorflow
Computation using data flow graphs for scalable machine learning
ttungl/TicTacToe