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/gem5
ttungl/gem5-test
ttungl/120-Data-Science-Interview-Questions
Answers to 120 commonly asked data science interview questions.
ttungl/aas
Code to accompany Advanced Analytics with Spark from O'Reilly Media
ttungl/awesome-interpretable-machine-learning
ttungl/aws-ai-qna-bot
Code samples related to "Creating a Question and Answer Bot with Amazon Lex and Amazon Alexa", published on the AWS AI Blog. QnABot (pronounced “Q and A Bot”), uses Amazon Lex and Amazon Alexa to provide a conversational interface to your “Questions and Answers”, so users can just ask their questions and get quick and relevant answers.
ttungl/BellkorAlgorithm
A Python implementation of the Bellkor Algorithm
ttungl/Communication-Cost-Predictive-Models
ttungl/deep-conv-attr
An implementation of our CIKM 2018 paper "Deep Conversion Attribution with Dual-attention Recurrent Neural Network"
ttungl/deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
ttungl/deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
ttungl/driverlessai-recipes
Recipes for Driverless AI
ttungl/DS-Career-Resources
Compilation of resources and insights that helped me on my journey to data scientist
ttungl/DS-Take-Home
My solution to the book A Collection of Data Science Take-Home Challenges
ttungl/DynamoDB-B2P
Lab files for A Cloud Guru 'Amazon DynamoDB - From Beginner to Pro '
ttungl/fast-bert
Super easy library for BERT based NLP models
ttungl/incubator-airflow
Apache Airflow (Incubating)
ttungl/ipyparallel
Interactive Parallel Computing in Python
ttungl/LeetCode-3
:pencil: Python / C++ 11 Solutions of All 468 LeetCode Questions
ttungl/LINCA
ttungl/mlcourse.ai
Open Machine Learning Course
ttungl/multi2sim-HeteroArchGen4M2S
ttungl/NeMo
Neural Modules: a toolkit for conversational AI
ttungl/NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
ttungl/Optimizers-CPLEX-GUROBI-Implementation
ttungl/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
ttungl/rep
Machine Learning toolbox for Humans
ttungl/sparkmagic
Jupyter magics and kernels for working with remote Spark clusters
ttungl/StarbucksStoreScraping
code for scraping starbucks store data from Store Locator API
ttungl/The-Elements-of-Statistical-Learning-Python-Notebooks
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book