ttungl

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/Statistical-Learning

ttungl/feature-selection-for-machine-learning

ttungl/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).

ttungl/RLE-NOC

ttungl/recommender_systems_fkane

ttungl/annotated_deep_learning_paper_implementations

🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, etc. 🧠

ttungl/Awsome-Deep-Learning-for-Video-Analysis

Papers, code and datasets about deep learning and multi-modal learning for video analysis

ttungl/causalml

Uplift modeling and causal inference with machine learning algorithms

ttungl/channel-attribution-model

An attention-based Recurrent Neural Net multi-touch attribution model in a supervised learning fashion of predicting if a series of events leads to conversion (purchase). The trained model can also assign credits to channels. The model also incorporates user-context information, such as user demographics and behavior, as control variables to reduce the estimation biases of media effects.

ttungl/coursera-natural-language-processing-specialization

Programming assignments from all courses in the Coursera Natural Language Processing Specialization offered by deeplearning.ai.

ttungl/data-science-from-scratch-1

ttungl/deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

ttungl/ds_salary_prediction

ttungl/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

ttungl/feature_engine

Feature engineering package with sklearn like functionality

ttungl/gem5

ttungl/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

ttungl/homepage

ttungl/kaggle

Public codes for data science competitions on Kaggle.

ttungl/laceupleetcoding

ttungl/learning-spark

Example code from Learning Spark book

ttungl/leetcode-contests

ttungl/Logo_Detection

ttungl/machine-learning-algorithms-implementation

ttungl/practical-statistics-for-data-scientists

Code repository for O'Reilly book

ttungl/shapash

Shapash makes Machine Learning models transparent and understandable by everyone

ttungl/spark_scala_fkane

ttungl/Surprise

A Python scikit for building and analyzing recommender systems

ttungl/ticdat

ttungl/ttungl.github.io