D. Sigdel's Projects
Building a pipeline that can be used within a web or mobile app to process real-world, user-supplied images
Project materials for RNN segment of AIND nanodegree
:mortar_board: Building a deep neural network that functions as part of an end-to-end machine translation pipeline. :rocket:
Data Science Apps in R
Built an advanced lane-finding algorithm using OpenCV for Hough Transforms and Canny edge detection, distortion correction, image rectification, color transforms, and gradient thresholding. Identified lane curvature and vehicle displacement. Detected highway lane lines on a video stream. Overcame environmental challenges such as shadows and pavement changes.
Built and trained a convolutional neural network for end-to-end driving in a simulator, using TensorFlow and Keras. Used optimization techniques such as regularization and drop out to generalize the network for driving on multiple tracks.
Finding Lane Lines on the Road
:octocat: Implemented Model Predictive Control to drive a vehicle around a track even with additional latency between commands.
:mortar_board: Create a path planner that is able to navigate a car safely around a virtual highway
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.
Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Optimized and evaluated the model on video data from an automotive camera taken during highway driving.
CaseOLAP: A cloud computing platform for phrase mining. This consists of downloading, parsing, and indexing of data, text-cube creation, entity count with search functionality, and caseOLAP score calculation.
:rocket: Udacity DLND-project-5: Built Generative adversarial networks (GANS) which puts two neural networks in competition, allowing these networks to model reality with amazing accuracy.
:octocat: Udacity DLND-project-4: Language translation using RNN
:rocket: Built basic neural network with python and numpy.
Docker Build
Repository for hyperparameter tuning
This repository contains the notebooks, slides, and miscellaneous on lectures delivered in LOCUS Quantum Computing 2021.
:rocket: Udacity Machine Learning Projects :mortar_board:
:octocat: Numpy for handling n-dim array, algebraic operation, plotting, statistics, and NumPy array functions :rocket:
:book: Theory and Models in Optimization (e.g., finding the minimum of a function, finding roots, global and local minimum)
Sample Node.js Docker application referred to by Azure Pipelines documentation
:octocat: Python Basics with fundamental datastructure, loops, functions and class, input/output, plotting
:book: Introduction to Quantum Computing and Projects :rocket:
RAPIDS tutorials from NVIDIA
:octocat: Introduction to Scientific Computing (e.g., differentiation, integration, interpolation, differential equations) with Numpy, Scipy, Matplotlib, Pandas
Text Classification
API with ASP.NET