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This project trains a series of next-token language models, starting from a linear predictor through multilayer perceptrons, self-attention, and small transformers. Character-level experiments use Tiny Shakespeare, while word-level experiments adapt the same transformer backbone to WikiText-2 and Penn Treebank (PTB). Hyperparameter sweeps compare context length, embedding size, depth/width, attention heads, optimizers, schedulers, and batch sizes, with results logged to report_src/ alongside plots and generated text samples.

Coursework for CSCI 689 implementing normalizing flows (RealNVP) and diffusion models on a 2D toy dataset. Code generates PDFs/plots documenting each question’s results.

Dataset (data.h5) - a training set of m_train images labeled as cat (y=1) or non-cat (y=0) - a test set of m_test images labeled as cat or non-cat - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB).

Build and apply a deep neural network to supervised learning.

Implement helper functions by implementing a TensorFlow model Implement a fully functioning ConvNet using TensorFlowBuild and train a ConvNet in TensorFlow for a classification problem

Implement the basic building blocks of ResNets. Put together these building blocks to implement and train a state-of-the-art neural network for image classification.

About object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers Redmon et al., 2016 and Redmon and Farhadi, 2016. Use object detection on a car detection dataset Deal with bounding boxes.

Implement the triplet loss function Use a pretrained model to map face images into 128-dimensional encodings Use these encodings to perform face verification and face recognition

Implement the neural style transfer algorithm Generate novel artistic images using your algorithm

Use non-linear units like ReLU to improve your model Build a deeper neural network (with more than 1 hidden layer) Implement an easy-to-use neural network class

Apply an LSTM to music generation. Generate your own jazz music with deep learning.

Ultimate Map for Academic Strategy & Success (UMASS) is a comprehensive web platform designed to help university students thrive academically and prepare for their future careers. Students may share resources, access course information, evaluate professors, collaborate with peers, and strategize their academic journey.

This is an advance algorithm project from CS course - Algorithms and Data Structures.
Algorithm Deliverable D - finding a good Ham cycle path by local search or Genetic Algorithm.
Algorithm Deliverable C - From the starting city use depth first search with iterative deepening to find the nearest goal city.
Algorithm Deliverable B - find the shortest Bitonic Tour of the cities.

This Project is Full-Stack Application of Airline Search service Restful API.
Client side, single Web page, was implement with Angular CLI version 8.3.20.
Server side implements by Java spring boot and connect to MySQL Database.

This project is the single page that build by React and fetch all data from web server using MongoDB to save data this server API that has been deployed to Heroru and testing with Mocha&Chai

This mobile app is for practice react native, react reduxe, and google firebase that implement by android studio, etc.

Parallel and Distributed Algorithms --- ICS 440-01 Fall 2021
Creating multi-thread of self-running object to wait for client
requests to be sent up then send back the appropriate response.
Use the writeUTF(String s) method on DataOutputStream to send a reqeust
(or a response) over the OutputStream of the Socket.
Use the readUTF() method on DataInputStream to receive a request (or a
response) from the InputStream of the Socket.