riven314/ULMFit-IMDB

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riven314/ULMFit-IMDB

riven314

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extended analysis on ULMFit modeling from lesson 4, Practical Deep Learning for Coders (fast.ai)

All-of-Statistics-Exercises

This repo documents some of my drafted solutions to the exercises from All of Statistics by Larry Wasserman. This repo only contains solutions to exercises that requires computer experiment. The book is a handy reference to most concepts of statistics.

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End2End-Controllable-Instagram-Captioning

An end-to-end approach to build an image captioning model with engaging captions and controllable attributes

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d435_module

Python module for Realsense D435i camera

TweakStory

Deployment repo for the trained stylised-controllable-image-captioning model.

WGANGP-Presentation

Materials for my presentation on 17/07/2019 in Hong Kong Machine Learning Meetup. The topic is WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty)

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Keras-UNet-Foreground-Extraction

Using U-Net for foreground and background separation on biomedical image

DeepLearning-Navigation

Document my capstone project in master programme

Divide_and_Conquer_Algo

This repository aims to document my learning progress on a MOOC course "Divide and Conquer, Sorting and Searching, and Randomized Algorithms" by Stanford University

CycleGAN-FastAI

as a proof-of-concept, test if CycleGAN can learn spatial variation from images with multiple MNIST digits

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riven314.github.io

OBSOLETE, PLEASE GO TO https://github.com/riven314/alexlauwh

tensor-client-py

Unofficial SDK for Tensor NFT Marketplace, in Python!

SelfDrivingCar_Simulator

This repo documents my work on training a CNN model for self-driving car. I deployed fastai framework for model training. I experimented with different models, the first two being pretrained ResNet34 and the CNN proposed by NVIDIA in literature.

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FasterRCNN-Pipeline-Pytorch

Setup a pipeline for training (transfer learning) Faster-RCNN in PyTorch. Data are in VOC format

PerceptualLoss-FastAI

Implementing "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" with FastAI framework. Apply hook (PyTorch mechanism) to calculate loss.

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