Deep Learning for Computer Vision 深度學習於電腦視覺 by Frank Wang 王鈺強
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Updated
Jun 30, 2024 - Python
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
Deep Learning for Computer Vision 深度學習於電腦視覺 by Frank Wang 王鈺強
A Great Collection of Deep Learning Tutorials and Repositories
Learning key AI and ML Concepts
Pure C multi modal 3D Hybrid GAN using Cross attention, attention and convolution
Recreating Hand written numbers using GAN Model and how far could AI replicate human Hand writing
Efficient generative adversarial networks using linear additive-attention Transformers
Generating aerial flood prediction imagery with generative adversarial networks
Synthetic data generation for tabular data
Portfolio of deep learning projects
📦 GAN-based models to flash-simulate the LHCb PID detectors
12 Weeks, 24 Lessons, AI for All!
🧑🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
NYCU Data Science 2024
The collection of pre-trained, state-of-the-art AI models for ailia SDK
A pytorch implementation of Progressive Growing GAN.
Repo for all the SRIP 2024 work at CVIG Lab IITGN under Prof. Shanmuganathan Raman
Synthetic data generators for tabular and time-series data
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Released June 10, 2014