CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

A game theoretic approach to explain the output of any machine learning model.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

TensorFlow Tutorials with YouTube Videos

Build your neural network easy and fast, 莫烦Python中文教学

Labs and demos for courses for GCP Training (http://cloud.google.com/training).

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K…

TensorFlow documentation

Reference models and tools for Cloud TPUs.

A simplified implemention of Faster R-CNN that replicate performance from origin paper

A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.

Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

[DEPRECATED] See the new edition:

[CVPR 2025] Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering

Light-Head R-CNN

Inference Code for Polygon-RNN++ (CVPR 2018)

This repository includes tutorials on how to use the TensorFlow estimator APIs to perform various ML tasks, in a systematic and standardised way

Auto-encoding & Generating 3D Point-Clouds.

Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

[ko] 패스트캠퍼스 강의 자료

Implemention of FCN-8 and FCN-16 with Keras and uses CRF as post processing

PyTorch tutorials A to Z

Esper instance for TV news analysis

JuPyter Notebooks and Python Package for Deep Learning Model Exploration, Translation and Deployment

Simple tutorials about SageMaker