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John's Projects

alphax-nasbench101 icon alphax-nasbench101

Neural Architecture Search with Deep Neural Network and Monte Carlo Tree Search

awesome-automl-and-lightweight-models icon awesome-automl-and-lightweight-models

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

awesome-automl-papers icon awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

awesome-nas icon awesome-nas

A curated list of neural architecture search (NAS) resources.

bert icon bert

TensorFlow code and pre-trained models for BERT

bertviz icon bertviz

Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)

c-cmaes icon c-cmaes

CMA-ES written in ANSI C (yet fairly object-oriented)

caffe icon caffe

Caffe: a fast open framework for deep learning.

caffe-android-demo icon caffe-android-demo

An android caffe demo app exploiting caffe pre-trained ImageNet model for image classification

conv_arithmetic icon conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

daae_ icon daae_

Denoising Adversairal Autoencoders

ddea-se icon ddea-se

Offline data-driven evolutionary optimization using selective surrogate ensembles

deepgamingai_fifa icon deepgamingai_fifa

A deep learning based AI bot for playing the football simulation game FIFA 18 (Windows platform)

deephyperneat icon deephyperneat

A public python implementation of the DeepHyperNEAT system developed by Felix Sosa and Kenneth Stanley for evolving neural networks. See paper here: https://eplex.cs.ucf.edu/papers/sosa_ugrad_report18.pdf

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为15个章节,近20万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06

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