TensorFlow Tutorials

You can find python source code under the python directory, and associated notebooks under notebooks.

	Source code	Description
1	basics.py	Setup with tensorflow and graph computation.
2	linear_regression.py	Performing regression with a single factor and bias.
3	polynomial_regression.py	Performing regression using polynomial factors.
4	logistic_regression.py	Performing logistic regression using a single layer neural network.
5	basic_convnet.py	Building a deep convolutional neural network.
6	modern_convnet.py	Building a deep convolutional neural network with batch normalization and leaky rectifiers.
7	autoencoder.py	Building a deep autoencoder with tied weights.
8	denoising_autoencoder.py	Building a deep denoising autoencoder which corrupts the input.
9	convolutional_autoencoder.py	Building a deep convolutional autoencoder.
10	residual_network.py	Building a deep residual network.
11	variational_autoencoder.py	Building an autoencoder with a variational encoding.

Installation Guides

For Ubuntu users using python3.4+ w/ CUDA 7.5 and cuDNN 7.0, you can find compiled wheels under the wheels directory. Use pip3 install tensorflow-0.8.0rc0-py3-none-any.whl to install, e.g. and be sure to add: export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" to your .bashrc. Note, this still requires you to install CUDA 7.5 and cuDNN 7.0 under /usr/local/cuda.

Resources

Author

Parag K. Mital, Jan. 2016.

http://pkmital.com

License

See LICENSE.md

From the basics to slightly more interesting applications of Tensorflow

Related tags

Overview

TensorFlow Tutorials

Installation Guides

Resources

Author

License

Owner

Parag K Mital

In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

[ICCV 2021 (oral)] Planar Surface Reconstruction from Sparse Views

ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS.

Auditing Black-Box Prediction Models for Data Minimization Compliance

[ICML 2021] Towards Understanding and Mitigating Social Biases in Language Models

Code for training and evaluation of the model from "Language Generation with Recurrent Generative Adversarial Networks without Pre-training"

"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

A library to inspect itermediate layers of PyTorch models.

Latte: Cross-framework Python Package for Evaluation of Latent-based Generative Models

Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)

Code for "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video", CVPR 2021 oral

Intelligent Video Analytics toolkit based on different inference backends.

【CVPR 2021, Variational Inference Framework, PyTorch】 From Rain Generation to Rain Removal

Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

一个多模态内容理解算法框架，其中包含数据处理、预训练模型、常见模型以及模型加速等模块。

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

NPBG++: Accelerating Neural Point-Based Graphics

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks

Face Recognition and Emotion Detector Device

ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'