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

code for paper -- "Seamless Satellite-image Synthesis"

Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗

Official Code for VideoLT: Large-scale Long-tailed Video Recognition (ICCV 2021)

Graph Neural Networks with Keras and Tensorflow 2.

Machine learning, in numpy

Codebase for arXiv preprint "NeRF++: Analyzing and Improving Neural Radiance Fields"

[NeurIPS-2021] Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.

The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

Geometry-Aware Learning of Maps for Camera Localization (CVPR2018)

Lightwood is Legos for Machine Learning.

Pytorch Implementation of Residual Vision Transformers(ResViT)

Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images

The source code for 'Noisy-Labeled NER with Confidence Estimation' accepted by NAACL 2021

Improving adversarial robustness by a coupling rejection strategy

Sharpened cosine similarity torch - A Sharpened Cosine Similarity layer for PyTorch

CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction. ICCV 2021

Train the HRNet model on ImageNet