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

MPLP: Metapath-Based Label Propagation for Heterogenous Graphs

FNet Implementation with TensorFlow & PyTorch

Contrastive Fact Verification

Code for Deep Single-image Portrait Image Relighting

A forwarding MPI implementation that can use any other MPI implementation via an MPI ABI

ML powered analytics engine for outlier detection and root cause analysis.

Pytorch implementation of the paper Improving Text-to-Image Synthesis Using Contrastive Learning

[ACMMM 2021, Oral] Code release for "Elastic Tactile Simulation Towards Tactile-Visual Perception"

Pretraining on Dynamic Graph Neural Networks

Code for "LASR: Learning Articulated Shape Reconstruction from a Monocular Video". CVPR 2021.

Sparse Physics-based and Interpretable Neural Networks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Code for ViTAS_Vision Transformer Architecture Search

Omnidirectional Scene Text Detection with Sequential-free Box Discretization (IJCAI 2019). Including competition model, online demo, etc.

CLASP - Contrastive Language-Aminoacid Sequence Pretraining

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention

Code for sound field predictions in domains with impedance boundaries. Used for generating results from the paper

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

MultiLexNorm 2021 competition system from ÚFAL

Occlusion robust 3D face reconstruction model in CFR-GAN (WACV 2022)