Introduction to TensorFlow 2.0 Tutorials
The TensorFlow 2.0 Tutorials project is recognized as the winner of the #PoweredByTF 2.0 Challenge, showcasing its significance and popularity within the machine learning community. This project provides extensive tutorials designed to help users understand and utilize TensorFlow 2.0, a powerful platform for machine learning and artificial intelligence tasks.
Development Timeline
The evolution of TensorFlow 2.0 reflects its growth and development over time:
- Aug. 14, 2018: The initial announcement of TensorFlow 2.0.
- Jan. 11, 2019: Preview version released.
- Mar. 7, 2019: Alpha version available.
- Jun. 8, 2019: Beta version introduced.
- Aug. 24, 2019: Release candidate (rc0) published.
- Oct. 1, 2019: Official stable release of TensorFlow 2.0.
Installation Guide
To get started, ensure that Python 3.x is installed on your system. There are two main installation methods based on whether you want CPU or GPU support:
CPU Installation
pip install tensorflow -U
GPU Installation
For GPU support, additional setup is required:
- Install CUDA 10.0 (or later) and cuDNN.
- Set up the
LD_LIBRARY_PATH.
pip install tensorflow-gpu -U
Testing the Installation
Once installed, you can test your setup with the following commands:
import tensorflow as tf
print(tf.__version__)
print(tf.test.is_gpu_available())
Video Tutorials
For visual learners, there is an accompanying video course titled "Deep Learning and TensorFlow 2 in Practice." This resource provides practical insights and examples to supplement the tutorials in the repository.
Acknowledgements
Special mention is given to AiKeKe-LiveLife for their friendly recommendation. Their support has contributed to the project's reach and usability.
Tutorial Content
The repository encompasses a wide array of tutorials covering fundamental to advanced topics in TensorFlow 2.0:
- General Topics: TensorFlow 2.0 Overview, Basic Usage.
- Machine Learning Models: Linear Regression, MNIST & FashionMNIST, CIFAR10.
- Deep Learning Architectures: Fully Connected Layers, VGG16, Inception Network, ResNet18.
- Sequential Models: Naive RNN, LSTM.
- Generative Models: Auto-Encoders, Variational Auto-Encoders, DCGAN, CycleGAN, WGAN, Pixel2Pixel.
- Other Models: ColorBot, Faster RCNN, A2C, GPT, BERT, GCN.
Users are encouraged to contribute by submitting pull requests to enhance and expand the repository's offerings.
Referenced Repositories
The TensorFlow 2.0 Tutorials project builds upon the foundation laid by various other open-source projects. Acknowledgement goes to the following repositories for their invaluable contributions:
- TensorFlow Eager Tutorials: Madalinabuzau/tensorflow-eager-tutorials
- CycleGAN Implementations: Herbiebradley/CycleGAN-Tensorflow
- Pix2Pix Eager Examples: tensorflow/tensorflow/contrib/eager/python/examples/pix2pix
- GANs with TensorFlow Eager: Moono/tf-eager-on-GAN
- FasterRCNN in TensorFlow: Viredery/tf-eager-fasterrcnn
- Python Git Ignore: GitHub/gitignore/blob/master/Python.gitignore
This rich set of tutorials and resources positions TensorFlow 2.0 Tutorials as an essential guide for anyone looking to delve into TensorFlow 2.0 for deep learning and beyond.