Download
TensorFlow 2.0 Tutorials is an open-source educational repository that provides practical examples and walkthroughs for learning deep learning using the TensorFlow 2.x framework. The repository contains a large set of hands-on tutorials that demonstrate how to build neural networks and machine learning systems with modern TensorFlow APIs. These examples cover a wide range of topics including convolutional neural networks, recurrent neural networks, generative adversarial networks, autoencoders, and transformer-based models such as GPT and BERT. Each section of the repository includes runnable code and structured experiments designed to illustrate how different architectures and algorithms function in real applications. The tutorials use well-known benchmark datasets such as MNIST, CIFAR, and Fashion-MNIST to demonstrate practical model training and evaluation workflows.
Features
- Hands-on tutorials demonstrating TensorFlow 2.x machine learning workflows
- Examples of neural network architectures such as CNNs, RNNs, and GANs
- Implementation of advanced models including BERT, GPT, and Faster R-CNN
- Training experiments using datasets such as MNIST, CIFAR, and Fashion-MNIST
- Step-by-step examples for deep learning model development
- Collection of practical code notebooks and scripts for experimentation
Project Samples
