Deep Learning with TensorFlow 2.0 in 7 Steps
Learn image classification and language modeling
Created by Robert Thas John
Explore how to build and train deep learning models using TensorFlow 2.0, focusing on image classification and language modeling. Gain practical experience by working through real-world tasks, from setting up your environment to deploying models for production use.
Packt | Sep 2019 | 164 min
What You Will Learn
You will follow a clear, step-by-step process to set up your environment, import data, and create deep learning models. Each stage builds on the last, so you can see your progress as you go. By working directly with code and practical examples, you will develop skills that transfer easily to your own projects.
Key Features
- Build and train neural networks for image and text data using TensorFlow 2.0
- Apply CNNs and RNNs to solve real-world classification and modeling problems
- Import, process, and split data for effective model training and evaluation
Target Audience
Ideal for developers, programmers, and data scientists who already understand basic machine learning and Python. If you want to quickly move into deep learning and start building models for image or language tasks, this content will help you get hands-on experience with TensorFlow 2.0.
