Hands-on Machine Learning with TensorFlow.js, published by Packt
This is the code repository for Hands-On Machine Learning with TensorFlow.js [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Machine learning is a growing and in-demand skill, but so far JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. TensorFlow.js is a great way to begin learning machine learning in the browser with TensorFlow.js. It allows you to operate offline to train new models and retrain existing models.
This course covers most of the major topics in machine learning and explains them with the help of Tensorflow.js implementations. The course is focused on the result-oriented nature of most JavaScript developers, and focuses on Tensorflow.js to the fullest in the least amount of time. At the end of the course, you’ll evaluate and implement the right model to design smarter applications.
- Immediately get started using Ansible
- Practical understanding of Ansible usage in real-world usage scenarios
- Concrete, real-world examples of Ansible playbook code provided (via a Git repo)
- Master task-based automation approaches to increase efficiency and save time administering systems
- Preparatory foundation for more advanced automation and IT streamlining with Ansible
- A deeper understanding of Ansible design and usage, paving the way for designing and managing your own automation using Ansible
To fully benefit from the coverage included in this course, you will need:
If you’re a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language, this is the course for you!
Working knowledge of JavaScript language is assumed and some background of machine learning concepts will be beneficial.
- OS: Linux, Windows, MAC
- Processor: 2.4 GHz
- Memory: 4 GB
- Storage: 100 GB
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
- OS: Linux
- Processor: 3.2 GHz
- Memory: 8 GB
- Storage: 500 GB
- Operating system: Linux, Windows or Mac
- Browser: Chrome (Latest Version)
- Node.js Installed