ML 1.6 Overview of Popular Machine Learning Frameworks: TensorFlow 2.0 with Keras API

Which is popular framework for Machine Learning ?

TensorFlow is one of the most popular open-source deep learning frameworks and extensively used in the field. It is basically a Python library and is often preferred for its scalability and production-ready capabilities. There are other frameworks like PyTorch, which is developed by Facebook's AI Research lab (FAIR).

In addition to its ease of use, TensorFlow's Python API leverages the rich ecosystem of Python libraries, making it highly beneficial for data preprocessing, visualization, and data manipulation tasks that often accompany machine learning projects.

The TensorFlow Java API provides a bridge between the Java programming language and TensorFlow's capabilities, enabling you to integrate deep learning models into your Java applications. It is especially useful if you have a Java-based project and prefer to work with Java for your entire codebase.

What is the Latest Version of TensorFlow?

TensorFlow 2.0 was a major update aimed at improving usability and developer experience. It included a more user-friendly and intuitive API called "tf.keras," which was integrated with the Keras high-level neural networks API. This integration made building and training neural networks in TensorFlow more straightforward and consistent.

What is Keras?

In simple terms, Keras provides a simplified and easy-to-use interface for developers to build and train neural networks, abstracting away the complexities of low-level operations and mathematical details. It serves as a powerful tool that empowers developers to work with deep learning models more efficiently and effectively.

Keras is an open-source high-level neural networks API written in Python. It provides a simple and intuitive interface for building and training neural networks. Developers can define neural network architectures using high-level building blocks like layers and activation functions, abstracting away the complexities of low-level operations.

How is Keras Integrated with TensorFlow 2.0?

While Keras can run on different backends, it has become tightly integrated with TensorFlow since TensorFlow version 2.0. In fact, Keras has been adopted as the high-level API of TensorFlow, known as "tf.keras," making it the recommended way to build neural networks using TensorFlow.

• Can execute operations immediately like regular Python code
• Provides a wide range of pre-built layers and model architectures
• Simplifies the process of defining model architecture and training neural networks with the help of Keras
• Fully supports GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). Both GPUs and TPUs are specialized hardware components designed to perform rapid and efficient processing of graphical and computational tasks. TPUs are specifically designed by Google for machine learning workloads

Overall, TensorFlow 2.0 is a powerful and versatile deep learning framework that covers all aspects of machine learning, from building and training models to deployment. With its user-friendly API, extensive documentation, and active community support, TensorFlow 2.0 is a popular choice for both beginners and experienced ML practitioners.

Links to other related topics:

ML 1.1 Job Elimination on the Horizon, but We're Not There Yet !

ML 1.2 An Introductory Look at Machine Learning with its variants !!!

ML 1.3 Exploring the Fame and Power of Generative AI in the World of Machine Learning

ML 1.4 The Perceptron: Foundation of deep learning

ML 1.5 Fundamental Concepts in Deep Learning