Keras - The High-Level Neural Networks API for Fast Experimentation

Overview:

         Keras is a high-level, user-friendly API used for building and training neural networks. It is designed to be user-friendly, modular, and easy to extend. Keras allows you to build, train, and deploy deep learning models with minimal code. It provides a high-level API that is intuitive and easy to use, making it ideal for beginners and experts alike.

Users:

         Keras is used by a diverse group of users, including developers, researchers, and professionals across various industries, including information technology, education, financial services, and health care.

Goal:

         The primary goal of Keras is to enable fast experimentation with deep neural networks. It was developed with a focus on enabling fast experimentation.

Uses and Features:

1. User-Friendly: Keras offers a simple, consistent interface that minimizes the cognitive load, making it easy to learn and use.
2. Modular and Extendable: Keras is designed to be modular and easy to extend, allowing users to create custom layers, loss functions, and preprocessing tasks.
3. Cross-Platform Compatibility: Keras runs on top of popular deep learning frameworks like TensorFlow, Theano, and CNTK, providing a consistent experience across platforms.
4. Scalability and Performance: Keras leverages the scalability and cross-platform capabilities of TensorFlow, making it suitable for both small-scale experiments and large-scale production systems.
5. Fast Experimentation: Keras allows for rapid prototyping and testing of different neural network architectures, making it ideal for exploratory work.

How it Works:

1. Building the Model: Define a class for the model that extends the tf.keras.Model class and define the layers in the constructor.
2. Constructing the Computation Graph: The computation graph is built on-the-fly as operations are performed.
3. Training the Model: Use the automatic differentiation capabilities of Keras for backpropagation.
4. Evaluating the Model: After training, the model can be evaluated on the test data.

Customer Companies Statistics:

1. Google: Keras is one of the key building blocks in YouTube Discovery's new modeling infrastructure.
2. Waymo: Keras has tremendously simplified the development workflow of Waymo's ML practitioners.
3. Hugging Face: Keras allows Hugging Face to prototype, research, and deploy deep learning models in an intuitive and streamlined manner.
4. Rune Labs: Keras allows Rune Labs to prototype, research, and deploy deep learning models in an intuitive and streamlined manner.
5. Amazon: Keras has something for every user at Amazon: easy customizability for the academic; out-of-the-box, performant models and pipelines for use by the industry, and readable, modular code for the student.