In this article I show how to build a neural network from scratch. The example is simple and short to make it easier to understand but I haven't took any shortcuts to hide details. First we create some random data. The parameters are initialized using normal distribution where mean is 0 and variance 1. The neural network contains two linear functions and one non-linear function between them.
Keras is a deep learning framework that sits on top of backend frameworks like TensorFlow. Keras is excellent because it allows you to experiment with different neural-nets with great speed! It sits atop other excellent frameworks like TensorFlow, and lends well to the experienced as well as to novice data scientists! It doesn't require nearly as much code to get up and running! Keras provides you with the flexibility to build all types of architectures; that could be recurrent neural networks, convolutional neural networks, simple neural networks, deep neural networks, etc.
Dave Burke, VP of engineering at Google, announced a new version of Tensorflow optimised for mobile phones. This new library, called Tensorflow Lite, would enable developers to run their artificial intelligence applications in real time on the phones of users. According to Burke, the library is designed to be fast and small while still enabling state-of-the-art techniques. It will be released later this year as part of the open source Tensorflow project. At the moment, most artificial intelligence processing happens on servers of software as a service providers.
TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research.