Open Source tools are an excellent choice for getting started with Machine learning. This article covers some of the top ML frameworks and tools.
Keras is an open source neural network library that is written in the Python language. It should be noted that it is capable of running on top of other frameworks/software libraries, such as Microsoft Cognitive Toolkit, TensorFlow, and Theano. For those who don’t know, a neural network is actually a computing system that is meant to imitate the neural activity of animal brains, and is a collection of nodes called “artificial neurons”.
Keras as a neural network helps to optimize a lot of functions, and is used to make working with image and text data infinitely easier. This could help to improve productivity for countless platforms and companies who work with data. It is a tool that caters to the recent technological trend of deep learning, where companies are using AI to optimize their companies and find out more information about their consumer base, such as predictive trends or overall consumer trends.
Keras has over 200,000 users already, and was recently the 10th most cited tool in the 2018 Nuggets 2018 software poll, which indicates that it is rising in popularity and relevancy in the tech sector. It ultimately helps many companies experiment faster with certain processes, as well.Hire Keras Specialists
Deep learning models with 1D, 2D, and 3D data. RNN VAE knowledge
I have created a Unet colorization autoencoder. The color space that I work on is Ciel Lab. The network accepts the luminance channel and predicts the a and b channel. In the so far created model, I would like to add some custom loss and metrics. The custom loss is the Perceptual loss and metric is the PSNR to be applied. I am looking for a customization in an existing model.