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.จ้าง Keras Specialists
Fourier series, analysis of vibration single and then recognition or classification to be done in kerasR or tensorflow
This is for non-business use. I have multiple similar Excel files. They have currently 9 input columns (side by side and in same position) and 1-3 output columns. Python needs to read these inputs and predict missing outputs and write them to Excel. I can install whatever you need for Python (I have Anaconda already installed), but I prefer Keras and TensorFlow. Requirements: - Read certain E...
Part 1: Build a convolution neural network in TS Keras from existing PyTorch code (45 USD) Part 2: Employ Invasive weed optimization on resulting model (155 USD) See attached description