TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming.

On Google Cloud Platform, Cloud ML Engine provides serverless machine learning, for training, hyperparameter optimization and predictions. Until recently, that was only for TensorFlow. Recently, though, the team has implemented all three capabilities for scikit-learn. In this article, I will take it for a spin. The problem is to predict the weight of a baby given some information about the pregnancy.

TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming.

Learn how to to scale up a machine learning algorithm. The cloud and machine learning: two phrases with a lot of hype that few people understand. We're intimately familiar with both here at Leverege, so hopefully this article will shed some light on the two topics. Before we share what we've learned using Google Cloud ML Engine, we need to do a quick refresher on how machine learning is done in production. So how does Google Cloud ML fit into all of this?