CNTK – In-Memory and Large Datasets
In this chapter, we will learn about how to work with the in-memory and large datasets in CNTK. Training with small in memory datasets When we talk about feeding data…
In this chapter, we will learn about how to work with the in-memory and large datasets in CNTK. Training with small in memory datasets When we talk about feeding data…
Here, we will understand about training the Neural Network in CNTK. Training a model in CNTK In the previous section, we have defined all the components for the deep learning…
This chapter will elaborate on creating a neural network in CNTK. Build the network structure In order to apply CNTK concepts to build our first NN, we are going to…
This chapter deals with concepts of Neural Network with regards to CNTK. As we know that, several layers of neurons are used for making a neural network. But, the question…
This chapter deals with constructing a logistic regression model in CNTK. Basics of Logistic Regression model Logistic Regression, one of the simplest ML techniques, is a technique especially for binary…
In this chapter, we will learn in detail about the sequences in CNTK and its classification. Tensors The concept on which CNTK works is tensor. Basically, CNTK inputs, outputs as well…
Microsoft Cognitive Toolkit offers two different build versions namely CPU-only and GPU-only. CPU only build version The CPU-only build version of CNTK uses the optimised Intel MKLML, where MKLML is…
Here, we will understand about the installation of CNTK on Windows and on Linux. Moreover, the chapter explains installing CNTK package, steps to install Anaconda, CNTK files, directory structure and…
Microsoft Cognitive Toolkit (CNTK), formerly known as Computational Network Toolkit, is a free, easy-to-use, open-source, commercial-grade toolkit that enables us to train deep learning algorithms to learn like the human…
It displays the total work remaining to achieve the sprint goal for a given time to sprint. It helps the team to manage the progress and respond accordingly. This chart…