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  • Verifying Access to Pre-Trained Models

    Before you learn to use a pre-trained model in your Python application, let us first verify that the models are installed on your machine and are accessible through the Python code. When you install Caffe2, the pre-trained models are copied in the installation folder. On the machine with Anaconda installation, these models are available in…

  • Installation

    Now, that you have got enough insights on the capabilities of Caffe2, it is time to experiment Caffe2 on your own. To use the pre-trained models or to develop your models in your own Python code, you must first install Caffe2 on your machine. On the installation page of Caffe2 site which is available at…

  • Introduction

    Last couple of years, Deep Learning has become a big trend in Machine Learning. It has been successfully applied to solve previously unsolvable problems in Vision, Speech Recognition and Natural Language Processing (NLP). There are many more domains in which Deep Learning is being applied and has shown its usefulness. Caffe (Convolutional Architecture for Fast Feature Embedding) is…

  • Caffe2 Tutorial

    In this tutorial, we will learn how to use a deep learning framework named Caffe2 (Convolutional Architecture for Fast Feature Embedding). Moreover, we will understand the difference between traditional machine learning and deep learning, what are the new features in Caffe2 as compared to Caffe and the installation instructions for Caffe2. Audience This tutorial is designed for…

  • AutoML

    To use AutoML, start a new Jupyter notebook and follow the steps shown below. Importing AutoML First import H2O and AutoML package into the project using the following two statements −import h2o from h2o.automl import H2OAutoML Initialize H2O Initialize h2o using the following statement −h2o.init() You should see the cluster information on the screen as…

  • Running Sample Application

    Click on the Airlines Delay Flow link in the list of samples as shown in the screenshot below − After you confirm, the new notebook would be loaded. Clearing All Outputs Before we explain the code statements in the notebook, let us clear all the outputs and then run the notebook gradually. To clear all…

  • H2O – Flow

    In the last lesson, you learned to create H2O based ML models using command line interface. H2O Flow fulfils the same purpose, but with a web-based interface. In the following lessons, I will show you how to start H2O Flow and to run a sample application. Starting H2O Flow The H2O installation that you downloaded…

  • Installation

    H2O can be configured and used with five different options as listed below −H2O – InstallationH2O can be configured and used with five different options as listed below − In our subsequent sections, you will see the instructions for installation of H2O based on the options available. You are likely to use one of the…

  • Introduction

    Have you ever been asked to develop a Machine Learning model on a huge database? Typically, the customer will provide you the database and ask you to make certain predictions such as who will be the potential buyers; if there can be an early detection of fraudulent cases, etc. To answer these questions, your task…

  • H2O Tutorial

    H2O is an open source Machine Learning framework with full-tested implementations of several widely-accepted ML algorithms. You just have to pick up the algorithm from its huge repository and apply it to your dataset. It contains the most widely used statistical and ML algorithms. H2O provides an easy-to-use open source platform for applying different ML…

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