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Author: misamaliraza94

  • Hello world!

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    Welcome to WordPress. This is your first post. Edit or delete it, then start writing!

  • Defining Complex Networks

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    misamaliraza94
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    In the previous lesson, you learned to create a trivial network and learned how to execute it and examine its output. The process for creating complex networks is similar to the process described above. Caffe2 provides a huge set of operators for creating complex architectures. You are encouraged to examine the Caffe2 documentation for a…

  • Creating Your Own Network

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    misamaliraza94
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    In this lesson, you will learn to define a single layer neural network (NN) in Caffe2 and run it on a randomly generated dataset. We will write code to graphically depict the network architecture, print input, output, weights, and bias values. To understand this lesson, you must be familiar with neural network architectures, its terms and mathematics used in them. Network Architecture…

  • Image Classification Using Pre-Trained Model

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    misamaliraza94
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    In this lesson, you will learn to use a pre-trained model to detect objects in a given image. You will use squeezenet pre-trained module that detects and classifies the objects in a given image with a great accuracy. Open a new Juypter notebook and follow the steps to develop this image classification application. Importing Libraries First, we import the…

  • Verifying Access to Pre-Trained Models

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    misamaliraza94
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    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

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    misamaliraza94
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    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

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    misamaliraza94
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    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

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    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

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    misamaliraza94
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    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

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    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…