Elton  Bogan

Elton Bogan

1601388000

A Layman’s Guide to Building Your First Image Classification Model in R Using Keras

Applications of machine learning (ML) are now almost an integral part of our everyday life. From a speech-recognition based virtual assistant in our smartphones to super-intelligent automated drones, ML and artificial intelligence (AI) is revolutionizing the dynamics of human-machine interactions. AI algorithms, especially the convolution neural networks (CNN) have made computer vision extremely powerful than ever. While the applications of it are breathtakingly awesome, it could be very intimidating to build one’s own CNN model, especially for a non-programmer or a beginner in data science, in general. As an R lover, it was not difficult for me to assert that it gets even more enigmatic for a novice R programmer. The plausible reason for this imbalance could be that the standard neural network and ML libraries (like Keras and Tensorflow) are primarily compatible with Python and naturally gravitate the masses to roll with Python itself, leading to a severe lack of novice’s guide and documentation to facilitate the implementation of these sophisticated frameworks in R. Nevertheless, APIs of Keras and Tensorflow is now available on CRAN. Herein, we are going to make a CNN based vanilla image-classification model using Keras and Tensorflow framework in R. With this article, my goal is to enable you to conceptualize and build your own CNN models in R using Keras and, sequentially help to boost your confidence through hands-on coding to build even more complex models in the future using this profound API. Apart from the scripting of the model, I will also try as much to concisely elaborate on the necessary components while plunging the hardcore underlying mathematics. Now let’s start.

#r #convolution-neural-net #modeling #image-classification #machine-learning

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A Layman’s Guide to Building Your First Image Classification Model in R Using Keras
Elton  Bogan

Elton Bogan

1601388000

A Layman’s Guide to Building Your First Image Classification Model in R Using Keras

Applications of machine learning (ML) are now almost an integral part of our everyday life. From a speech-recognition based virtual assistant in our smartphones to super-intelligent automated drones, ML and artificial intelligence (AI) is revolutionizing the dynamics of human-machine interactions. AI algorithms, especially the convolution neural networks (CNN) have made computer vision extremely powerful than ever. While the applications of it are breathtakingly awesome, it could be very intimidating to build one’s own CNN model, especially for a non-programmer or a beginner in data science, in general. As an R lover, it was not difficult for me to assert that it gets even more enigmatic for a novice R programmer. The plausible reason for this imbalance could be that the standard neural network and ML libraries (like Keras and Tensorflow) are primarily compatible with Python and naturally gravitate the masses to roll with Python itself, leading to a severe lack of novice’s guide and documentation to facilitate the implementation of these sophisticated frameworks in R. Nevertheless, APIs of Keras and Tensorflow is now available on CRAN. Herein, we are going to make a CNN based vanilla image-classification model using Keras and Tensorflow framework in R. With this article, my goal is to enable you to conceptualize and build your own CNN models in R using Keras and, sequentially help to boost your confidence through hands-on coding to build even more complex models in the future using this profound API. Apart from the scripting of the model, I will also try as much to concisely elaborate on the necessary components while plunging the hardcore underlying mathematics. Now let’s start.

#r #convolution-neural-net #modeling #image-classification #machine-learning

Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Keras Tutorial

Introduction to Keras

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Characteristics of Keras

Keras has the following characteristics:

  • It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
  • Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
  • Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
  • It offers a consistent API that provides necessary feedback when an error occurs.
  • Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

Benefits of using Keras

The following major benefits of using Keras over other deep learning frameworks are:

  • The simple API structure of Keras is designed for both new developers and experts.
  • The Keras interface is very user friendly and is pretty optimized for general use cases.
  • In Keras, you can write custom blocks to extend it.
  • Keras is the second most popular deep learning framework after TensorFlow.
  • Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Keras Installation

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

Starting with Keras

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

I am Developer

1597469369

Crop and Resize Image Before Upload In Laravel Using with jQuery Copper JS

Crop and resize image size before upload in laravel using jquery copper js. In this post, i will show you how to crop and resize image size in laravel using jQuery copper js in laravel.

This laravel crop image before upload using cropper js looks like:

laravel crop image before upload

Laravel Crop Image Before Uploading using Cropper js Tutorial

Laravel crop image before upload tutorial, follow the following steps and learn how to use cropper js to crop image before uploading in laravel app:

  • Step 1: Install New Laravel App
  • Step 2: Add Database Details
  • Step 3: Create Migration & Model
  • Step 4: Add Route
  • Step 5: Create Controller By Artisan
  • Step 6: Create Blade View
  • Step 7: Make Upload Directory
  • Step 8: Start Development Server

Read More => https://www.tutsmake.com/laravel-crop-image-before-upload-using-jquery-copper-js/

Live Demo Laravel Crop image Before Upload.

#laravel crop image before upload, #laravel crop and resize image using cropper.js #ajax image upload and crop with jquery and laravel #crop and upload image ajax jquery laravel #crop image while uploading with jquery laravel #image crop and upload using jquery with laravel ajax

Keras Models - Types and Examples

A model is the basic data structure of Keras. Keras models define how to organize layers. In this article, we will discuss Keras Models and its two types with examples. We will also learn about Model subclassing through which we can create our own fully-customizable models.

Types of Keras Models

Models in keras are available in two types:

  • Keras Sequential Model
  • Keras Functional API

#keras tutorials #functional api in keras #keras models #models in keras

Aayush Singh

Aayush Singh

1607579145

Keras Tutorial For Beginners | What is Keras | Keras Sequential Model | Keras Training

In this video on Keras, you will understand what is Keras and why do we need it, how to compose different models in Keras like the Sequential model and functional model, and later on how to define the inputs, how to connect layers over, and finally hands-on demo.
Why Keras is important

Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast, and easy to use. Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Keras can help you with that.

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#keras tutorial for beginners #what is keras #keras sequential model #keras training