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Keras modules provide various predefined classes and functions for deep learning algorithms. In this Keras tutorial, we will learn various modules in Keras. We will study the features and few of the applications of these modules.
Various Modules available in keras are:
#keras tutorials #keras modules #modules in keras #keras
1597811895
Keras modules provide various predefined classes and functions for deep learning algorithms. In this Keras tutorial, we will learn various modules in Keras. We will study the features and few of the applications of these modules.
Various Modules available in keras are:
#keras tutorials #keras modules #modules in keras #keras
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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 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.
Keras has the following characteristics:
The following major benefits of using Keras over other deep learning frameworks are:
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.
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.
It allows you to define more complex models.
#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras
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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.
Models in keras are available in two types:
#keras tutorials #functional api in keras #keras models #models in keras
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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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Link: https://www.youtube.com/watch?v=nS1J-2uoKto
#keras tutorial for beginners #what is keras #keras sequential model #keras training
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This article is the spotlight on the need for python deep learning library, Keras. Keras offers a uniform face for various deep learning frameworks including Tensorflow, Theano, and MXNet. Let us see why you should choose and learn keras now.
Keras makes deep learning accessible and local on your computer.It also acts as a frontend for other big cloud providers. It is the most voted recommendation for beginners who want to start their journey in machine learning. It provides a minimal approach to run neural networks. This allows students to learn complex features from input data sequentially.
Let us see some of the features of keras that make you learn Keras.
Keras is the most easy to use the library for machine learning for beginners. Being simple helps it to bring machine learning from imaginations to reality. It provides an infrastructure that can be learned in very less time. Using Keras, you will be able to stack layers like experts.
Python is the most popular library for machine learning and Data Science. The compatibility with python allows Keras to have many useful features. Writing less code, easy to debug, easy to deploy, extensibility is due to the support of Keras with python 2.7 and python 3.6.
Keras being a high-level API provides support for multiple popular and powerful backend frameworks. Tensorflow, theano, CNTK are very dominant for backend computations and Keras supports all of them.
The importance of Keras leads to many other innovative tools to explore deep learning. These tools are built on top of Keras making Keras as the base. The following tools are:
#keras tutorials #importance of keras #keras features #learn keras #deep learning