Kawsar  Ahmed

Kawsar Ahmed

1612547100

Feature Ranking in Keras and Scikit-Learn: Perturbation Ranking

Perturbation Ranking will tell which imports are the most important for any machine learning model, such as a deep neural network. The provided code work with TensorFlow and Keras. Because Perturbation ranking uses no internal model information (only results from generated inputs), it can be used with any classification or regression model.

Code for this video: https://github.com/drcannady/pub/tree/master/ijcnn-2017

Subscribe: https://www.youtube.com/channel/UCR1-GEpyOPzT2AO4D_eifdw

#keras #tensorflow #python

What is GEEK

Buddha Community

Feature Ranking in Keras and Scikit-Learn: Perturbation Ranking

Why Learn Keras - Reasons Why Choose Keras - DataFlair

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.

Why learn Keras

Why Learn Keras?

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.

Features of Keras

Let us see some of the features of keras that make you learn Keras.

1. Simple API

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.

2. Pythonic Nature

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.

3. Strong Backend Support

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.

4. Base for Innovations

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:

  • Deepjazz: This is deep learning-driven jazz built using Keras and theano, available on github.
  • Eclipse Picasso: It is a visualization tool that works with Keras checkpoints.
  • Auto Keras: It is built upon Keras and used for machine learning model automation.

Reasons to Learn and Use Keras

  • Keras allows us to switch between the backends as per the requirement of our applications. It acts as a wrapper that gives us the privilege to use either TensorFlow, theano, or any other framework.
  • Keras is very easy and enjoyable to use. It uses great guiding principles like extensibility, python nativeness, and modularity.
  • The ability of Keras to create the state of the art implementations of common deep neural networks. These are fast and it is easy to get them running using Keras.
  • Being Keras user, you will be more faster and productive, you will have the ability to try more ideas.
  • Keras provides Multi-GPU and strong distributed support. We can run our deep learning models on large GPU clusters.
  • We can deploy Keras deep learning models on multiple platforms. For example, We can deploy in the browser using tensorflow.js, on the server using either TensorFlow serving or using Node.js runtime. On mobile devices i.e in android or IOS, we can deploy using TensorFlow Lite.
  • Keras has a large ecosystem of products to support your deep learning development. Some of the popular products are Tensorflow Cloud, Keras Tuner, Tensorflow Lite,Tensorflow.js, and Tensorflow Model Optimizatio

#keras tutorials #importance of keras #keras features #learn keras #deep learning

Michael  Hamill

Michael Hamill

1618278600

Scikit-Learn Is Still Rocking, Been Introduced To French President

Amilestone for open source projects — French President Emmanuel Macron has recently been introduced to Scikit-learn. In fact, in a recent tweet, Scikit-learn creator and Inria tenured research director, Gael Varoquaux announced the presentation of Scikit-Learn, with applications of machine learning in digital health, to the president of France.

He stated the advancement of this free software machine learning library — “started from the grassroots, built by a community, we are powering digital revolutions, adding transparency and independence.”

#news #application of scikit learn for machine learning #applications of scikit learn for digital health #scikit learn #scikit learn introduced to french president

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

Oleta  Becker

Oleta Becker

1603198800

Feature Ranking with Recursive Feature Elimination in Scikit-Learn

Feature selection is an important task for any machine learning application. This is especially crucial when the data in question has many features. The optimal number of features also leads to improved model accuracy. Obtaining the most important features and the number of optimal features can be obtained via feature importance or feature ranking. In this piece, we’ll explore feature ranking.

Recursive Feature Elimination

The first item needed for recursive feature elimination is an estimator; for example, a linear model or a decision tree model.

These models have coefficients for linear models and feature importances in decision tree models. In selecting the optimal number of features, the estimator is trained and the features are selected via the coefficients, or via the feature importances. The least important features are removed. This process is repeated recursively until the optimal number of features is obtained.

Application in Sklearn

Scikit-learn makes it possible to implement recursive feature elimination via the sklearn.feature_selection.**RFE**class. The class takes the following parameters:

  • estimator — a machine learning estimator that can provide features importances via the coef_ or feature_importances_ attributes.
  • n_features_to_select — the number of features to select. Selects half if it’s not specified.
  • step — an integer that indicates the number of features to be removed at each iteration, or a number between 0 and 1 to indicate the percentage of features to remove at each iteration.

Once fitted, the following attributes can be obtained:

  • ranking_ — the ranking of the features.
  • n_features_ — the number of features that have been selected.
  • support_ — an array that indicates whether or not a feature was selected.

#overviews #feature selection #machine learning #python #scikit-learn

Vaughn  Sauer

Vaughn Sauer

1622792520

Top Free Resources To Learn Scikit-Learn

Scikit-Learn is one of the popular software machine learning libraries. The library is built on top of NumPy, SciPy, and Matplotlib and supports supervised and unsupervised learning as well as provides various tools for model fitting, data preprocessing, model selection and evaluation.

Scikit-Learn Tutorials

About: From the developers of Scikit-Learn, this tutorial provides an introduction to machine learning with Scikit-Learn. It includes topics such as problem setting, loading an example dataset, learning and predicting. The tutorial is suitable for both beginners and advanced students.

Perform Sentiment Analysis with Scikit-Learn

**About: **In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. You will learn how to develop and employ a logistic regression classifier using Scikit-Learn, perform feature extraction with The Natural Language Toolkit (NLTK), tune model hyperparameters and evaluate model accuracy etc.

Python Machine Learning: Scikit-Learn Tutorial

**About: **Python Machine Learning: Scikit-Learn tutorial will help you learn the basics of Python machine learning. You will learn how to use Python and its libraries to explore your data with the help of Matplotlib and Principal Component Analysis (PCA). You will also learn how to work with the KMeans algorithm to construct an unsupervised model, fit this model to your data, predict values, and validate the model.

Scikit Learn Tutorial | Machine Learning with Python

**About: **Edureka’s video tutorial introduces machine learning in Python. It will take you through regression and clustering techniques along with a demo of SVM classification on the famous iris dataset. This video helps you to learn the introduction to Scikit-learn and how to install it, understand how machine learning works, among other things.

Regression using Scikit-Learn

About: In this Coursera offering, you will learn about Linear Regression, Regression using Random Forest Algorithm, Regression using Support Vector Machine Algorithm. Scikit-Learn provides a comprehensive array of tools for building regression models.

Machine Learning with Scikit-Learn Tutorial

About: In this course, you will learn about machine learning, algorithms, and how Scikit-Learn makes it all so easy. You will get to know the machine learning approach, jargons to understand a dataset, features of supervised and unsupervised learning models, algorithms such as regression, classification, clustering, and dimensionality reduction.

Predict Sales Revenue with Scikit-Learn

About: In this two-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the Scikit-Learn module for calculating the linear regression while using pandas for data management and seaborn for plotting. By the end of this course, you will be able to build a simple linear regression model in Python with Scikit-Learn, employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas.

SciPy 2016 Scikit-learn Tutorial

**About: **This tutorial is available on GitHub. It includes an introduction to machine learning with sample applications, data formats, preparation and representation, supervised learning: training and test data, the Scikit-Learn estimator interface and more.

Build NLP pipelines using Scikit-Learn

About: This is a two-hour long project-based course, where you will understand the business problem and the dataset and learn how to generate a hypothesis to create new features based on existing data. You will learn to perform text pre-processing and create custom transformers to generate new features. You will also learn to implement an NLP pipeline, create custom transformers and build a text classification model.

#developers corner #learn scikit-learn #machine learning library #scikit learn