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
#keras #tensorflow #python
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
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
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
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.
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.
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
n_features_to_select— the number of features to select. Selects
halfif 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
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.
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.
**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.
**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.
**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.
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.
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.
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.
**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.
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