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Exploring different optimization algorithms

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Machine learning is a field of study in the broad spectrum of artificial intelligence (AI) that can make predictions using data without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as recommendation engines, computer vision, spam filtering and so much more. They perform extraordinary well where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data-- over and over, faster and faster -- is a recent development. One of the most overwhelmingly represented machine learning techniques is a neural network.


Automated Machine Learning (AutoML) Libraries for Python - AnalyticsWeek

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AutoML provides tools to automatically discover good machine learning model pipelines for a dataset with very little user intervention. It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling task. Open-source libraries are available for using AutoML methods with popular machine learning libraries in Python, such as the scikit-learn machine learning library. In this tutorial, you will discover how to use top open-source AutoML libraries for scikit-learn in Python. Automated Machine Learning (AutoML) Libraries for Python Photo by Michael Coghlan, some rights reserved.


Automated Machine Learning (AutoML) Libraries for Python

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AutoML provides tools to automatically discover good machine learning model pipelines for a dataset with very little user intervention. It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling task. Open-source libraries are available for using AutoML methods with popular machine learning libraries in Python, such as the scikit-learn machine learning library. In this tutorial, you will discover how to use top open-source AutoML libraries for scikit-learn in Python. Automated Machine Learning (AutoML) Libraries for Python Photo by Michael Coghlan, some rights reserved.


Scikit-Optimize for Hyperparameter Tuning in Machine Learning

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Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset. There are many ways to perform hyperparameter optimization, although modern methods, such as Bayesian Optimization, are fast and effective. The Scikit-Optimize library is an open-source Python library that provides an implementation of Bayesian Optimization that can be used to tune the hyperparameters of machine learning models from the scikit-Learn Python library. You can easily use the Scikit-Optimize library to tune the models on your next machine learning project. In this tutorial, you will discover how to use the Scikit-Optimize library to use Bayesian Optimization for hyperparameter tuning.


Deep Learning for Weather Classification

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The main aim of this project is to make a model that correctly classifies the weather states on the images it sees. This seems pretty easy but the main challenge the model will face is that it doesn't need to learn about the shapes of the objects in the image, i.e. Clouds, etc. Rather it needs to learn about the sky color in the image. But it does also need to learn where the clouds actually represent the Cloudy weather or are just there. This project is available on my Github Repo.


4 Python AutoML Libraries Every Data Scientist Should Know

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With the use of recent methods like Bayesian Optimization, the library is built to navigate the space of possible models and learns to infer if a specific configuration will work well on a given task. Created by Matthias Feurer, et al., the library's technical details are described in a paper, Efficient and Robust Machine Learning. In addition to discovering data preparation and model selections for a dataset, it learns from models that perform well on similar datasets. Top-performing models are aggregated in an ensemble. On top of an efficient implementation, auto-sklearn requires minimal user interaction.


3 Steps to Improve your Efficiency when Hypertuning ML Models

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You may hear about "no free lunch" (NFL) theorem, which indicates that there is no best algorithm for every data. One algorithm may perform well in one data but perform poorly in other data. That is why there are so many machine learning algorithms available to train data. How do we know which machine learning model is the best? We cannot know until we experiment and compare the performance of different models.


HyperOpt for Automated Machine Learning With Scikit-Learn

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Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. HyperOpt is an open-source library for large scale AutoML and HyperOpt-Sklearn is a wrapper for HyperOpt that supports AutoML with HyperOpt for the popular Scikit-Learn machine learning library, including the suite of data preparation transforms and classification and regression algorithms. In this tutorial, you will discover how to use HyperOpt for automatic machine learning with Scikit-Learn in Python. HyperOpt for Automated Machine Learning With Scikit-Learn Photo by Neil Williamson, some rights reserved. HyperOpt is an open-source Python library for Bayesian optimization developed by James Bergstra.


Visualizing TensorFlow training jobs with TensorBoard

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TensorBoard is an open source toolkit for TensorFlow users that allows you to visualize a wide range of useful information about your model, from model graphs; to loss, accuracy, or custom metrics; to embedding projections, images, and histograms of weights and biases. This post demonstrates how to use TensorBoard with Amazon SageMaker training jobs, write […]


6 Common Mistakes in Data Science and How To Avoid Them - KDnuggets

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In data science or machine learning, we use data for descriptive analytics to draw out meaningful conclusions from the data, or we can use data for predictive purposes to build models that can make predictions on unseen data. The reliability of any model depends on the level of expertise of the data scientist. It is one thing to build a machine learning model. It is another thing to ensure the model is optimal and of the highest quality. This article will discuss six common mistakes that can adversely influence the quality or predictive power of a machine learning model with several case studies included.