#Amazon AWS #DeepComposer is “the world’s first #MachineLearning -enabled musical keyboard”
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MusicRadar
Amazon AWS DeepComposer is “the world’s first machine learning-enabled musical keyboard”
Use Generative AI to create a complete track out of a simple melody
How to Use Out-of-Fold Predictions in #MachineLearning
Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation.
During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are referred to as out-of-fold predictions, a type of out-of-sample predictions.
Out-of-fold predictions play an important role in machine learning in both estimating the performance of a model when making predictions on new data in the future, so-called the generalization performance of the model, and in the development of ensemble models.
In this tutorial, you will discover a gentle introduction to out-of-fold predictions in machine learning.
After completing this tutorial, you will know:
*Out-of-fold predictions are a type of out-of-sample predictions made on data not used to train a model.
* Out-of-fold predictions are most commonly used to estimate the performance of a model when making predictions on unseen data.
*Out-of-fold predictions can be used to construct an ensemble model called a stacked generalization or stacking ensemble.
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🔭 @DeepGravity
Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation.
During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are referred to as out-of-fold predictions, a type of out-of-sample predictions.
Out-of-fold predictions play an important role in machine learning in both estimating the performance of a model when making predictions on new data in the future, so-called the generalization performance of the model, and in the development of ensemble models.
In this tutorial, you will discover a gentle introduction to out-of-fold predictions in machine learning.
After completing this tutorial, you will know:
*Out-of-fold predictions are a type of out-of-sample predictions made on data not used to train a model.
* Out-of-fold predictions are most commonly used to estimate the performance of a model when making predictions on unseen data.
*Out-of-fold predictions can be used to construct an ensemble model called a stacked generalization or stacking ensemble.
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🔭 @DeepGravity
Free #AI #Resources
Find The Most Updated and Free #ArtificialIntelligence, #MachineLearning, #DataScience, #DeepLearning, #Mathematics, #Python Programming Resources. (Last Update: December 4, 2019)
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Find The Most Updated and Free #ArtificialIntelligence, #MachineLearning, #DataScience, #DeepLearning, #Mathematics, #Python Programming Resources. (Last Update: December 4, 2019)
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MarkTechPost
Free AI/ Data Science Resources
Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python, R Programming Resources.
#MachineLearning and the physical sciences
ABSTRACT
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
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🔭 @DeepGravity
ABSTRACT
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.
Paper
🔭 @DeepGravity
Reviews of Modern Physics
Machine learning and the physical sciences
In October 2018 an APS Physics Next Workshop on Machine Learning was held in Riverhead, NY. This article reviews and summarizes the proceedings of this very broad, emerging field.This needs to be a placard in the left-hand column, with a custom tag.
#DeepSpeech 0.6: Mozilla’s #Speech_to_Text Engine Gets Fast, Lean, and Ubiquitous
The #MachineLearning team at #Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. DeepSpeech is a deep learning-based ASR engine with a simple API. We also provide pre-trained English models.
Our latest release, version v0.6, offers the highest quality, most feature-packed model so far. In this overview, we’ll show how DeepSpeech can transform your applications by enabling client-side, low-latency, and privacy-preserving speech recognition capabilities.
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🔭 @DeepGravity
The #MachineLearning team at #Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. DeepSpeech is a deep learning-based ASR engine with a simple API. We also provide pre-trained English models.
Our latest release, version v0.6, offers the highest quality, most feature-packed model so far. In this overview, we’ll show how DeepSpeech can transform your applications by enabling client-side, low-latency, and privacy-preserving speech recognition capabilities.
Link
🔭 @DeepGravity
Mozilla Hacks – the Web developer blog
DeepSpeech 0.6: Mozilla’s Speech-to-Text Engine Gets Fast, Lean, and Ubiquitous
The Machine Learning team at Mozilla continues work on DeepSpeech, an automatic speech recognition (ASR) engine which aims to make speech recognition technology and trained models openly available to developers. ...
Tune #Hyperparameters for Classification #MachineLearning Algorithms
The seven classification algorithms we will look at are as follows:
Logistic Regression
Ridge Classifier
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Bagged Decision Trees (Bagging)
Random Forest
Stochastic Gradient Boosting
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The seven classification algorithms we will look at are as follows:
Logistic Regression
Ridge Classifier
K-Nearest Neighbors (KNN)
Support Vector Machine (SVM)
Bagged Decision Trees (Bagging)
Random Forest
Stochastic Gradient Boosting
Article
🔭 @DeepGravity
The Pros and Cons of Using #JavaScript for #MachineLearning
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
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🔭 @DeepGravity
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
Link
🔭 @DeepGravity
DLabs
The Pros and Cons of Using JavaScript for Machine Learning - DLabs
There’s a misconception in the world of machine learning (ML) Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. Python and Java often top the list. Python for its simplicity:…
Deep Speech, a good #Persian podcasts about #AI
We will talk about #ArtificialIntelligence, #MachineLearning and DeepLearning news.
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🔭 @DeepGravity
We will talk about #ArtificialIntelligence, #MachineLearning and DeepLearning news.
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Castbox
Deep Speech | Listen Free on Castbox.
We will talk about artificial intelligence, machine learning and deep learning news.Millions of podcasts for all topics. Listen to the best free podcast...
#MachineLearning Algorithm Cheat Sheet for #Azure Machine Learning designer
#Microsoft
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#Microsoft
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Docs
Machine Learning Algorithm Cheat Sheet - designer - Azure Machine Learning
A printable Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm for your predictive model in Azure Machine Learning designer.
Prediction of Physical Load Level by #MachineLearning Analysis of Heart Activity after Exercises
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Improving Out-of-Distribution Detection in #MachineLearning Models
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#Google Research
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#Google Research
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Google AI Blog
Improving Out-of-Distribution Detection in Machine Learning Models
Posted by Jie Ren, Research Scientist, Google Research and Balaji Lakshminarayanan, Research Scientist, DeepMind Successful deployment o...