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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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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Free AI/ Data Science Resources
Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python, R Programming Resources.
Understanding #TransferLearning for #Medical Imaging
As #DeepNeuralNetworks are applied to an increasingly diverse set of domains, transfer learning has emerged as a highly popular technique in developing deep learning models. In transfer learning, the neural network is trained in two stages: 1) pretraining, where the network is generally trained on a large-scale benchmark dataset representing a wide diversity of labels/categories (e.g., ImageNet); and 2) fine-tuning, where the pretrained network is further trained on the specific target task of interest, which may have fewer labeled examples than the pretraining dataset. The pretraining step helps the network learn general features that can be reused on the target task.
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As #DeepNeuralNetworks are applied to an increasingly diverse set of domains, transfer learning has emerged as a highly popular technique in developing deep learning models. In transfer learning, the neural network is trained in two stages: 1) pretraining, where the network is generally trained on a large-scale benchmark dataset representing a wide diversity of labels/categories (e.g., ImageNet); and 2) fine-tuning, where the pretrained network is further trained on the specific target task of interest, which may have fewer labeled examples than the pretraining dataset. The pretraining step helps the network learn general features that can be reused on the target task.
Link
🔭 @DeepGravity
blog.research.google
Understanding Transfer Learning for Medical Imaging
Simplified Action Decoder for Deep Multi-Agent #ReinforcementLearning
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e., the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, #RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2-5 players on the self-play part of the Hanabi challenge. Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at https://github.com/facebookresearch/Hanabi_SAD.
Paper
🔭 @DeepGravity
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e., the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, #RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2-5 players on the self-play part of the Hanabi challenge. Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at https://github.com/facebookresearch/Hanabi_SAD.
Paper
🔭 @DeepGravity
#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.
Paper
🔭 @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
How to Implement #Pix2Pix #GAN Models From Scratch With #Keras
What Is the Pix2Pix GAN?
Pix2Pix is a #GenerativeAdversarialNetwork, or GAN, model designed for general purpose image-to-image translation.
The approach was presented by Phillip Isola, et al. in their 2016 paper noscriptd “Image-to-Image Translation with Conditional Adversarial Networks” and presented at CVPR in 2017.
The GAN architecture is comprised of a generator model for outputting new plausible synthetic images and a discriminator model that classifies images as real (from the dataset) or fake (generated). The discriminator model is updated directly, whereas the generator model is updated via the discriminator model. As such, the two models are trained simultaneously in an adversarial process where the generator seeks to better fool the discriminator and the discriminator seeks to better identify the counterfeit images.
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What Is the Pix2Pix GAN?
Pix2Pix is a #GenerativeAdversarialNetwork, or GAN, model designed for general purpose image-to-image translation.
The approach was presented by Phillip Isola, et al. in their 2016 paper noscriptd “Image-to-Image Translation with Conditional Adversarial Networks” and presented at CVPR in 2017.
The GAN architecture is comprised of a generator model for outputting new plausible synthetic images and a discriminator model that classifies images as real (from the dataset) or fake (generated). The discriminator model is updated directly, whereas the generator model is updated via the discriminator model. As such, the two models are trained simultaneously in an adversarial process where the generator seeks to better fool the discriminator and the discriminator seeks to better identify the counterfeit images.
Link
🔭 @DeepGravity
MachineLearningMastery.com
How to Implement Pix2Pix GAN Models From Scratch With Keras - MachineLearningMastery.com
The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products.…
Learning #ReinforcementLearning: #REINFORCE with #PyTorch!
Getting started with #PolicyGradients
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Getting started with #PolicyGradients
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Medium
Learning Reinforcement Learning: REINFORCE with PyTorch!
The REINFORCE algorithm is one of the first policy gradient algorithms in reinforcement learning and a great jumping off point to get into…
#Internship Opportunities: Researcher #ReinforcementLearning for #Game Intelligence
Cambridge, Cambridgeshire, United Kingdom, #Microsoft #AI and Research
This is an exceptional opportunity to drive ambitious research while collaborating with a diverse team. Key research challenges we are currently tackling include, but are not limited to, robustness and generalization in (#deep) #RL, multi-agent RL, sample-efficiency and scalability of RL algorithms. The focus and scope of internship projects considers the team’s direction as well as successful candidates’ experience and research interests.
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#Job
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Cambridge, Cambridgeshire, United Kingdom, #Microsoft #AI and Research
This is an exceptional opportunity to drive ambitious research while collaborating with a diverse team. Key research challenges we are currently tackling include, but are not limited to, robustness and generalization in (#deep) #RL, multi-agent RL, sample-efficiency and scalability of RL algorithms. The focus and scope of internship projects considers the team’s direction as well as successful candidates’ experience and research interests.
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#Job
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Microsoft
Internship Opportunities: Researcher Reinforcement Learning for Game Intelligence in Cambridge, Cambridgeshire, United Kingdom…
Apply for Internship Opportunities: Researcher Reinforcement Learning for Game Intelligence job with Microsoft in Cambridge, Cambridgeshire, United Kingdom. Research at Microsoft
#StatisticalModelling vs MachineLearning
At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.
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At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.
Link
🔭 @DeepGravity
KDnuggets
Statistical Modelling vs Machine Learning - KDnuggets
At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding…
#Transform-Invariant #ConvolutionalNeuralNetworks for Image #Classification and Search
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and non-linear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in #CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https://github.com/jasonustc/caffe-multigpu/tree/TICNN.
Paper
🔭 @DeepGravity
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and non-linear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in #CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https://github.com/jasonustc/caffe-multigpu/tree/TICNN.
Paper
🔭 @DeepGravity
GitHub
jasonustc/caffe-multigpu
linux && windows compatible caffe. Contribute to jasonustc/caffe-multigpu development by creating an account on GitHub.
#AI Transformation Playbook, How to lead your company into the AI era, by Andrew #Ng
This AI Transformation Playbook draws on insights gleaned from leading the #Google Brain team and the Baidu AI Group, which played leading roles in transforming both Google and Baidu into great AI companies. It is possible for any enterprise to follow this Playbook and become a strong AI company, though these recommendations are tailored primarily for larger enterprises with a market cap/valuation from $500M to $500B.
Link
🔭 @DeepGravity
This AI Transformation Playbook draws on insights gleaned from leading the #Google Brain team and the Baidu AI Group, which played leading roles in transforming both Google and Baidu into great AI companies. It is possible for any enterprise to follow this Playbook and become a strong AI company, though these recommendations are tailored primarily for larger enterprises with a market cap/valuation from $500M to $500B.
Link
🔭 @DeepGravity
When Does Label Smoothing Help?
Rafael Müller, Simon Kornblith, Geoffrey #Hinton
The #generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing has been used in many state-of-the-art models, including image classification, language translation and speech recognition. Despite its widespread use, label smoothing is still poorly understood. Here we show empirically that in addition to improving generalization, label smoothing improves model calibration which can significantly improve beam-search. However, we also observe that if a teacher network is trained with label smoothing, knowledge distillation into a student network is much less effective. To explain these observations, we visualize how label smoothing changes the representations learned by the penultimate layer of the network. We show that label smoothing encourages the representations of training examples from the same class to group in tight clusters. This results in loss of information in the logits about resemblances between instances of different classes, which is necessary for distillation, but does not hurt generalization or calibration of the model's predictions.
Paper
🔭 @DeepGravity
Rafael Müller, Simon Kornblith, Geoffrey #Hinton
The #generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing has been used in many state-of-the-art models, including image classification, language translation and speech recognition. Despite its widespread use, label smoothing is still poorly understood. Here we show empirically that in addition to improving generalization, label smoothing improves model calibration which can significantly improve beam-search. However, we also observe that if a teacher network is trained with label smoothing, knowledge distillation into a student network is much less effective. To explain these observations, we visualize how label smoothing changes the representations learned by the penultimate layer of the network. We show that label smoothing encourages the representations of training examples from the same class to group in tight clusters. This results in loss of information in the logits about resemblances between instances of different classes, which is necessary for distillation, but does not hurt generalization or calibration of the model's predictions.
Paper
🔭 @DeepGravity
#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. ...
Beyond #Accuracy: #Precision and #Recall
Precision is defined as the number of true positives divided by the number of true positives plus the number of false positives. False positives are cases the model incorrectly labels as positive that are actually negative, or in our example, individuals the model classifies as terrorists that are not. While recall expresses the ability to find all relevant instances in a dataset, precision expresses the proportion of the data points our model says was relevant actually were relevant.
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Precision is defined as the number of true positives divided by the number of true positives plus the number of false positives. False positives are cases the model incorrectly labels as positive that are actually negative, or in our example, individuals the model classifies as terrorists that are not. While recall expresses the ability to find all relevant instances in a dataset, precision expresses the proportion of the data points our model says was relevant actually were relevant.
Link
🔭 @DeepGravity
Medium
Beyond Accuracy: Precision and Recall
Choosing the right metrics for classification tasks
A Gentle Introduction to KFold Cross-Validation
KFold vs StratifiedKFold
Just a small notebook to point out that KFold and Stratified may not do what you think.
🔭 @DeepGravity
KFold vs StratifiedKFold
Just a small notebook to point out that KFold and Stratified may not do what you think.
🔭 @DeepGravity