Curriculum for #ReinforcementLearning
A curriculum is an efficient tool for humans to progressively learn from simple concepts to hard problems. It breaks down complex knowledge by providing a sequence of learning steps of increasing difficulty. In this post, we will examine how the idea of curriculum can help reinforcement learning models learn to solve complicated tasks.
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A curriculum is an efficient tool for humans to progressively learn from simple concepts to hard problems. It breaks down complex knowledge by providing a sequence of learning steps of increasing difficulty. In this post, we will examine how the idea of curriculum can help reinforcement learning models learn to solve complicated tasks.
Article
🔭 @DeepGravity
Lil'Log
Curriculum for Reinforcement Learning
A curriculum is an efficient tool for humans to progressively learn from simple concepts to hard problems. It breaks down complex knowledge by providing a sequence of learning steps of increasing difficulty. In this post, we will examine how the idea of curriculum…
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective
We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
Paper
🔭 @DeepGravity
We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
Paper
🔭 @DeepGravity
What is Game Theory?
... game theory can easily become one of the strongest fields in the following decades.
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... game theory can easily become one of the strongest fields in the following decades.
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DeepAI
Game Theory
Game Theory is the study of micro-situations where each situation demands a decision that
How to Develop a Cost-Sensitive Neural Network for Imbalanced Classification
After completing this tutorial, you will know:
How the standard neural network algorithm does not support imbalanced classification.
How the neural network training algorithm can be modified to weight misclassification errors in proportion to class importance.
How to configure class weight for neural networks and evaluate the effect on model performance.
Link
🔭 @DeepGravity
After completing this tutorial, you will know:
How the standard neural network algorithm does not support imbalanced classification.
How the neural network training algorithm can be modified to weight misclassification errors in proportion to class importance.
How to configure class weight for neural networks and evaluate the effect on model performance.
Link
🔭 @DeepGravity
Machine Learning Mastery
How to Develop a Cost-Sensitive Neural Network for Imbalanced Classification - Machine Learning Mastery
Deep learning neural networks are a flexible class of machine learning algorithms that perform well on a wide range of problems.
Neural networks are trained using the backpropagation of error algorithm that involves calculating errors made by the model…
Neural networks are trained using the backpropagation of error algorithm that involves calculating errors made by the model…
This Python Package ‘Causal ML’ Provides a Suite of Uplift Modeling and Causal Inference with Machine Learning
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MarkTechPost
This Python Package 'Causal ML' Provides a Suite of Uplift Modeling and Causal Inference with Machine Learning | MarkTechPost
This Python Package ‘Causal ML’ Provides a Suite of Uplift Modeling and Causal Inference with Machine Learning. ‘Causal ML’ is a Python package that deals
A Gentle Introduction to Cross-Entropy for Machine Learning
After completing this tutorial, you will know:
How to calculate cross-entropy from scratch and using standard machine learning libraries.
Cross-entropy can be used as a loss function when optimizing classification models like logistic regression and artificial neural networks.
Cross-entropy is different from KL divergence but can be calculated using KL divergence, and is different from log loss but calculates the same quantity when used as a loss function.
Link
🔭 @DeepGravity
After completing this tutorial, you will know:
How to calculate cross-entropy from scratch and using standard machine learning libraries.
Cross-entropy can be used as a loss function when optimizing classification models like logistic regression and artificial neural networks.
Cross-entropy is different from KL divergence but can be calculated using KL divergence, and is different from log loss but calculates the same quantity when used as a loss function.
Link
🔭 @DeepGravity
Methods in Computational Neuroscience
Course Date: August 2 – August 28, 2020
Deadline: March 16, 2020
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🔭 @DeepGravity
Course Date: August 2 – August 28, 2020
Deadline: March 16, 2020
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#MIT Deep Learning #course, with Alexander Amini, & Ava Soleimany
Link to the course page
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Link to the course page
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MIT Deep Learning 6.S191
MIT's introductory course on deep learning methods and applications
#Microsoft Research Webinar Series
Data Visualization: Bridging the Gap Between Users and Information
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Data Visualization: Bridging the Gap Between Users and Information
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