training


New Theory Cracks Open the Black Box of Deep Learning Quanta Magazine

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During deep learning, connections in the network are strengthened or weakened as needed to make the system better at sending signals from input data -- the pixels of a photo of a dog, for instance -- up through the layers to neurons associated with the right high-level concepts, such as "dog." It was a stunning indication that, as the biophysicist Ilya Nemenman said at the time, "extracting relevant features in the context of statistical physics and extracting relevant features in the context of deep learning are not just similar words, they are one and the same." In their experiments, Tishby and Shwartz-Ziv tracked how much information each layer of a deep neural network retained about the input data and how much information each one retained about the output label. The scientists found that, layer by layer, the networks converged to the information bottleneck theoretical bound: a theoretical limit derived in Tishby, Pereira and Bialek's original paper that represents the absolute best the system can do at extracting relevant information.


Seq2seq for NLP: encoder-decoder framework for Tensorflow

@machinelearnbot

General Purpose: We initially built this framework for Machine Translation, but have since used it for a variety of other tasks, including Summarization, Conversational Modeling, and Image Captioning. Several types of input data are supported, including standard raw text. General Purpose: We initially built this framework for Machine Translation, but have since used it for a variety of other tasks, including Summarization, Conversational Modeling, and Image Captioning. Several types of input data are supported, including standard raw text.


The 6 types of artificial intelligence 7wData

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Wow – lots and lots and lot of uses for Machine Learning, once the machine has figured out how to do the recognition – the "training". Initial training is hard work, both for the humans who prepare the training data and the computer that's trying to create the models from the training data. If we give a machine learning system a goal and lot of power, it might come to the unfortunate decision that humans get in the way of achieving its goals, and that if it could get rid of those humans, it would reach its goal faster. There is the inference part - the part that takes in the data and figures out what's going on.


The 6 types of artificial intelligence 7wData

#artificialintelligence

Wow – lots and lots and lot of uses for Machine Learning, once the machine has figured out how to do the recognition – the "training". Initial training is hard work, both for the humans who prepare the training data and the computer that's trying to create the models from the training data. If we give a machine learning system a goal and lot of power, it might come to the unfortunate decision that humans get in the way of achieving its goals, and that if it could get rid of those humans, it would reach its goal faster. There is the inference part - the part that takes in the data and figures out what's going on.


Machine Learning - Ensemble Methods

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Model A Model B Model C Input Sample Each Model receives the same input Vote Each Model outputs its Prediction to a vote accumulator ŷ3 ŷ1 ŷ2 ŷf A final predictor is determined from a majority vote of the model's Predictors. Ensemble – Decision Stumps Decision Stumps – Weak Learners 1st Feature 2nd Feature 4 4 3rd Feature weight width 2.5 2.5 height banana apple banana apple apple 4 4 banana MAJORITY VOTE Weight: 4.2 Apple Width: 2.3 Banana Height: 5.5 Banana VOTE Banana 7. Training Data Random Subset Random Subset Random Subset Random Subset Random Subsets Random Splitting into Subsets Models Models Models Models Models Trained Weaker Models Majority Vote Models' Predictions Stronger Predictor 8. Training Data Random Subset Random Subset Random Subset Random Subset Random Subsets Random Splitting into Subsets Models Models Models Models Models Trained Weaker Models Majority Vote Models' Predictions Stronger Predictor


6 machine learning projects to automate machine learning

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Here are six automated machine learning tools leading the way. The first project, "AutoML," was created to automate the design of multi-layer deep learning models. Instead of having human beings toss-and-test one deep learning network design after another, AutoML uses a reinforcement learning algorithm to test thousands of possible networks. This story, "6 machine learning projects to automate machine learning" was originally published by InfoWorld .


Machine learning and Industrial IoT: Now and into the future

@machinelearnbot

Support vector machines (SVM), logistic regression, and artificial neural networks are commonly used supervised ML algorithms. By using multiple hidden layers, DL algorithms learn the features that need to be extracted from the input data without the need to explicitly input the features to the learning algorithm. DL has seen recent success in IIoT applications mainly because of the coming of age of technological components, such as more compute power in hardware, large repositories of labeled training data, breakthroughs in learning algorithms and network initialization, and the availability of open source software frameworks. Using transfer learning, you can start with a pre-trained neural network (most DL software frameworks provide fully trained models that you can download) and fine-tune it with data from your application.


The AI Glossary: A Data Scientist's No-Fluff Explanations for Key AI Concepts

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But the term "artificial intelligence" today can still refer to either the strong or weak versions, making "machine learning" a subset of "artificial intelligence" work. A subset of artificial intelligence work, machine learning is more narrowly focused on computer systems optimized to perform specific tasks, fed by large amounts of example data to "learn" from, using methods from computational statistics and probability theory. A form of machine learning in which there are no pre-existing labels or outputs defined on the input training data, and the system instead "learns" whatever patterns, clusters, or regularities it can extract from the training data. For example, if we are studying the mean temperature across some region of the Earth over time, and we have measured this mean temperature at some finite number of times, we can create a regression model of temperature as a function of time based on these data points to predict what the temperature might be between two of our measurements ("interpolation") or what the temperature might be at future times ("extrapolation").


How Small Businesses Can Leverage Artificial Intelligence

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Hiring: Hiring is another area where automating processes and utilizing existing AI solutions can help leaders streamline their processes and make better decisions. Case in point, applications leverage natural language processing techniques in order to improve the quality of new hires. Hiring: Hiring is another area where automating processes and utilizing existing AI solutions can help leaders streamline their processes and make better decisions. Case in point, applications leverage natural language processing techniques in order to improve the quality of new hires.


Sentiment Analysis Just Got Smarter

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They've developed a social sentiment technology based on deep learning that lets brands capture customer sentiment with 90% accuracy. This AI technology for the first time truly understands the meaning of full sentences and is able to accurately determine customer attitudes and contextual reactions in tweets, posts and articles. There are two main approaches most vendors use today: sentiment analysis based on keyword scoring, or a calculation based on predefined categories. For the first time, the algorithm understands the meaning of full sentences and is able to accurately determine customer attitudes and contextual reactions in tweets, posts and articles.