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Inductive Learning


Brutal cold snap hits northeastern US, shattering record lows

Al Jazeera

A dangerous combination of record-setting cold temperatures and powerful winds have buffeted the northeastern United States. On Saturday, New Hampshire's Mount Washington recorded a wind chill, a measure of how the combined effect of air and wind feels to the skin, of -78 Celsius (-108 Fahrenheit), which appeared to be the lowest ever in the United States. The air temperature at the peak reached -44C (-47F), with winds gusting near 160km/h (100 mph), according to the Mount Washington Observatory. In Boston, where officials closed down the public school system on Friday due to the impending freeze, the low temperature hit -23C (-10 F), shattering the day's record set more than a century ago, the National Weather Service (NWS) said. In Providence, Rhode Island, the mercury dropped to -23C (-9F), well below the previous all-time low of -19C (-2F), set in 1918.


Applications of Imitation Learning part1(Machine Learning)

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Abstract: isual imitation learning enables reinforcement learning agents to learn to behave from expert visual demonstrations such as videos or image sequences, without explicit, well-defined rewards. Previous research either adopted supervised learning techniques or induce simple and coarse scalar rewards from pixels, neglecting the dense information contained in the image demonstrations. In this work, we propose to measure the expertise of various local regions of image samples, or called \textit{patches}, and recover multi-dimensional \textit{patch rewards} accordingly. Patch reward is a more precise rewarding characterization that serves as a fine-grained expertise measurement and visual explainability tool. Specifically, we present Adversarial Imitation Learning with Patch Rewards (PatchAIL), which employs a patch-based discriminator to measure the expertise of different local parts from given images and provide patch rewards.


Learning by on-line gradient descent - IOPscience

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We study on-line gradient-descent learning in multilayer networks analytically and numerically. The training is based on randomly drawn inputs and their corresponding outputs as defined by a target rule. In the thermodynamic limit we derive deterministic differential equations for the order parameters of the problem which allow an exact calculation of the evolution of the generalization error. First we consider a single-layer perceptron with sigmoidal activation function learning a target rule defined by a network of the same architecture. For this model the generalization error decays exponentially with the number of training examples if the learning rate is sufficiently small.


The Evolution of Boosting Algorithms

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Decision Trees are used in statistics, data mining and machine learning and they are a supervised learning method which can be applied in both classification and regression. But the Decision Trees can be improved using boosting as it was first described by Schapire in his paper "The Strength of Weak Learnability "[1]. Basically, a boosting algorithm is a learning algorithm that will take advantage of the weak learners in order to generate high-accuracy hypotheses. However, over the years the algorithm has been improved and adapted by various contributors. The fact that the algorithm suffered a series of mutation that lead to algorithms like XGBoost, AdaBoost, Gradient Boost, LightGBM, is proof that the main idea has passed "the test of time".


Agile Scrum Master Training : Case Studies And Confessions

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Includes Narration from Randal Shaffer. Agile scrum is a simple method for managing and completing even the most complex project, even in difficult situations . Based on my experience, it is the number one most popular way to deliver projects on-time while maintaining a high degree of quality. Who should take is course? Whether you are acrum Master, Project Manager, Product Owner or Team Member or simply someone who wants the answer to the question "how do I deal with difficult/challenging situations using scrum", this is definitely the class is for you.


I. The Fundamental of Machine Learning

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Machine learning is the science (and art) of programming computers so they can learn from data. It is a subfield of Artificial Intelligence founded on the notion that machines are capable of learning from data, spotting patterns, and making judgements with little assistance from humans. A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. One of the best example of Machine Learning program is " The spam filter" in our Gmail application. The spam filter take examples of spam emails (flagged by users) and examples of regular emails (non-spam, also called "ham"), and can learn to flag spam. These examples that system or algorithm take called "Training set", and each training example is known as "Training instances".


Bag of Tricks for Optimizing Machine Learning Training Pipelines - MLOps Community

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Finally, one more interesting aspect of our training infrastructure is that we use a multi-cloud setup in practice. As it was told earlier, GCP is our main vendor for training instances for cost and powerful machines availability-related reasons, while our default production infrastructure is AWS. It means that sometimes we need to combine the two: e.g. We use Flyte to orchestrate this process. Flyte is a workflow management system that allows us to define a pipeline as a DAG of tasks. It is useful for us because it allows us to define a pipeline once and run its steps on different machines with different computational resources allocation, and it also provides a nice UI for monitoring the progress of the pipeline.



Recent updates in Self-Supervised Learning methods part1(Machine Learning)

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Abstract: Contrastive learning has become a prominent ingredient in learning representations from unlabeled data. However, existing methods primarily consider pairwise relations. This paper proposes a new approach towards self-supervised contrastive learning based on Group Ordering Constraints (GroCo). Building on the recent success of differentiable sorting algorithms, group ordering constraints enforce that the distances of all positive samples (a positive group) are smaller than the distances of all negative images (a negative group); thus, enforcing positive samples to gather around an anchor. This leads to a more holistic optimization of the local neighborhoods.


Recent updates in Self-Supervised Learning methods part2(Machine Learning)

#artificialintelligence

Abstract: Contrastive learning has become a prominent ingredient in learning representations from unlabeled data. However, existing methods primarily consider pairwise relations. This paper proposes a new approach towards self-supervised contrastive learning based on Group Ordering Constraints (GroCo). Building on the recent success of differentiable sorting algorithms, group ordering constraints enforce that the distances of all positive samples (a positive group) are smaller than the distances of all negative images (a negative group); thus, enforcing positive samples to gather around an anchor. This leads to a more holistic optimization of the local neighborhoods.