Inductive Learning


r/MachineLearning - [P] Announcing the release of StellarGraph version 0.6.0 open source machine learning library for geometric deep learning.

#artificialintelligence

StellarGraph is a Python 3 library. The StellarGraph library implements several state-of-the-art algorithms for applying machine learning methods to discover patterns and answer questions using graph-structured data. Added GraphConvolution layer, GCN class for a stack of GraphConvolution layers, and FullBatchNodeGenerator class for feeding data into GCN (from version 0.5.0) We provide examples of using StellarGraph to solve such tasks using several real-world datasets.


Python machine learning libraries

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This blog is a part of the learn machine learning coding basics in a weekend . The Python built-in list type does not allow for efficient array manipulation. The NumPy package is concerned with manipulation of multi-dimensional arrays. NumPy is at the foundation of almost all the other packages covering the Data Science aspects of Python. From a Data Science perspective, collections of Data types like Documents, Images, Sound etc can be represented as an array of numbers.


Python machine learning libraries

#artificialintelligence

This blog is a part of the learn machine learning coding basics in a weekend . The Python built-in list type does not allow for efficient array manipulation. The NumPy package is concerned with manipulation of multi-dimensional arrays. NumPy is at the foundation of almost all the other packages covering the Data Science aspects of Python. From a Data Science perspective, collections of Data types like Documents, Images, Sound etc can be represented as an array of numbers.


Python machine learning libraries

#artificialintelligence

This blog is a part of the learn machine learning coding basics in a weekend . The Python built-in list type does not allow for efficient array manipulation. The NumPy package is concerned with manipulation of multi-dimensional arrays. NumPy is at the foundation of almost all the other packages covering the Data Science aspects of Python. From a Data Science perspective, collections of Data types like Documents, Images, Sound etc can be represented as an array of numbers.


Google brings differential privacy to third-party ML developers using TensorFlow

#artificialintelligence

Ahead of the 2019 TensorFlow Dev Summit, Google is announcing a new way for third-party developers to adopt differential privacy when training machine learning models. TensorFlow Privacy is designed to be easy to implement for developers already using the popular open-source ML library. The goal (via The Verge) of differential privacy for machine learning is to only "encode general patterns rather than facts about specific training examples." This allows user data to remain private, while the system overall still learns and can advance from general behavior. In particular, when training on users' data, those techniques offer strong mathematical guarantees that models do not learn or remember the details about any specific user.


Introducing TensorFlow Privacy: Learning with Differential Privacy for Training Data

#artificialintelligence

Today, we're excited to announce TensorFlow Privacy (GitHub), an open source library that makes it easier not only for developers to train machine-learning models with privacy, but also for researchers to advance the state of the art in machine learning with strong privacy guarantees. Modern machine learning is increasingly applied to create amazing new technologies and user experiences, many of which involve training machines to learn responsibly from sensitive data, such as personal photos or email. Ideally, the parameters of trained machine-learning models should encode general patterns rather than facts about specific training examples. To ensure this, and to give strong privacy guarantees when the training data is sensitive, it is possible to use techniques based on the theory of differential privacy. In particular, when training on users' data, those techniques offer strong mathematical guarantees that models do not learn or remember the details about any specific user.


How Alexa Learns

#artificialintelligence

Over the past 10 years, commercial AI has enjoyed what we at Amazon call the flywheel effect: customer interactions with AI systems generate data; with more data, machine learning algorithms perform better, which leads to better customer experiences; better customer experiences drive more usage and engagement, which in turn generate more data. Those data are used to train machine learning systems in three chief ways. The first is supervised learning, in which the training data are hand-labeled (with, say, words' parts of speech or the names of objects in an image) and the system learns to apply labels to unlabeled data. A variation of this is weakly supervised learning, which uses easily acquired but imprecise labels to enable machine learning at scale. If a website visitor performs a search, for instance, the links she clicks indicate which search results should have been at the top of the list; that kind of implicit information can be used to automatically label data.


TikTok agrees to pay record $5.7-million settlement in FTC children's online privacy case

USATODAY

USA TODAY tech expert Jefferson Graham explains the pros and cons of your childrens' favorite apps. TikTok, a popular video-sharing app, has agreed to pay $5.7 million to settle Federal Trade Commission allegations that it illegally collected personal information from children. The FTC's complaint, filed by the Department of Justice, alleged that TikTok, formerly known as Musical.ly, The act requires websites and online services to direct children under 13 to get parental consent before collecting personal information. The operators of the app "knew many children were using the app but they still failed to seek parental consent before collecting names, email addresses, and other personal information from users under the age of 13," FTC Chairman Joe Simons said in a statement Wednesday.


Kyle Busch gets record-setting 52nd NASCAR Trucks win

FOX News

Kyle Busch broke a tie with Ron Hornaday Jr. for the NASCAR Truck Series victory record Saturday at Atlanta Motor Speedway, winning No. 52 in a race delayed by rain with nine laps to go. "It means a lot," Busch said. The first 16 victories came in trucks fielded by Billy Ballew before Busch founded his own Kyle Busch Motorsports team. With Ballew in attendance at Atlanta, Busch's Tundra carried the former owner's name. "I had Billy Ballew on board with us here today," Busch said.


Saliency Learning: Teaching the Model Where to Pay Attention

arXiv.org Artificial Intelligence

Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model's behavior and predictions, which are helpful for determining the reliability of the model's prediction. However, such methods do not fix and improve the model's reliability. In this paper, we teach our models to make the right prediction for the right reason by providing explanation training signal and ensuring alignment of the models explanation with the ground truth explanation. Our experimental results on multiple tasks and datasets demonstrate the effectiveness of the proposed method, which produces more reliable predictions while delivering better results compared to traditionally trained models.