(testing signal)

Tag: trainingdata

How Much Training Data Do You Require For Machine Learning?

It is a crucial component of machine learning (ML), and having the proper quality and amount of data sets is critical for accurate outcomes. The more training data available for the machine learning algorithm, the better the model will be able to identify different sorts of objects, making it simpler to distinguish them in real-life predictions.However, how will you determine how much training is sufficient for your machine learning? As insufficient data will affect…… Read more...

Are Zero-Shot Text Classification Transformer Models the Key to Better Chatbots?

Overcome the need for training data with zero-shot text classification Transformer models for your next chatbot project

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One of the most cumbersome tasks for many natural language processing (NLP) projects is collecting and labelling training data. But, there’s a potential solution to this problem when it comes to intent classification for chatbots, and that is using zero-shot text classification Transformer models. If successful, this method would reduce the complexity required for developing chatbots while potentially improving their performance. I encourage you to expand upon these ideas and possibly integrate them into your chatbot systems.

Intent classification is a fundamental tasks that chatbots perform.

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Data Labeling for Machine Learning Models

Machine learning models make use of training datasets for predictions. And, thus labeled data is an important component for making the machines learning and interpret information. A variety of different data are prepared. They are identified and marked with labels, also often as tags, in the form of images, videos, audio, and text elements. Defining these labels and categorization tags generally includes human-powered effort.

Machine learning models which fall under the categories of supervised and unsupervised, pick the datasets and make use of the information as per ML algorithms. Data labeling for machine learning or training data preparation encompasses tasks such as data tagging, categorization, labeling, model-assisted labeling, and annotation.

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Stanford’s AIMI is Revolutionizing Healthcare AI by Providing Free Big Data to Researchers

As AI continues to expand its role as a solution for numerous areas in our society, the need for more high-quality training data grows. Learning algorithms and models are limited only by the data they are fed; this is where Stanford University’s AIMI comes into the big picture.

Big AI, IoT, AIoT, robotics, and 5G-enabled smart systems need as much information as possible in order to create real-time solutions. AIMI’s library is expected to hit the 2 million image mark in 2022. It is the most robust resource of curated, patient-deidentified, and AI-ready data, and its ramifications for AI in healthcare are unimaginable.

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My ML Model Fails. Why? Is It the data?

Understand if the model does not perform well because of a bad model selection or because of noise in the training data with a real example.

Image: The School of Athens by Raphael 1509

One of the most common problems in Machine Learning when you build and train a model and you check its accuracy is “Is the accuracy the best I can get from the data or could a find a better model?”.

Also, once your model is deployed, the next common question is “Why does the model fail when it does?”. Sometimes neither of these questions can be answered but sometimes we can find preprocessing errors, model bias, and also data leaks by studying the statistical distribution of the model errors.

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A Beginners Guide to Federated Learning


Recently, Google has built one of the most secure and robust cloud infrastructures for processing data and making our services better, known as Federated Learning.

In Federated Learning, a model is trained from user interaction with mobile devices. Federated Learning enables mobile phones to collaboratively learn over a shared prediction model while keeping all the training data on the device, changing the ability to perform machine learning techniques by the need to store the data on the cloud. This method goes beyond the use of local models that make predictions based on mobile device APIs like the Mobile Vision API or the On-Device Smart Reply, bringing model training to the device as well.

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Top Resources To Learn About Federated Learning


Federated learning, or collaborative learning, is a collaborative machine learning method that operates without changing original data. Unlike standard machine learning approaches that require centralising the training data into one machine or datacentre, federated learning trains algorithms across multiple decentralised edge devices or servers. This learning technique enables mobile phones to learn a shared prediction model while keeping the training data on the device itself and without having to store data in the cloud.

Today, we list down ten resources that will help you learn about federated learning.

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Courses

Federated learning using PyTorch: Udemy 

Created by ML enthusiast Mohamed Gharibi, this course on Udemy is targeted towards all federated learning enthusiasts.

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With MAPIE, uncertainties are back in machine learning !

MAPIE is based on the resampling methods introduced in a state-of-the-art research paper by R. Foygel-Barber et al. (2021) [1] for estimating prediction intervals in regression settings and coming with strong guarantees. MAPIE implements no less than 8 different methods from this paper, in particular the Jackknife+ and the CV+.

The so-called Jackknife+ method is based on the construction of a set of leave-one-out models: each perturbed model is trained on the entire training data with one point removed. Interval predictions are then estimated from the distribution of the leave-one-out residuals estimated by these perturbed models. The novelty of this elegant method is that predictions on a new test sample are no longer centered on the predictions estimated out by the base model as with the standard jackknife method but on the predictions from each perturbed model.

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Geometric deep learning of RNA structure

RNA molecules fold into complex three-dimensional shapes that are difficult to determine experimentally or predict computationally. Understanding these structures may aid in the discovery of drugs for currently untreatable diseases. Townshend et al. introduced a machine-learning method that significantly improves prediction of RNA structures (see the Perspective by Weeks). Most other recent advances in deep learning have required a tremendous amount of data for training. The fact that this method succeeds given very little training data suggests that related methods could address unsolved problems in many fields where data are scarce.

Science , abe5650, this issue p. [1047][1]; see also abk1971, p.

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Automated Data Labeling with Machine Learning

Labeling training data is the one step in the data pipeline that has resisted automation. It’s time to change that.

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Webinar: September 2nd, 2021, noon ET, 3 pm PT

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The significant issues with hand labeling include the introduction of bias (and hand labels are neither interpretable nor explainable), the prohibitive costs (both financial costs and the time of subject matter experts), and the fact that there is no such thing as gold labels (even the most well-known hand labeled datasets have label error rates of at least 5%!).

Shayan Mohanty, CEO of Watchful, joins Hugo Bowne-Anderson, Head of Data Science Evangelism at Coiled, to discuss why hand labeling, a fundamental part of human-mediated machine intelligence, is naive, dangerous, and expensive.

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Bounding the Sample Size of a Machine Learning Algorithm

One common problem with machine learning algorithms is that we don’t know how much training data we need. A common way around this is the often used strategy: keep training until the training error stops decreasing. However, there are still issues with this. How do we know we’re not stuck in a local minimum? What if the training error has strange behavior, sometimes staying flat over training iterations but sometimes decreasing sharply? The bottom line is that without a precise way of knowing how much training data we need, there will always be some uncertainty as to whether or not we are done training.

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Traditional ML and DL VS the amount of training data

Why has deep learning been so successful? What is the fundamental reason that deep learning can learn from big data? Why cannot traditional ML learn from the large data sets that are now available for different tasks as efficiently as deep learning can? These questions can be answered by understanding the learnability of deep learning — otherwise known as Vapnik-Chervonenkis dimension, illustrated beautifully by the curve in figure 1 [1]. The curve captures the performance of traditional ML and DL VS the amount of data used to train the models.

It can be observed that when data sets are small, the traditional ML has better performance compared to DL, but as the data sets move into the big data zone, the DL’s performance keeps increasing, almost exponentially.… Read more...

Outsourcing Training Data Needs to Data Labeling Companies

The performance of an AI system undeniably depends on the training data rather than the programming. Data is necessary for machine learning models to work. Even the most performant algorithms can be rendered worthless without a foundation of high-quality training data. Indeed, when substandard or irrelevant data is provided to machine learning models in the early stages, then it will hamper the overall result and the whole thing might also cripple. Therefore, quality data is necessary for…… Read more...

Why Outsourcing Triumphs Over Crowdsourcing in AI Training Data ?

Outsourcing in AIMany firms want to automate processes using technology to reap benefits, reach business goals faster, or to simply get on with the digital transformation wave for heightening efficiency. At the back of every AI program is specialized training data that enable machine learning algorithms and AI programs to work self-reliantly. And thus, data annotation is central to machine learning models.Normally, data with which machines can be trained is available in an unstructured…… Read more...

Free dataset worth $1350 to test the accent gap!

With so many accent variations, how do speech and voice technologies keep up? In a few words: accented speech training data, representative of diverse groups of people. The more people your model can understand, the more likely you are to acquire and retain customers.

Sponsored Post.

Free dataset worth $1350 to test the accent gap!

Here’s how accented speech data is the key to understanding multi-cultural users

Given the demographic composition of the United States, it’s no wonder that spoken English varies greatly across the country. According to the US census, there are 35 million non-native English speakers living in the US, of which 60% are native Spanish speakers.

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Understanding BERT with Hugging Face

Using BERT and Hugging Face to Create a Question Answer Model

 
In a recent post on BERT, we discussed BERT transformers and how they work on a basic level. The article covers BERT architecture, training data, and training tasks.

However, we don’t really understand something before we implement it ourselves. So in this post, we will implement a Question Answering Neural Network using BERT and a Hugging Face Library.

What is a Question Answering Task?

 
In this task, we are given a question and a paragraph in which the answer lies to our BERT Architecture and the objective is to determine the start and end span for the answer in the paragraph.

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