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Multi-task learning in Machine Learning

 Towards Data Science

In most machine learning contexts, we are concerned with solving a single task at a time. Regardless of what that task is, the problem is typically framed as using data to solve a single task or…

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A Primer on Multi-task Learning — Part 1

 Analytics Vidhya

Multi-task Learning (MTL) is a collection of techniques intended to learn multiple tasks simultaneously instead of learning them separately. The motivation behind MTL is to create a “Generalist”…

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Multi-task Learning: All You Need to Know(Part-1)

 Python in Plain English

Figure: Framework of Multi-task learning Multi-task learning is becoming incredibly popular. This article provides an overview of the current state of multi-task learning. It discusses the extensive m...

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Multi-Task Machine Learning: Solving Multiple Problems Simultaneously

 Towards Data Science

Some supervised, some unsupervised, some self-supervised, in NLP and computer vision Continue reading on Towards Data Science

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Multi-task learning in Computer Vision: Image classification

 Analytics Vidhya

Ever faced an issue where you had to create a lot of deep learning models because of the requirements you have, worry no more as multi-task learning is here. Multi-task learning can be of great help…

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Optimizing Multi-task Learning Models in Practice

 Towards Data Science

Why Multi-task learning Multi-task learning Multi-task learning (MTL) [1] is a field in machine learning in which we utilize a single model to learn multiple tasks simultaneously. Multi-task learning ...

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A Primer on Multi-task Learning — Part 2

 Analytics Vidhya

Towards building a “Generalist” model. “A Primer on Multi-task Learning — Part 2” is published by Neeraj Varshney in Analytics Vidhya.

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A Primer on Multi-task Learning — Part 3

 Analytics Vidhya

Towards building a “Generalist” model. “A Primer on Multi-task Learning — Part 3” is published by Neeraj Varshney in Analytics Vidhya.

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Multi-Task Learning for Classification with Keras

 Towards Data Science

Learn how to build a model capable of performing multiple image classifications concurrently with Multiple-Task Learning Photo by Markus Winkler on Unsplash Multi-task learning (MLT) is a subfield of...

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Two Tasks, Two Datasets, One Network: Multi-task Learning with DnD

 Towards Data Science

Multi-task learning using multiple datasets for multiple tasks implemented in Pytorch and applied to a DnD use case

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Multi-task learning with Multi-gate Mixture-of-experts

 Towards Data Science

Multi-task learning is a machine learning method in which a model learns to solve multiple tasks simultaneously. The assumption is that by learning to complete multiple correlated tasks with the same…...

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Multitask learning: teach your AI more to make it better

 Towards Data Science

Hi everyone! Today I want to tell you about the topic in machine learning that is, on one hand, very research oriented and supposed to bring machine learning algorithms to more human-like reasoning…

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How to Learn Multiple Tasks with a Single Neural Network

 Towards Data Science

Modern neural networks are very good at learning one particular thing. Whether it be playing chess or folding proteins, with enough data and time, neural networks can achieve amazing results…

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Deep Multi-Task Learning — 3 Lessons Learned

 Towards Data Science

For the past year, my team and I have been working on a personalized user experience in the Taboola feed. We used Multi-Task Learning (MTL) to predict multiple Key Performance Indicators (KPIs) on…

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The Multi-Task Optimization Controversy

 Towards Data Science

The multi-task learning paradigm — that is, the ability to train models on multiple task at the same time — has been a blessing as much as a curse. A blessing because it allows us to build a single mo...

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Norms, Penalties, and Multitask learning

 Towards Data Science

A regularizer is commonly used in machine learning to constrain a model’s capacity to cerain bounds either based on a statistical norm or on prior hypotheses. This adds preference for one solution…

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Transfer Learning

 Analytics Vidhya

As humans growing and learning in day-to-day activities right from childhood. As humans acquire knowledge by learning one task. By using the same knowledge we tend to solve the related task. Say in…

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Multi-Task Learning with Pytorch and FastAI

 Towards Data Science

Following the concepts presented on my post named Should you use FastAI?, I’d like to show here how to train a Multi-Task deep learning model using the hybrid Pytorch-FastAI approach. The basic idea…

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When Multi-Task Learning meet with BERT

 Towards Data Science

BERT (Devlin et al., 2018) got the state-of-the-art result in 2018 in multiple NLP problems. It leveraged transformer architecture to learn contextualized word embeddings such that those vectors…

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Multi-Task Learning in Recommender Systems: A Primer

 Towards Data Science

While multi-task learning has been has been well established in computer vision and natural language processing, its use in modern recommender systems is still relatively new and therefore not very we...

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Multi-Task Learning in Language Model for Text Classification

 Towards Data Science

Howard and Ruder propose a new method to enable robust transfer learning for any NLP task by using pre-training embedding, LM fine-tuning and classification fine-tuning. The sample 3-layer of LSTM…

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Joint feature selection with multi-task Lasso

 Scikit-learn Examples

Joint feature selection with multi-task Lasso The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected features to be the same across tasks. This example simulate...

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A Practical and Intuitive Guide to Building Multi-task Learning Models

 Daily Dose of Data Science

Yesterday’s post discussed four critical model training paradigms used in training many real-world ML models. Here’s the visual from that post for a quick recap: After releasing this post, a few reade...

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HMTL - Multi-task Learning for solving NLP Tasks

 Towards Data Science

The field of Natural Language Processing includes dozens of tasks, among them machine translation, named-entity recognition, and entity detection. While the different NLP tasks are often trained and…

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