Chelsie  Towne

Chelsie Towne

1596716340

A Deep Dive Into the Transformer Architecture – The Transformer Models

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models

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A Deep Dive Into the Transformer Architecture – The Transformer Models
Chelsie  Towne

Chelsie Towne

1596716340

A Deep Dive Into the Transformer Architecture – The Transformer Models

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models

Edna  Bernhard

Edna Bernhard

1596525540

A Deep Dive Into the Transformer Architecture

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models #ai

Eve  Klocko

Eve Klocko

1596736920

A Deep Dive Into the Transformer Architecture

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models

A Deep Dive Into the Transformer Architecture — The Development of Transformer Models

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What do Transformers do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

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#architecture #natural-language-process #transformers #deep-learning

Serverless Vs Microservices Architecture - A Deep Dive

Companies need to be thinking long-term before even starting a software development project. These needs are solved at the level of architecture: business owners want to assure agility, scalability, and performance.

The top contenders for scalable solutions are serverless and microservices. Both architectures prioritize security but approach it in their own ways. Let’s take a look at how businesses can benefit from the adoption of serverless architecture vs microservices, examine their differences, advantages, and use cases.

#serverless #microservices #architecture #software-architecture #serverless-architecture #microservice-architecture #serverless-vs-microservices #hackernoon-top-story