bert VS aws-lambda-java-libs

task-specific parameters, and is trained on the downstream tasks by simply fine-tuning all pre-trained parameters.

[0] https://arxiv.org/abs/1810.04805

The release of BERT in 2018 kicked off the language model revolution. The Transformers architecture succeeded RNNs and LSTMs to become the architecture of choice. Unbelievable progress was made in a number of areas: summarization, translation, text classification, entity classification and more. 2023 tooks things to another level with the rise of large language models (LLMs). Models with billions of parameters showed an amazing ability to generate coherent dialogue.

The general idea is that you have a particular task & dataset, and you optimize these vectors to maximize that task. So the properties of these vectors - what information is retained and what is left out during the 'compression' - are effectively determined by that task.

In general, the core task for the various "LLM tools" involves prediction of a hidden word, trained on very large quantities of real text - thus also mirroring whatever structure (linguistic, syntactic, semantic, factual, social bias, etc) exists there.

If you want to see how the sausage is made and look at the actual algorithms, then the key two approaches to read up on would probably be Mikolov's word2vec (https://arxiv.org/abs/1301.3781) with the CBOW (Continuous Bag of Words) and Continuous Skip-Gram Model, which are based on relatively simple math optimization, and then on the BERT (https://arxiv.org/abs/1810.04805) structure which does a conceptually similar thing but with a large neural network that can learn more from the same data. For both of them, you can either read the original papers or look up blog posts or videos that explain them, different people have different preferences on how readable academic papers are.

Could you post a link to "the BERT paper"? I've read some, but would be interested reading anything that anyone considered definitive :) Is it this one? "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" :https://arxiv.org/abs/1810.04805

Rust crate rust_bert implementation of the BERT language model (https://arxiv.org/abs/1810.04805 Devlin, Chang, Lee, Toutanova, 2018). The base model is implemented in the bert_model::BertModel struct. Several language model heads have also been implemented, including:

The achievement of training a BERT model to 90% of the GLUE score on a single GPU in ~100 hours is indeed impressive. As for the original BERT pretraining run, the paper [1] mentions that the pretraining took 4 days on 16 TPU chips for the BERT-Base model and 4 days on 64 TPU chips for the BERT-Large model.

Regarding the translation of these techniques to the pretraining phase for a GPT model, it is possible that some of the optimizations and techniques used for BERT could be applied to GPT as well. However, the specific architecture and training objectives of GPT might require different approaches or additional optimizations.

As for the SOPHIA optimizer, it is designed to improve the training of deep learning models by adaptively adjusting the learning rate and momentum. According to the paper [2], SOPHIA has shown promising results in various deep learning tasks. It is possible that the SOPHIA optimizer could help improve the training of BERT and GPT models, but further research and experimentation would be needed to confirm its effectiveness in these specific cases.

[1] https://arxiv.org/abs/1810.04805

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So AWS Lambda is basically a serverless computing service that is offered by AWS. It enables developers to run the code in response to various events. It protects the developers from the pain of managing the servers. Using a serverless execution model helps the developers to handle provision, manage and scale the servers automatically. Through this approach the developers can fully focus on writing the code instead of dealing with other aspects.

The first product that popularized the term “serverless” was AWS Lambda, which is both the prototypical and archetypical function as a service provider. It also has a great name, which pings back to its envisioned place in the cloud of the future. In computer programming, a lambda, often referred to as a lambda function or lambda expression, is a concise way to represent an anonymous function, which is a function without a name. The concept originates from lambda calculus in mathematical logic and has been adopted by many programming languages, each with its own syntax and characteristics.

You can build a custom config rules in 2 ways, using AWS Lambda and CloudFormation Guard. Lambda gives you a lot of flexibility, but it also brings complexity of maintaining. CloudFormation Guard is a bit more lightweight in that regard. Yes, you still need to maintain the logic to determine when your resource is compliant or not. But you need to do this in both cases, thus my go to preference is CloudFormation Guard.

AWS Lambda: https://aws.amazon.com/lambda/

This blog details how you can use some key serverless components from AWS like Amazon Eventbridge, AWS Lambda, and Simple Notification Service to setup a system that will monitor your site (which can be running anywhere) and send emails, text messages, slack messages, and more when the reachability status of your site changes.

I use the OpenWeather API to receive the current temperature data by providing the latitude and longitude coordinates as query strings in the request. A Lambda function invokes the weather API and sends the current temperature value as a custom metric to CloudWatch. I then graph the temperature values on a dashboard and activate an alarm when the temperature sinks below zero degrees Celsius.

After two years, I moved to a Web3 startup where I was given a lead software engineer role. This new role gave me more hands-on experience with AWS, where I've learned to implement serverless technologies like Lambda and DynamoDB.

I discussed a way to control access to endpoints using JSON web tokens and a Lambda authorizer earlier.

In this blog post, you will learn how to build a Serverless solution for entity detection using Amazon Comprehend, AWS Lambda and the Go programming language.