It was introduced in the paper DiT: Self-supervised Pre-training for Document Image Transformer by Li et al. and first released in this repository. Note that DiT is identical to the architecture of BEiT. An application program interface (API) is a set of routines, protocols, and tools for building software applications. Basically, an API specifies how software components should interact. FastAPI is a Web framework for developing RESTful APIs in Python.

No matter how efficient your Machine Learning model is, it will only be useful when it creates value for the Business. This can not happen when it's stored in a folder on your computer. In this fast-growing environment, speed and good deployment strategies are required to get your AI solution to the market! This article explains how Fast APIcan help on that matter. We will start by having a global overview of Fast API and its illustration by creating an API.

No matter how efficient your Machine Learning model is, it will only be useful when it creates value for the Business. This can not happen when it's stored in a folder on your computer. In this fast-growing environment, speed and good deployment strategies are required to get your AI solution to the market! This article explains how Fast APIcan help on that matter. We will start by having a global overview of Fast API and its illustration by creating an API.

The Natural Language framework provides a variety of natural language processing (NLP) functionality with support for many different languages and scripts. Use the Speech framework to recognize spoken words in recorded or live audio. The keyboard's dictation support uses speech recognition to translate audio content into text. This framework provides a similar behavior, except that you can use it without the presence of the keyboard. For example, you might use speech recognition to recognize verbal commands or handle text dictation in other parts of your app.

In this project, we will learn how we can build an application programming interface (API) for your machine learning model and then deploy it with simple code. It took me one hour to learn FastAPI and five minutes to learn how to deploy it to Deta servers. We will also test our API on both local server and remote server using Python Request. Let's go a little bit deeper into the technologies that we are going to use in our project. We will be using quite a simple and small prebuilt English model to extract entities from our text.

Day by day the number of machine learning models is increasing at a pace. With this increasing rate, it is hard for beginners to choose an effective model to perform Natural Language Understanding (NLU) and Natural Language Generation (NLG) mechanisms. Researchers across the globe are working around the clock to achieve more progress in artificial intelligence to build agile and intuitive sequence-to-sequence learning models. And in recent times transformers are one such model which gained more prominence in the field of machine learning to perform speech-to-text activities. The wide availability of other sequence-to-sequence learning models like RNNs, LSTMs, and GRU always raises a challenge for beginners when they think about transformers.

Deep convolutional neural network (CNN) based image classification plays an essential role in seamlessly performing most of the challenges from disease diagnosis to predicting consumerism behavior. Using Deep CNN reduces the time and effort required to spend on extracting and selecting classification features manually. In recent times, deep CNN has been applied to image classification … ...

Explore machine learning with DeepLobe, a unique MLaaS API for applications like image recognition, OCR, object detection, etc.

This article demonstrates how building a smarter API serving Deep Learning models minimizes the response time. Your team worked hard to build a Deep Learning model for a given task (let's say: detecting bought products in a store thanks to Computer Vision). You then developed and deployed an API that integrates this model (let's keep our example: self-checkout machines would call this API). The new product is working well and you feel like all the work is done. But since the manager decided to install more self-checkout machines (I really like this example), users have started to complain about the huge latency that occurs each time they are scanning a product. Ask data scientists to try reducing the depth of the model without degrading its accuracy?

In case if you are not doing that specialization then you can just do this course to learn about TensorFlow and how you can use it for Artifical Intelligence and Machine Learning.