Active learning for object detection In this tutorial, we will see results of active learning applied to object detection. It is no secret that machine learning models, especially deep learning models, need lots of training data. In the real world, unsupervised data is plenty while supervised data is rare and costly to obtain. Thus, you … Continue reading Active Learning for Object Detection
A chatbot needs data for two main reasons: to know what people are saying to it, and to know what to say back. An effective chatbot requires a massive amount of training data in order to quickly resolve user requests without human intervention. However, the main obstacle to the development of chatbot is obtaining realistic … Continue reading 36 Best Machine Learning Datasets for Chatbot Training
Faster Training With Kili Technology : Active Learning It is no secret that machine learning models, especially deep learning models, need lots of training data. In the real world, unsupervised data is plenty while supervised data is rare and costly to obtain. Thus, you may be interested in using active learning. It is the task … Continue reading Faster training with active learning
How to read & label dicom medical images on Kili In this tutorial, we will show you how to upload medical images to Kili Technology. We will use pydicom, a python package, to read medical data in a Dicom format. Data used in this tutorial comes from the RSNA Pneumonia Detection Challenge hosted on Kaggle … Continue reading How to read & label dicom medical images on Kili
Kili Tutorial: How to chain annotation projects with webhooks on Kili In this tutorial, we will show how to use webhooks to monitor actions in Kili, such as a label creation. The goal of this tutorial is to illustrate some basic components and concepts of Kili in a simple way, but also to dive into … Continue reading How to chain annotation projects with webhooks on Kili
AutoML for fast labeling with Kili Technology This tutorial is taken from our recipes. You can find an executable version of the Jupyter notebook on Github. In this tutorial, we will show how to use automated machine learning (AutoML) to accelerate labeling in Kili Technology. We will apply it in the context of text classification: … Continue reading AutoML for fast annotation
Video annotation is the process of labelling video clips. This is done to prepare it as a dataset for training deep learning (DL) and machine learning (ML) models. These pre-trained neural networks are then used for computer vision applications, such as automatic video classification tools. ML is a field of artificial intelligence (AI) research, which … Continue reading What is Video Annotation for Deep Learning
The dataset that you use to train your machine learning models can make or break the performance of your applications. For example, using a text dataset that contains loads of biased information can significantly decrease the accuracy of your machine learning model. This was what happened to Amazon's initial tests. They trained a machine learning … Continue reading How to Create a Dataset to Train Your Machine Learning Applications
Image annotation plays an important role in training a machine to automatically assign relevant metadata information to a digital picture. This metadata often includes captions, keywords, location markers, or any combination of these details. This process is required for creating datasets that are used to train the deep learning models of computer vision applications. Many … Continue reading What is Image Annotation in Deep Learning?
What is Text Annotation in Machine Learning? Simply put, text annotation in machine learning ( ML ) is the process of associating labels to a digital file or document and its content. This is an NLP method where different types of sentence structures are highlighted by various criteria. Because human language is quite complex, annotation … Continue reading What is Text Annotation in Machine Learning?