This handbook is a hands-on guide on how to approach text annotation tasks. It provides a gentle introduction to the topic, an overview of theoretical concepts as well as practical advice. The topics covered are mostly technical, but business, ethical and regulatory issues are also touched upon. The focus lies on readability and conciseness rather than completeness and scientific rigor. Experience with annotation and knowledge of machine learning are useful but not required. The document may serve as a primer or reference book for a wide range of professions such as team leaders, project managers, IT architects, software developers and machine learning engineers.

In current technical era, as we know AI ML is everywhere like air and Data is the lifeblood of that. But, as I mentioned in the previous blog on Data Augmentation, from row data to stage that data for ML training is not only challenging but also time consuming. Sometimes we dont have enough data and sometimes we train the ML model for specific purpose for which already been trained by someone else better. At that moment, what if we can leverage the pre-trained models, fine tune that and can get it predicting results for our business needs. Transfer learning is a powerful technique in machine learning that enables us to build accurate models with much less data and computation.

Deep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning-based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre-processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided.

Table of contents: What is Conformal Prediction? What is Conformal Prediction used for? Why should I use Conformal Prediction? Why shouldn’t I use Conformal Prediction? How can Conformal Prediction be used in Finance? How can Conformal Prediction be used in Algorithmic Trading? What are some Conformal Prediction alternatives? Understanding Conformal Prediction What is MAPIE? How […]

Word2vec is a powerful tool for creating word embeddings, which are numerical representations of words that capture the context and meaning of the words in a dataset. Word embeddings are a key component of many natural language processing (NLP) tasks, as they enable machine learning models to understand the meaning and context of words in a way that is similar to how humans process language. In this article, we will explore the basics of word2vec and how it can be used to create word embeddings that are effective for NLP tasks. Word2vec is a neural network model that was developed by Google researchers in 2013 for the purpose of creating word embeddings. It is based on the idea of using the context of words to predict a target word, and it uses this information to learn the relationships between words in a dataset.

If you have spent any time working with machine learning algorithms, you have likely encountered the concept of regularization. This powerful technique is used to prevent overfitting, which is when a model performs well on the training data but poorly on unseen data. There are many different types of regularization techniques, each with their own strengths and weaknesses. In this article, we will explore the most common types of regularization and provide practical tips on how to implement them in your own machine learning projects. One of the most widely used regularization techniques is called L2 regularization, which adds a penalty term to the objective function that is proportional to the square of the weights.

In the first part of this series, we explored how to use pre-trained models to classify images. In this second part, we will build our own classifiers from scratch, in order to understand some of the underlying techniques used to constructing image classification models. When we are working on an image classification problem, it is important that we understand the problem and the data. So, before jumping in and building our model, let's first try to understand what we are trying to build and why. We will be using this animal dataset from Kaggle.

Machine learning models have been shown to be vulnerable to adversarial attacks, which consist of perturbations added to inputs designed to fool the model that are often imperceptible to humans. In this document, we highlight the several methods of generating adversarial examples and methods of evaluating adversarial robustness. History of Adversarial Attacks Adversarial examples are inputs to machine learning models designed to intentionally fool them or to cause mispredictions. The canonical example is the one from Ian Goodfellow's paper below. While adversarial machine learning is still a very young field (less than 10 years old), there's been an explosion of papers and work around attacking such models and finding their vulnerabilities, turning into a veritable arms race between defenders and attackers.

Tadej Magajna is a former lead machine learning engineer, former data scientist and now a software engineer at Microsoft. He currently works in a team responsible for language model training and building language packs for keyboards such as Microsoft SwiftKey. He is also a Master of computer science. He started his career as a 15-year-old at a local media company as a web developer and progressed towards more complex engineering and machine learning problems. He tackled problems like NLP market research, public transport bus and train capacity forecasting and finally language model training at his current role.

Book 1 Have you ever wondered how a programmer develops games and writes code without having to think too much? Do you want to know what makes a programmer confident about the code they write? Do you want to learn how programmers use algorithms to determine how to structure their programs before they develop it? If you did, this is the book for you. An algorithm is a set of rules or instructions you provide to a system.