Pandas is a popular library for data analysis built on top of the Python programming language. Pandas can be though as a digital toolbox that holds various tools for working with data. Pandas pairs well with other libraries for statistics, natural language processing, machine learning, visualization, and more. Pandas is comparable to Microsoft’s spreadsheet software Excel or Google’s inbrowser Sheets application. All three of these options allow users to interact with single- or multi-column collections of data organized in a tabular (grid-like) shape. Data can be sorted, filtered, aggregated, split, pivoted, summarized, and more

Pandas was initially developed by data…


ML Concept: What are Hyper-parameters?

The goal of ML applications is to create models that can master a task based on a dataset. But how do we know that our models are learning at an optimal rate? To achieve that, we need to regularly tune different aspects of the model and evaluate its performance. Think about the process of learning to play the guitar. With every new melody, we are constantly tuning different aspects of the guitar to make it sound better.

In ML, those aspects that you can fine-tune are known as hyper-parameters.

Conceptually, a hyper-parameter is a parameter that helps control the learning…


Introduction

Let’s talk about the nature of learning. We are not born knowing much. Over the curse of our lifetimes, we slowly gain an understanding of the world through interaction. We learn about cause and effect or how the world responds to our actions. Once we have an understanding of how the world works, we can use our knowledge to accomplish specific goals. we’ll take a stab at attaining a scientific understanding of how this learning from interaction happens. Specifically, we’ll take a computational approach called reinforcement learning or RL for short. Since the world is quite complicated, we’ll simplify the…


The most common strategy that people use to make AI smarter is for the Machine Learning models to tell humans when they are uncertain about a task, and then ask the humans for the correct feedback. In general, unlabeled data that confuses a Machine Learning algorithm will be the most valuable when it is labeled and added to the training data. If the Machine Learning algorithm can already label an item with high confidence it is probably correct already.

Interpreting Uncertainty in a Machine Learning Model

Uncertainty Sampling is a strategy for identifying unlabeled items that are near a decision boundary in your current Machine Learning model…


You may not realize it, but you’ve probably used Active Learning before. Filtering your data by keyword or some other pre-processing step is a form of Active Learning, although not a very principled one. Because you are probably using filtered data by the time you build a Machine Learning model, it can be helpful to think of most Machine Learning problems as already being in the middle of the iteration process for Active Learning.

Interpreting model predictions and data to support Active Learning

Almost all Supervised Machine Learning models will give you to things:
> A predicted label (or set of predictions)
> A number (or set of numbers) associated with…


Unlike robots in the movies, most of today’s Artificial Intelligence (AI) cannot learn by itself: it relies on intensive human feedback. Probably 90% of Machine Learning applications today are powered by Supervised Machine Learning. So this raises one of the most important questions in technology today: what are the right ways for humans and machine learning algorithms to interact to solve problems?. this series of blogs help you to answer these questions.

The Basic Principles of Human-in-the-Loop Machine Learning

Human-in-the-Loop Machine Learning is when humans and Machine Learning processes interact to solve one or more of the following:
> Making Machine Learning more accurate
> Getting Machine…


Fundamental Knowledge

Practical


Sequence-to-sequence (S2S) models are a special case of a general family of models called encoder–decoder models. An encoder–decoder model is a composition of two models, an “encoder” and a “decoder,” that are typically jointly trained. The encoder model takes an input and produces an encoding or a representation (ϕ) of the input, which is usually a vector.1 The goal of the encoder is to capture important properties of the input with respect to the task at hand. The goal of the decoder is to take the encoded input and produce a desired output. …


Sequence prediction tasks require us to label each item of a sequence. Such tasks are common in natural language processing. Some examples include language modeling. in which we predict the next word given a sequence of words at each step; part-of-speech tagging, in which we predict the grammatical part of speech for each word; named entity recognition, in which we predict whether each word is part of a named entity, such as Person, Location, Product, or Organization; and so on. Sometimes, in NLP literature, the sequence prediction tasks are also referred to as sequence labeling.

Although in theory we can…


A sequence is an ordered collection of items. Traditional machine learning assumes data points to be independently and identically distributed (IID), but in many situations, like with language, speech, and time-series data, one data item depends on the items that precede or follow it. Such data is also called sequence data. Sequential information is everywhere in human language. For example, speech can be considered a sequence of basic units called phonemes. In a language like English, words in a sentence are not haphazard. They might be constrained by the words that come before or after them.

In deep learning, modeling…

Duy Anh Nguyen

AI Researcher - NLP Practitioner

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