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Duy Anh Nguyen
Duy Anh Nguyen

116 Followers

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May 7, 2021

Pandas Fluency — Introducing Pandas (P1)

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…

Data Science

5 min read

Pandas Fluency — Introducing Pandas (P1)
Pandas Fluency — Introducing Pandas (P1)
Data Science

5 min read


Sep 5, 2020

loop#1 Hyper-parameters, The Lottery Ticket Hypothesis, and Weight&Biases platform

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…

4 min read

loop#1 Hyper-parameters, The Lottery Ticket Hypothesis, and Weight&Biases platform
loop#1 Hyper-parameters, The Lottery Ticket Hypothesis, and Weight&Biases platform

4 min read


Jul 2, 2020

Deep Reinforcement Learning part 1 — Hello World !!!!

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…

17 min read

Deep Reinforcement Learning part 1 — Hello World !!!!
Deep Reinforcement Learning part 1 — Hello World !!!!

17 min read


Mar 31, 2020

NLP onboarding

Fundamental Knowledge NLP Fundamentals — A Primer(P1) Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and…medium.com NLP Fundamentals — Text Classifier(P2) “Organizing is what you do before you do something, so that when you do it, it is not all mixed up”medium.com NLP Fundamentals — Text Classifier(P3) In this section we discussed feature engineering techniques using neural networks, such as word-embeddings…medium.com

1 min read

1 min read


Mar 6, 2020

NLP Fundamentals — Sequence Modeling (P7)

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…

6 min read

NLP Fundamentals — Sequence Modeling (P7)
NLP Fundamentals — Sequence Modeling (P7)

6 min read


Mar 5, 2020

NLP Fundamentals — Sequence Modeling (P6)

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…

4 min read

NLP Fundamentals — Sequence Modeling (P6)
NLP Fundamentals — Sequence Modeling (P6)

4 min read


Mar 5, 2020

NLP Fundamentals — Sequence Modeling (P5)

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…

3 min read

NLP Fundamentals — Sequence Modeling (P5)
NLP Fundamentals — Sequence Modeling (P5)

3 min read


Mar 5, 2020

NLP Fundamentals — Embedding Words(P4)

Representing discrete types (e.g., words) as dense vectors is at the core of deep learning’s successes in NLP. The terms “representation learning” and “embedding” refer to learning this mapping from one discrete type to a point in the vector space. …

6 min read

NLP Fundamentals — Embedding Words(P4)
NLP Fundamentals — Embedding Words(P4)

6 min read


Mar 4, 2020

NLP Fundamentals — Text Classifier(P3)

In this section we discussed feature engineering techniques using neural networks, such as word-embeddings, character-embeddings. The advantage of using embedding based features is that they create a dense, low-dimensional feature representation instead of the sparse, high-dimensional structure of bag of words/TFIDF and other such features. …

15 min read

NLP Fundamentals — Text Classifier(P3)
NLP Fundamentals — Text Classifier(P3)

15 min read


Mar 3, 2020

NLP Fundamentals — Text Classifier(P2)

“Organizing is what you do before you do something, so that when you do it, it is not all mixed up” In this Section we will look at one of the most popular tasks in NLP — text classification. It concerns with assigning one or more groups for a given…

9 min read

NLP Fundamentals — Text Classifier(P2)
NLP Fundamentals — Text Classifier(P2)

9 min read

Duy Anh Nguyen

Duy Anh Nguyen

116 Followers

AI Researcher - NLP Practitioner

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