Training: Deep Learning for NLP

Data Analist

15 uur

Engels (US)

Training: Deep Learning for NLP

Data Analist

15 uur

Engels (US)

Deep Learning for NLP

Productinformatie

Natural language processing (NLP) heeft de laatste tijd veel vooruitgang geboekt, waarvan het meeste in deep learning-modellen. NLP als een probleem is erg ingewikkeld, en deep learning-modellen kunnen dit vergemakkelijken met behulp van verschillende variaties van neurale netwerkarchitectuur. Hoe dit in de praktijk werkt, leer je tijdens deze training. Je begint met de basis van NLP en al snel ga je door naar Neural Network Architectures (NNA). NNA biedt een methode voor het verwerken van op taal gebaseerde informatie om complexe datagestuurde problemen op te lossen. Vervolgens leer je de basisprincipes van memory-based networks en hoe je TensorFlow implementeert om met uitgebreide context in talen om te gaan.

Het essentiële aspect van menselijke intelligentie zijn onze leerprocessen, voortdurend aangevuld met de overdracht van concepten en fundamenten. Deze training helpt je de basisprincipes van transfer learning voor NLP, de verschillende uitdagingen en gebruiksscenario's te leren kennen. Ten slotte verken je GitHub-bugvoorspellingsanalyses om real-life problemen op te lossen.

Inhoud van de training

Deep Learning for NLP

15 uur

Deep Learning for NLP: Introduction

In recent times, natural language processing (NLP) has seen many
advancements, most of which are in deep learning models. NLP as a
problem is very complicated, and deep learning models can handle
that scale and complication with many different variations of
neural network architecture. Deep learning also has a broad
spectrum of frameworks that supports NLP problem solving
out-of-the-box. Explore the basics of deep learning and different
architectures for NLP-specific problems. Examine other use cases
for deep learning NLP across industries. Learn about various tools
and frameworks used such as - Spacy, TensorFlow, PyTorch, OpenNMT,
etc. Investigate sentiment analysis and explore how to solve a
problem using various deep learning steps and frameworks. Upon
completing this course, you will be able to use the essential
fundamentals of deep learning for NLP and outline its various
industry use cases, frameworks, and fundamental sentiment analysis
problems.

Deep Learning for NLP: Neural Network Architectures

Natural language processing (NLP) is constantly evolving with
cutting edge advancements in tools and approaches. Neural network
architecture (NNA) supports this evolution by providing a method of
processing language-based information to solve complex data-driven
problems. Explore the basic NNAs relevant to NLP problems. Learn
different challenges and use cases for single-layer perceptron,
multi-layer perceptron, and RNNs. Analyze data and its distribution
using pandas, graphs, and charts. Examine word vector
representations using one-hot encodings, Word2vec, and GloVe and
classify data using recurrent neural networks. After you have
completed this course, you will be able to use a product
classification dataset to implement neural networks for NLP
problems.

Deep Learning for NLP: Memory-based Networks

In the journey to understand deep learning models for natural
language processing (NLP), the subsequent iterations are
memory-based networks, which are much more capable of handling
extended context in languages. While basic neural networks are
better than machine learning (ML) models, they still lack in more
significant and large language data problems. In this course, you
will learn about memory-based networks like gated recurrent unit
(GRU) and long short-term memory (LSTM). Explore their
architectures, variants, and where they work and fail for NLP.
Then, consider their implementations using product classification
data and compare different results to understand each
architecture's effectiveness. Upon completing this course, you will
have learned the basics of memory-based networks and their
implementation in TensorFlow to understand the effect of memory and
more extended context for NLP datasets.

Deep Learning for NLP: Transfer Learning

The essential aspect of human intelligence is our learning
processes, constantly augmented with the transfer of concepts and
fundamentals. For example, as a child, we learn the basic alphabet,
grammar, and words, and through the transfer of these fundamentals,
we can then read books and communicate with people. This is what
transfer learning helps us achieve in deep learning as well. This
course will help you learn the fundamentals of transfer learning
for NLP, its various challenges, and use cases. Explore various
transfer learning models such as ELMo and ULMFiT. Upon completing
this course, you will understand the transfer learning methodology
of solving NLP problems and be able to experiment with various
models in TensorFlow.

Deep Learning for NLP: GitHub Bug Prediction Analysis

Get down to solving real-world GitHub bug prediction problems in
this case study course. Examine the process of data and library
loading and perform basic exploratory data analysis (EDA) including
word count, label, punctuation, and stop word analysis. Explore how
to clean and preprocess data in order to use vectorization and
embeddings and use counter vector and term frequency-inverse
document frequency (TFIDF) vectorization methods with
visualizations. Finally, assess different classifiers like logistic
regression, random forest, or AdaBoost. Upon completing this
course, you will understand how to solve industry-level problems
using deep learning methodology in the TensorFlow ecosystem.

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Docent inbegrepen

Bereidt voor op officieel examen

Engels (US)

15 uur

Data Analist

90 dagen online toegang

HBO

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Doelgroep	Data-analist
Voorkennis	Geen formele voorwaarden. Enige voorkennis over het onderwerp wordt echter aanbevolen.
Resultaat	Na het afronden van deze training, ben je in staat om de essentiële basisprincipes van deep learning voor NLP te gebruiken en de verschillende industriële use-cases, frameworks én fundamentele sentimentanalyseproblemen te schetsen. Daarnaast ben je ook in staat om een productclassificatiedataset te schetsen om neural networks voor NLP-problemen te implementeren. Verder heb je de basis geleerd van memory-based networks en hun implementatie in TensorFlow, om het effect van geheugen en een brede context voor NLP-datasets te begrijpen. Je zult ook de transfer learning-methodologie voor het oplossen van NLP-problemen begrijpen en in staat zijn om te experimenteren met verschillende modellen in TensorFlow, evenals hoe je problemen op industrieniveau kunt oplossen met behulp van deep learning-methodologie in het TensorFlow-ecosysteem.

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Training: Leidinggeven aan de AI transformatie

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Onbeperkt Leren aangeschaft omdat je veel waar voor je geld krijgt. Ik gebruik het nog maar kort, maar eerste indruk is goed.

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Training: Deep Learning for NLP

Productinformatie

Inhoud van de training

Deep Learning for NLP

Deep Learning for NLP: Introduction

Deep Learning for NLP: Neural Network Architectures

Deep Learning for NLP: Memory-based Networks

Deep Learning for NLP: Transfer Learning

Deep Learning for NLP: GitHub Bug Prediction Analysis

Kenmerken

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Training: Deep Learning for NLP

Productinformatie

Inhoud van de training

Deep Learning for NLP

Deep Learning for NLP: Introduction

Deep Learning for NLP: Neural Network Architectures

Deep Learning for NLP: Memory-based Networks

Deep Learning for NLP: Transfer Learning

Deep Learning for NLP: GitHub Bug Prediction Analysis

Kenmerken

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