Deep Learning for NLP E-Learning Training
Bestel nu de unieke Deep Learning for NLP E-Learning Training en krijg 1 jaar lang, 24/7 toegang tot een innovatieve online leeromgeving met interactieve video’s, gesproken uitleg, voortgangsrapportages en toetsen per hoofdstuk om je kennis direct te toetsen.
In deze cursus leer je hoe deep learning wordt toegepast binnen Natural Language Processing (NLP) — een belangrijk domein binnen AI waarin menselijke taal en machine learning samenkomen. Je verkent geavanceerde modellen zoals word embeddings, recurrent neural networks (RNNs), transformers en BERT, en leert hoe je deze inzet voor toepassingen zoals sentimentanalyse, tekstgeneratie en automatische vertaling.
Waarom kiezen voor deze opleiding?
- Toegang tot een bekroond e-learningplatform met 1 jaar 24/7 toegang
- Leer hoe deep learning NLP transformeert via hands-on uitleg en theorie
- Per hoofdstuk toetsen en rapportages om je voortgang te meten
- Ideaal als voorbereiding op AI- of data science-functies
- Inclusief certificaat van deelname
Wie zou moeten deelnemen?
- AI- en data science-professionals die zich willen verdiepen in NLP
- Softwareontwikkelaars en machine learning engineers
- Studenten in informatica, taaltechnologie of kunstmatige intelligentie
- Professionals die NLP willen toepassen binnen hun organisatie
- Iedereen met basiskennis van Python en machine learning
Cursusinhoud
Deep Learning for NLP: Introduction
Course: 1 Hour, 18 Minutes
- Course Overview
- NLP with Deep Learning
- NLP Use Cases in Deep Learning
- Basic Deep Learning Frameworks
- Intermediate Deep Learning Frameworks
- Advanced Deep Learning Frameworks
- Introduction to Sentiment Data
- Using Deep Learning Pipelines for Sentiment Data
- Sentiment Analysis - Overview & Data
- Sentiment Analysis - EDA
- Sentiment Analysis - Pre-processing
- Sentiment Analysis - Modeling & Evaluation
- Sentiment Analysis - Creating Accuracy & Loss Graphs
- Course Summary
Deep Learning for NLP: Neural Network Architectures
Course: 2 Hours, 30 Minutes
- Course Overview
- Basic Architecture of a Neural Network
- Multilayer Perceptron (MLP)
- Recurrent Neural Network (RNN) Architecture
- Challenges in RNN
- Applications of Neural Network-based Architecture
- Introducing the Product Reviews Data
- Loading Product Reviews Data into Google Colaboratory
- Understanding Product Reviews Data
- Exploring Product Reviews Data
- Pre-processing Product Reviews Data
- Applying Feature Engineering - Word Representation
- Creating Vector Representations Using Word2vec
- Averaging Feature Vectors
- Creating Word Embeddings with Word2Vec
- Constructing a RNN Model with Word2vec Embeddings
- Using GloVe Vectors
- Product Reviews Classification Using RNN
- Course Summary
Deep Learning for NLP: Memory-based Networks
Course: 1 Hour, 27 Minutes
- Course Overview
- Introduction to Memory-based Networks
- Gated Recurrent Unit (GRU) Architecture
- Long Short-term Memory (LSTM) Architecture
- Fall of RNN versus Rise of LSTM
- Variants of LSTM networks
- Product Review Data Preparation for Modeling
- Product Review Data Classification Using GRU
- Product Review Data Classification Using LSTM
- Product Review Data Classification Using Bi-LSTM
- Result Comparison between RNN, GRU, and LSTM
- Course Summary
Deep Learning for NLP: Transfer Learning
Course: 2 Hours, 10 Minutes
- Course Overview
- Introduction to Transfer Learning
- Advantages and Challenges of Transfer Learning
- Role of Language Modeling in Transfer Learning
- Introduction to Basic Transfer Learning Models
- Intermediate Transfer Learning Models
- Advance Transfer Learning Models
- Building ELMo Embedding Layer for Reviews
- Creating ELMo an Model for Product Reviews
- Classifying Product Reviews Using ELMo
- Reshaping Data for the ELMo Embedding Layer
- Building a Language Model Using ULMFiT
- Implementing the Language Model Using ULMFiT
- Classifying Product Reviews Using ULMFIT & FastText
- Performing Result Comparison
- Course Summary
Deep Learning for NLP: GitHub Bug Prediction Analysis
Course: 1 Hour, 56 Minutes
