Deep Learning for NLP - Deep Learning - Artificial Intelligence - Machine learning - Data Science

Na inschrijving van de Award Winning E-learning training Deep Learning for NLP, ontvangt u per e-mail een link om in te loggen in uw leeromgeving waar u een persoonlijk wachtwoord aanmaakt. Eenmaal ingelogd in uw persoonlijke leeromgeving ziet u een overzicht van de cursusonderdelen. Via de inhoudsopgave schakelt u automatisch over naar elk gewenst onderdeel van de training.

Er zit tevens een Voortgangsbewaking bij om eenvoudig te zien hoe ver u bent binnen uw training. U kunt daarbij 1 jaar lang (365 dagen), 24/7 (elke dag en nacht) inloggen om verder te gaan met de training.

Deze Engelstalige training met ondertiteling heeft interactieve, eenvoudig te volgen video's in HD beeldkwaliteit met heldere audio kwaliteit. Daarnaast biedt de training Support en/of een Online Mentor aan als u problemen ondervindt. De training is beschikbaar in elke browser voor zowel PC, Mac, Tablet én Smartphone. Dus zelfs via uw mobiel kunt u handig de training volgen.

De training is inclusief lees- en/of praktijkopdrachten met trainingstest, mits noodzakelijk voor de training. Na afronding van de training krijgt u een Certificaat van Deelname en sluit u zich aan bij de reeds duizenden tevreden cursisten.

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

• Course Overview 
• Case Study: Introduction to GitHub Bug Prediction
• Case Study: Loading Data & Libraries
• Case Study: Understanding the Data
• Case Study: Basic Exploratory Data Analysis
• Case Study: Punctuation & Stop Word Analysis
• Case study: Advanced Data Preprocessing
• Case Study: Data Cleaning
• Case Study: Exploring Vectorization
• Case Study: Exploring Embeddings
• Case Study: Applying Deep Learning Modeling
• Case Study: Performing Model Comparison
• Course Summary