Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing
Deep Learning for Natural Language Processing

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5h 26m | 2.22 GB
eLearning | Skill level: Intermediate


Applications of Deep Neural Networks to Machine Learning Tasks

An intuitive introduction to processing natural language data with Deep Learning models Deep Learning for Natural Language Processing LiveLessons is an introduction to processing natural language with Deep Learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library. In the early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In the later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data.

Learn How To

  • Preprocess natural language data for use in machine learning applications
  • Transform natural language into numerical representations with word2vec
  • Make predictions with Deep Learning models trained on natural language
  • Apply state-of-the-art NLP approaches with Keras, the high-level TensorFlow API
  • Improve Deep Learning model performance by tuning hyperparameters
+ Table of Contents

0.0 Deep Learning for Natural Language Processing – Introduction
1.0 Topics
1.1 Introduction to Deep Learning for Natural Language Processing
1.2 Computational Representations of Natural Language Elements
1.3 NLP Applications
1.4 Installation, Including GPU Considerations
1.5 Review of Prerequisite Deep Learning Theory
1.6 A Sneak Peak
2.0 Topics
2.1 Vector-Space Embedding
2.2 word2vec
2.3 Data Sets for NLP
2.4 Creating Word Vectors with word2vec
3.0 Topics
3.1 Best Practices for Preprocessing Natural Language Data
3.2 The Area Under the ROC Curve
3.3 Dense Neural Network Classification
3.4 Convolutional Neural Network Classification
4.0 Topics
4.1 Essential Theory of RNNs
4.2 RNNs in Practice
4.3 Essential Theory of LSTMs and GRUs
4.4 LSTMs and GRUs in Practice
5.0 Topics
5.1 Bi-Directional LSTMs
5.2 Stacked LSTMs
5.3 Parallel Network Architectures
5.4 Hyperparameter Tuning
6.0 Deep Learning for Natural Language Processing – Summary

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