Description
Deep Learning for Natural Language Processing LiveLessons (Video Training), 2nd Edition, is an introductory training course for natural language processing using TensorFlow-Keras deep learning models. This course is an introduction to the construction of natural language learning models using deep learning, with the lessons of this course, you will have an intuitive explanation of theoretical topics along with working with Jupyter notebook in a practical way.
What you will learn in Deep Learning for Natural Language Processing LiveLessons (Video Training) course:
- Preprocessing of natural language data used in machine learning applications
- Transforming natural language into numeric form using word2vec
- Making predictions using deep learning models trained using natural language
- Applying the latest natural language learning technologies using Keras
- Optimizing the performance of deep learning models by choosing the appropriate model architecture and scaling the hyperparameters of the models
Course details
Publisher: InformIT
Instructors: Jon Krohn
English language
Education level: Intermediate
Number of courses: 34
Duration: 4 hours and 59 minutes
Course topics:
Introduction
Lesson 1: The Power and Elegance of Deep Learning for NLP
Topics
1.1 Introduction to Deep Learning for Natural Language Processing
1.2 Running the Hands-On Code Examples in Jupyter Notebooks
1.3 Review of Prerequisite Deep Learning Theory
1.4 A Sneak Peek
Lesson 2: Word Vectors
Topics
2.1 Computational Representations of Natural Language Elements
2.2 Visualizing Word Vectors with word2viz
2.3 Localist Versus Distributed Representations
2.4 Elements of Natural Human Language
2.5 The word2vec Algorithm
2.6 Creating Word Vectors with word2vec
2.7 Pre-Trained Word Vectors and doc2vec
Lesson 3: Modeling Natural Language Data
Topics
3.1 Best Practices for Preprocessing Natural Language Data
3.2 The Area Under the ROC Curve
3.4 Document Classification with a Dense Neural Net
3.5 Classification with a Convolutional Neural Net
Lessons 4: Recurrent Neural Networks
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
Lesson 5: Advanced Models
Topics
5.1 Bi-Directional LSTMs
5.2 Stacked LSTMs
5.3 Datasets for NLP
5.4 Sequence Generation
5.5 seq2seq and Attention
5.6 Transfer Learning in NLP: BERT, ELMo, GPT-2 and Other Characters
5.7 Non-Sequential Architectures: The Keras Functional API
5.8 (Financial) Time Series Applications
Summary
Course prerequisites:
The author’s Deep Learning with TensorFlow, Keras, and PyTorch LiveLessonsor familiarity with the topics covered in Chapters 5 through 9 of his book Deep Learning Illustratedare a prerequisite.
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
Subtitle: None
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
8.3 GB
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