Description
Deep Learning for Natural Language Processing LiveLessons (Video Training), 2nd Edition, is an introductory course in natural language processing using TensorFlow-Keras deep learning models. This course is an introduction to building natural language learning models using deep learning. With this course, you will get an intuitive explanation of theoretical topics while working with Jupyter notebook.
What you will learn in Deep Learning for Natural Language Processing LiveLessons (video training):
- Preprocessing natural language data used in machine learning applications
- Turn natural language into digital mode using word2vec
- Make predictions using deep learning models taught in natural language
- Using the latest natural language learning technologies using Keras
- Optimize the efficiency of deep learning models by selecting the appropriate model architecture and quantifying model hyperparameters
Course specifications
Publisher: Inform IT
Instructors: Jon Krohn
French language
Learning level: Medium
Number of lessons: 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 practical code examples in Jupyter notebooks
1.3 Review of prerequisite deep learning theory
1.4 An overview
Lesson 2: Word Vectors
Topics
2.1 Computational representations of natural language elements
2.2 Visualize 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: Natural Language Data Modeling
Topics
3.1 Best Practices for Natural Language Data Preprocessing
3.2 The area under the ROC curve
3.4 Classification of documents with a dense neural network
3.5 Classification with a convolutional neural network
Lessons 4: Recurrent Neural Networks
Topics
4.1 Essential theory of RNNs
4.2 RNN in practice
4.3 Essential theory of LSTMs and GRUs
4.4 LSTM and GRU in practice
Lesson 5: Advanced Models
Topics
5.1 Bidirectional LSTMs
5.2 Stacked LSTMs
5.3 Datasets for TAL
5.4 Sequence generation
5.5 seq2seq and Caution
5.6 Transfer learning in NLP: BERT, ELMo, GPT-2 and other characters
5.7 Non-sequential architectures: API
5.8 Time Series (Financial) Applications
Summary
Course prerequisites:
The authors Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons or familiarity with the topics covered in chapters 5-9 of his book Illustrated deep learning are a prerequisite.
Pictures
sample movie
Installation guide
After ripping, watch with your favorite player.
Subtitle: None
Quality: 720p
Download link
File password(s): ngaur.com
Cut
8.3 GB