Natural Language Processing with PyTorch from Pluralsight

This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch.

From chatbots to machine-generated literature, some of the hottest applications of ML and AI these days are for data in textual form. In this course, Natural Language Processing with PyTorch, you will gain the ability to design and implement complex text processing models using PyTorch, which is fast emerging as a popular choice for building deep-learning models owing to its flexibility, ease-of-use, and built-in support for optimized hardware such as GPUs. First, you will learn how to leverage recurrent neural networks (RNNs) to capture sequential relationships within text data. Next, you will discover how to express text using word vector embeddings, a sophisticated form of encoding that is supported by out-of-the-box in PyTorch via the torchtext utility. Finally, you will explore how to build complex multi-level RNNs and bidirectional RNNs to capture both backward and forward relationships within data. You will round out the course by building sequence-to-sequence RNNs for language translation. When you are finished with this course, you will have the skills and knowledge to design and implement complex natural language processing models using sophisticated recurrent neural networks in PyTorch.

What's inside

Syllabus

Course Overview

Implementing Recurrent Neural Networks (RNNs) in PyTorch

Performing Binary Text Classification Using Words

Performing Multi-class Text Classification Using Characters

Performing Sentiment Analysis Using Word Embeddings

Performing Language Translation Using Sequence-to-Sequence Models

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers advanced topics relevant to natural language processing, such as word embeddings, RNNs, and bidirectional RNNs

Taught by instructors, Janani Ravi, who are recognized for their work in deep learning and natural language processing

Utilizes PyTorch, a popular deep learning framework that is widely used in industry

Provides hands-on experience through building and implementing complex natural language processing models

Suitable for learners with a background in deep learning and natural language processing

May require additional resources or prerequisites to fully grasp the concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Natural Language Processing with PyTorch with these activities:

Review Basic Python Programming

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Strengthen Python foundation to prepare for implementing RNN models in PyTorch

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Review Python syntax and data structures
Practice writing basic Python programs

Review 'Natural Language Processing with PyTorch'

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Reinforce course concepts by reviewing a comprehensive reference book on Natural Language Processing with PyTorch

View Natural Language Processing with PyTorch: Build... on Amazon

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Read key chapters relevant to course topics
Summarize important concepts and techniques
Review examples and practice exercises

Explore Advanced PyTorch Tutorials

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Expand knowledge of PyTorch's capabilities and best practices through curated tutorials

Browse courses on PyTorch

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Identify reputable online resources or tutorials
Follow tutorials to implement advanced PyTorch features
Experiment with different techniques and compare results

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Attend RNN-Focused Study Group

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Engage with peers to discuss and clarify RNN concepts, boosting comprehension

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Find or create a study group focused on RNNs
Participate in group discussions and Q&A sessions
Collaborate on practical exercises or projects

Drill Recurrent Neural Network (RNN) Implementations

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Solidify understanding of RNN architectures through repetitive exercises

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Review RNN equations and operations
Implement Vanilla RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models in PyTorch
Run experiments to compare performance of different RNN architectures

Develop a Text Classification Model

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Apply RNNs to solve a practical text classification problem, deepening understanding of their capabilities

Browse courses on Text Classification

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Choose a text classification dataset
Design and train an RNN-based text classification model using PyTorch
Evaluate model performance and fine-tune parameters
Create a presentation or write a report to explain results

Develop a Sentiment Analysis Tool

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Apply RNNs with word embeddings to solve a real-world sentiment analysis problem, solidifying practical understanding

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Gather a dataset of text with sentiment labels
Train an RNN-based sentiment analysis model using PyTorch and word embeddings
Deploy the model as a user-friendly tool for sentiment analysis
Write documentation or create a tutorial to share the tool

Build a Language Translation Model

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Challenge understanding by building a complex language translation model using advanced RNN architectures, such as Transformers

Browse courses on Language Translation

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Gather a parallel language dataset
Design and implement a Transformer-based language translation model in PyTorch
Train and evaluate the model on the dataset
Deploy the model and test its performance on real-world data

Career center

Learners who complete Natural Language Processing with PyTorch will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

Natural Language Processing Engineers develop and maintain natural language processing models. They use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course is a great fit for Natural Language Processing Engineers who want to learn more about natural language processing.

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Computational Linguist

Computational Linguists use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course is a great fit for Computational Linguists who want to learn more about natural language processing.

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Data Scientist

Data Scientists use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Data Scientists who want to learn more about natural language processing.

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Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models. They use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Machine Learning Engineers who want to learn more about natural language processing.

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Business Analyst

Business Analysts use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Business Analysts who want to learn more about natural language processing.

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Software Engineer

Software Engineers develop and maintain software applications. They use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Software Engineers who want to learn more about natural language processing.

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Product Manager

Product Managers develop and manage software products. They use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Product Managers who want to learn more about natural language processing.

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Marketing Manager

Marketing Managers use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Marketing Managers who want to learn more about natural language processing.

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Technical Writer

Technical Writers use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Technical Writers who want to learn more about natural language processing.

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Entrepreneur

Entrepreneurs use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Entrepreneurs who want to learn more about natural language processing.

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Customer Success Manager

Customer Success Managers use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Customer Success Managers who want to learn more about natural language processing.

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Research Scientist

Research Scientists conduct research in the field of natural language processing. They use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Research Scientists who want to learn more about natural language processing.

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Freelance Writer

Freelance Writers use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Freelance Writers who want to learn more about natural language processing.

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Consultant

Consultants use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Consultants who want to learn more about natural language processing.

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Sales Manager

Sales Managers use their knowledge of natural language processing to develop models that can understand and generate human language. This course covers the use of advanced neural network constructs and architectures, such as recurrent neural networks, word embeddings, and bidirectional RNNs, to solve complex word and language modeling problems using PyTorch. This course may be useful for Sales Managers who want to learn more about natural language processing.

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