Getting Started with NLP Deep Learning Using PyTorch 1 and fastai 1 from Pluralsight

This course will teach you how to start using fastai library and PyTorch to obtain near-state-of-the-art results with Deep Learning NLP for text classification. It will give you a theoretical background and show how to take models to production.

In this course, Getting Started with NLP Deep Learning Using PyTorch and fastai, we'll have a look at the amazing fastai library, built on top of the PyTorch Deep Learning Framework, to learn how to perform Natural Language Processing (NLP) with Deep Neural Networks, and how to achieve some of the most recent state-of-the-art results in text classification.

First, we’ll learn how to train a model for text classification very quickly, thanks to the fastai library and transfer learning. Next, we'll explore some of the theory behind Deep Learning NLP techniques, and how to deploy our models to production in Microsoft Azure. Finally, we’ll discover how to train a custom language model from scratch.

When you’re finished with this course, you’ll know why fastai and PyTorch are great frameworks, how to train deep learning models for NLP tasks on your own datasets, and how to bring them to production.

What's inside

Syllabus

Course Overview

Exploring the fastai Library

Setting up a Development Environment

Building a Text/Topic Classifier with Transfer Learning

Using Deep Learning for NLP

Going from Prototype to Production

Building a Custom Language Model from Scratch

Recapping and Next Steps

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Gianni Rosa Gallina, who is established in the field

Develops knowledge and skills that are essential for NLP and text classification

Uses fastai, a popular library known for its ease of use and efficiency

Builds on top of PyTorch, a well-established deep learning framework

Emphasizes practical application, with a focus on taking models to production

Covers advanced topics such as training custom language models, which are highly sought-after skills

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 Getting Started with NLP Deep Learning Using PyTorch 1 and fastai 1 with these activities:

Compile a list of resources on NLP with PyTorch and fastai

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Organizes and expands knowledge by fostering research and gathering relevant materials.

Browse courses on NLP

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Search for articles, tutorials, and documentation on NLP with PyTorch and fastai.
Create a shared document or online repository to store the resources.

Review 'Deep Learning with PyTorch' by Manning and Chatterjee

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Provides a comprehensive overview of deep learning and PyTorch framework to prepare for this course.

View Deep Learning with PyTorch: Build, train, and... on Amazon

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Read Chapters 1-3 to understand the basics of deep learning and PyTorch.
Work through the exercises in Chapters 1-3 to practice using PyTorch.

Join a study group and discuss course topics

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Encourages collaboration, knowledge sharing, and deeper understanding of course material.

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Find or form a study group with fellow students.
Meet regularly to discuss course concepts, assignments, and projects.

Five other activities

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Complete 'PyTorch Tutorial for Deep Learning' on Coursera

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Offers hands-on experience with PyTorch and deep learning concepts, reinforcing course material.

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Enroll in the Coursera course and complete the video lectures.
Follow along with the coding exercises and quizzes.

Solve PyTorch coding challenges on LeetCode

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Provides targeted practice with PyTorch coding skills, strengthening the foundation for the course.

Browse courses on PyTorch

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Identify easy-to-medium level PyTorch coding challenges on LeetCode.
Attempt to solve the challenges on your own.
Review solutions and discuss approaches with peers or mentors.

Build a simple text classification model with fastai

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Applies course concepts to a practical project, fostering hands-on understanding.

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Choose a small text classification dataset.
Load the dataset and preprocess the text.
Train a fastai text classification model.
Evaluate the model's performance.

Participate in a Kaggle competition on NLP text classification

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Challenges students to apply their skills in a competitive environment, fostering problem-solving and innovation.

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Identify a relevant Kaggle competition on NLP text classification.
Form a team or participate individually.
Develop and submit a solution to the competition.

Mentor a beginner in PyTorch and NLP

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Reinforces understanding by explaining concepts and providing guidance to others, solidifying knowledge.

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Identify a beginner who needs support in PyTorch and NLP.
Provide guidance and support on specific topics or projects.

Career center

Learners who complete Getting Started with NLP Deep Learning Using PyTorch 1 and fastai 1 will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

Natural Language Processing Engineers build and maintain NLP models. NLP is a rapidly growing field, so there is a high demand for qualified engineers. This course will give you the skills and knowledge you need to be successful as a Natural Language Processing Engineer.

See salaries and explore the career path for Natural Language Processing Engineer

Data Scientist

In recent years, Data Scientists have become increasingly in demand. This course, Getting Started with NLP Deep Learning Using PyTorch and fastai, provides a solid foundation in Natural Language Processing (NLP), a crucial skill for many Data Scientists. Those looking to enter this field should take this course because it will teach them about NLP techniques and how to apply them to real-world problems.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course will provide you with the skills you need to build and deploy NLP models. NLP is a key technology used by Machine Learning Engineers to help their models understand text data.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. NLP is a key technology used by Software Engineers in a variety of industries. This course will give you the skills you need to build and deploy NLP models in your own software applications.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts use data to solve business problems. NLP is a key technology used by Data Analysts to help them understand text data. This course will give you the skills you need to use NLP to solve business problems.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to help businesses make better decisions. NLP is a key technology used by Business Analysts to help them understand customer feedback and other text data. This course will give you the skills you need to use NLP to make better business decisions.

See salaries and explore the career path for Business Analyst

Marketing Manager

Marketing Managers are responsible for planning and executing marketing campaigns. NLP is a key technology used by Marketing Managers to help them understand customer feedback and other text data. This course will give you the skills you need to use NLP to create more effective marketing campaigns.

See salaries and explore the career path for Marketing Manager

Product Manager

Product Managers are responsible for planning and developing new products and features. NLP is a key technology used by Product Managers to help them understand customer feedback and other text data. This course will give you the skills you need to use NLP to build better products.

See salaries and explore the career path for Product Manager

Sales Manager

Sales Managers are responsible for leading and managing sales teams. NLP is a key technology used by Sales Managers to help them understand customer needs and close deals.

See salaries and explore the career path for Sales Manager

Customer Success Manager

Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. NLP is a key technology used by Customer Success Managers to help them understand customer feedback and resolve issues.

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Technical Writer

Technical Writers create documentation and other materials to help people use products and services. NLP is a key technology used by Technical Writers to help them understand complex technical concepts and write clear and concise documentation.

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Content Writer

Content Writers create content for websites, blogs, and other marketing materials. NLP is a key technology used by Content Writers to help them understand how people search for information online and write content that is both informative and engaging.

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Social Media Manager

Social Media Managers are responsible for managing a company's social media presence. NLP is a key technology used by Social Media Managers to help them understand customer feedback and trends.

See salaries and explore the career path for Social Media Manager

UI Designer

UI Designers are responsible for designing the user interface for products and services. NLP is a key technology used by UI Designers to help them understand how people interact with products and services.

See salaries and explore the career path for UI Designer

UX Designer

UX Designers are responsible for designing the user experience for products and services. NLP is a key technology used by UX Designers to help them understand how people interact with products and services.

See salaries and explore the career path for UX Designer