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Small Language Models

Alfredo Deza and Noah Gift

In this comprehensive course, you will:

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In this comprehensive course, you will:

  • Gain a solid understanding of Small Language Models (SLMs) and their advantages
  • Explore real-world use cases for SLMs, from chatbots to language translation
  • Learn to develop applications using SLMs through hands-on labs and practical examples
  • Discover best practices for optimal SLM performance and reliability
  • Equip yourself with the skills to build efficient, effective NLP solutions

Whether you're a developer, data scientist, or machine learning enthusiast, this course will empower you to leverage the power of SLMs for your specific needs and build cutting-edge natural language processing applications.

Enroll now

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What's inside

Learning objectives

  • Understanding small language models and their characteristics
  • Leveraging high-quality, specialized training data for improved performance
  • Loading and running inference with pre-trained slms
  • Fine-tuning slms for specialized tasks
  • Developing applications using slms in production environments

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops knowledge, skills, and expertise in Small Language Models (SLMs) and NLP
Teaches best practices for optimal SLM performance and reliability
Appropriate for developers, data scientists, and machine learning enthusiasts who want to leverage SLM for NLP solutions
Emphasizes the use of real-world examples and hands-on labs for practical application
Taught by instructors who are recognized for their work in the field

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Save Small Language Models to your list so you can find it easily later:
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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 Small Language Models with these activities:
Review Python programming fundamentals
Refreshing your Python programming skills will ensure you have the necessary foundation to work with SLMs.
Browse courses on Python
Show steps
  • Review basic Python syntax.
  • Practice writing and executing Python code.
Review past course notes
Reviewing past notes will help you identify areas where you need to improve and better prepare for this course.
Show steps
  • Gather old notes and materials from your past courses related to natural language processing.
  • Organize and compile these materials in a way that makes them easy to find and use.
Read 'Deep Learning with Python'
This book provides a comprehensive foundation for deep learning, including a thorough explanation of SLMs and their applications.
View Deep Learning with TensorFlow and Keras - Third... on Amazon
Show steps
  • Obtain a copy of the book.
  • Read and understand the chapters covering SLMs.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete online tutorials on SLM frameworks
Completing online tutorials will supplement your understanding of SLMs and give you practical experience in using SLM frameworks.
Show steps
  • Identify and select online tutorials from reputable sources.
  • Follow the tutorials, complete the exercises, and apply what you learn to your own projects.
Join a study group or online forum
Engaging in discussions with peers will broaden your understanding of SLMs and NLP concepts, and will help you in your learning journey.
Show steps
  • Identify and join a study group or online forum related to SLMs or NLP.
  • Participate in discussions, ask questions, and share knowledge with other members.
Practice exercises using SLM APIs
Working through practice exercises will help you deepen your understanding of how to use SLMs in practice and implement them in real-world applications.
Show steps
  • Find and identify a public SLM API.
  • Read the documentation for the API and understand its features.
  • Use the API to perform various tasks, such as text classification, language translation, or chatbot development.
Complete a course on NLP with Tensorflow
This course will provide practical experience in using Tensorflow to build and deploy NLP models, including SLMs.
Show steps
  • Enroll in a course on NLP with Tensorflow.
  • Follow the course content, complete the assignments, and build your own NLP projects.
Develop a basic chatbot using SLMs
Developing a chatbot using SLMs will challenge you to apply your theoretical knowledge and practical skills in a meaningful way.
Show steps
  • Design and define the functionality of the chatbot.
  • Select and integrate an appropriate SLM.
  • Train and fine-tune the SLM on a relevant dataset.
  • Develop a user interface for interacting with the chatbot.

Career center

Learners who complete Small Language Models will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
Effort
2 to 3 hours per week for 2 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Alfredo Deza
Noah Gift
Session
April 23, 2024 - April 23, 2025
Language
English
Transcript
English
Good to know
Develops knowledge, skills, and expertise in Small Language Models (SLMs) and NLP
Teaches best practices for optimal SLM performance and reliability
Appropriate for developers, data scientists, and machine learning enthusiasts who want to leverage SLM for NLP solutions
Emphasizes the use of real-world examples and hands-on labs for practical application
Taught by instructors who are recognized for their work in the field
Share and help others discover this course.
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