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Small Language Models

Alfredo Deza and Noah Gift

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

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

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

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

Traffic lights

Read about what's good

what should give you pause

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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Reviews summary

Practical slm applications and fundamentals

According to students, this course offers a largely positive and solid introduction to Small Language Models. Learners frequently commend the clear explanations provided by the knowledgeable instructors, finding that they simplify complex topics effectively. The course is particularly praised for its practical, hands-on labs and exercises, which are described as incredibly helpful for building confidence and applying concepts to real-world use cases and production environments. While many find it an excellent resource for getting started and building a strong foundation, some with prior ML experience felt it might be too basic, wishing for more advanced topics and deeper dives.

Best suited for those new to SLMs or with foundational ML knowledge.

"This course is fantastic for anyone looking to get started with SLMs."

"A very solid introduction to Small Language Models... highly recommended for beginners to intermediate learners."

"The course provides a decent overview of SLMs, but I felt it was a bit too basic for my needs."

"Good course for understanding the basics of SLMs. It gives a good foundation."

Teaches practical skills for applying SLMs in real-world scenarios.

"I especially appreciated the modules on fine-tuning and deploying models in a production environment."

"The focus on real-world use cases is a major plus, and the hands-on elements are fantastic."

"The production deployment module was somewhat useful, but overall, it felt more like an introduction than an in-depth course for experienced professionals."

"I've already applied some of what I learned to my work."

Instructors effectively simplify complex concepts for learners.

"The instructors explain complex concepts very clearly, and the hands-on labs are incredibly helpful."

"The instructors are very knowledgeable. I particularly enjoyed the hands-on exercises."

"Absolutely brilliant! The instructors make even complex topics seem simple."

Strong emphasis on practical exercises that build confidence.

"The hands-on labs are incredibly helpful. I especially appreciated the modules on fine-tuning and deploying models in a production environment."

"I particularly enjoyed the hands-on exercises which helped me build confidence."

"The hands-on coding and projects are the strongest part of the course for me."

"The labs were effective. It gives a good foundation."

Some users faced challenges with environment setup and forum support.

"I encountered some issues setting up the environment, and the forum support was slow."

May be too basic for experienced professionals seeking deep dives.

"My only minor critique is that some of the advanced optimization techniques were only briefly touched upon. I would have liked more depth there."

"As someone with prior ML experience, I was hoping for more advanced topics and deeper dives into the nuances of model architecture and scaling."

"I found some of the explanations to be superficial. Not ideal for a standalone learning experience."

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

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Refreshing your Python programming skills will ensure you have the necessary foundation to work with SLMs.

Browse courses on Python

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Review basic Python syntax.
Practice writing and executing Python code.

Review past course notes

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Reviewing past notes will help you identify areas where you need to improve and better prepare for this course.

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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'

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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

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Obtain a copy of the book.
Read and understand the chapters covering SLMs.

Five other activities

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Show all eight activities

Complete online tutorials on SLM frameworks

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Completing online tutorials will supplement your understanding of SLMs and give you practical experience in using SLM frameworks.

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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

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Engaging in discussions with peers will broaden your understanding of SLMs and NLP concepts, and will help you in your learning journey.

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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

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Working through practice exercises will help you deepen your understanding of how to use SLMs in practice and implement them in real-world applications.

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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

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This course will provide practical experience in using Tensorflow to build and deploy NLP models, including SLMs.

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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

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Developing a chatbot using SLMs will challenge you to apply your theoretical knowledge and practical skills in a meaningful way.

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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

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Small Language Models.

Speech and Language Processing. An Introduction to...

Save

This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.

Speech and Language Processing An Introduction to...

Paperback

Fluent Python

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Serves as the ultimate masterclass in idiomatic Python, guiding developers through data models, generators, metaprogramming, and concurrent concurrency patterns.

Fluent Python: Clear, Concise, and Effective...

Paperback

Fluent Python: Clear, Concise, and Effective...

Paperback

$$$

(日本語) Fluent Python ―Pythonicな思考とコーディング手法