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Mastering Translations with Generative AI in PyTorch

IntermediateGuided Project

You will learn step-by-step how to build a powerful translation model using transformers in PyTorch. From understanding the core concepts of transformer architecture to implementing the model from scratch, you'll explore the intricacies of attention mechanisms, positional encoding, and multi-head self-attention. With practical code examples and hands-on exercises, you'll gain the skills to preprocess data, train the model, and generate translations. By the end of this tutorial, you'll have the confidence to create your own translation models using transformers and unlock their potential.

4.8 (10 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 105

Skills You Will Learn

  • Generative AI, Artificial Intelligence, LLM, PyTorch, Deep Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 2 hours

Platform

  • SkillsNetwork

Last Update

  • May 15, 2024
About This Guided Project

A Look at the Project Ahead

In this engaging tutorial, you will dive into the fascinating world of translation models powered by transformers in PyTorch. Discover why this topic is crucial in the field of natural language processing and how it has revolutionized machine translation. By completing this project, you will gain valuable insights and practical skills to create your own translation models using state-of-the-art transformer architectures.

Learning Objectives:
  • Understand Transformer Architecture: Delve into the fundamental concepts behind transformers, including self-attention mechanisms, multi-head attention, and positional encoding. Gain a deep understanding of how transformers enable effective language modelling and translation.
  • Build a Translation Model from Scratch: Learn how to implement a translation model using PyTorch. Follow step-by-step instructions to preprocess textual data, design the transformer architecture, train the model using parallel computing, and fine-tune it for optimal translation performance.
  • Translate a PDF in German and Generate a PDF in English

What You'll Need

To embark on this guided project, you will need a solid foundation in Python programming and familiarity with PyTorch. Prior exposure to machine learning concepts, such as neural networks and sequence-to-sequence models, will be beneficial. The IBM Skills Network Labs environment provides pre-installed tools and libraries to ensure a seamless learning experience. 

Join us on this transformative journey as you unlock the power of transformers and embark on creating your very own translation models. Expand your skill set, gain practical experience, and become proficient in building advanced language translation systems using the cutting-edge techniques of transformer models in PyTorch. Let's get started!

Instructors

Fateme Akbari

Data Scientist @IBM

I'm a data-loving Ph.D. Candidate with a passion for making a real impact. I thrive on applying science to improve the world we all share, not just for humans, but for all creatures. I've racked up years of experience working with data and I love diving into tough problems that make my brain sweat. But when I'm not crunching numbers, you'll catch me out in nature, either trekking or biking, and having a blast at ball games.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Contributors

Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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