Day 23 of 30-Day Challenge: Learning Gen AI and LLM's
Rupali Raosaheb Darade
Senior project and test Manager & Quantum Ambassador at IBM, Product Manager, Product Owner, GenAI Leader, Strategic Thinker, Project and Test Management for SAP/Non-SAP Projects, Enterprise Design Thinking Co-Creator
Transfer Learning
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The Story of a Smart Robot
?Imagine a robot named Robby who lives in a world where he has to learn new things every day. One day, Robby's teacher, Mrs. Bot, asks him to learn how to play soccer. Robby has never played soccer before, but he's excited to learn.
?Mrs. Bot starts teaching Robby the basics of soccer, like how to kick the ball and how to run with it. Robby learns quickly, but then Mrs. Bot says, "Robby, I want you to learn how to play basketball now."
?Robby is confused, "But Mrs. Bot, I just learned how to play soccer! Do I have to start all over again?" Mrs. Bot smiles and says, "No, Robby. You can use what you learned from playing soccer to help you learn basketball. You already know how to run and kick a ball, so you can use those skills to learn how to dribble and shoot a basketball."
?Robby's eyes light up, "Really? That's amazing!" And with that, Robby starts learning basketball using the skills he already learned from playing soccer.
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What is Transfer Learning?
?This is similar to how Transfer Learning works. Transfer Learning is a way for machines, like Robby, to use what they've learned from one task to help them learn another task. Just like how Robby used his soccer skills to learn basketball, machines can use what they've learned from one task to learn another task.
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How Does Transfer Learning Work?
?Imagine a machine learning model, like a big brain, that's been trained on a lot of data to learn how to recognize pictures of cats. This brain has learned to look for whiskers, ears, and fur to recognize a cat. Now, if we want to train this brain to recognize pictures of dogs, we can use what it's already learned from recognizing cats. The brain can use the features it's learned to recognize, like fur and ears, to help it learn to recognize dogs.
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Applications of Transfer Learning
?Transfer Learning is used in many areas, like:
?Generative AI: Machines can use what they've learned from one task to generate new things, like pictures or music.
Large Language Models (LLMs): Machines can use what they've learned from one language to understand and generate text in another language.
Computer Vision: Machines can use what they've learned from one task, like recognizing pictures of cats, to recognize pictures of other animals.
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Robby, the smart robot, learned that he can use what he's learned from one task to help him learn another task. This is the power of Transfer Learning. Machines can use what they've learned from one task to learn another task, making them smarter and more efficient.
As Robby says, "Transfer Learning is like having a superpower! I can learn new things faster and easier using what I already know!"
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?What topic would you like to explore next?
?Let ME know in the comments if there's a specific topic you'd like to explore next. I'll do my best to cover it in our upcoming posts.
Stay tuned for Day 24!
?I'll be back tomorrow with another exciting topic. Stay tuned and keep learning!
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Founder @Agentgrow | 3x P-club & Head of Sales
5 个月It's exciting to see the emphasis on community growth within the #TransferLearning space. The synergy between #GenAI and transfer learning is particularly potent, enabling rapid adaptation of pre-trained models for specialized NLP tasks. But how do you envision fine-tuning these models for explainability in a way that aligns with ethical AI principles, considering the inherent complexities of transformer architectures?