Transformer & Gen AI
Transformers are advanced deep learning models that power Generative AI (Gen AI) by enabling tasks like text generation, image creation, and code completion. Gen AI leverages these models to generate human-like content, making it a key technology in AI-driven automation, creativity, and problem-solving.
Satyajit Pattnaik
Instructor
Included with Data Science & AI - From Python to GenAI
What you will learn?
- Understanding Transformers
- Concept of Generative AI and its applications
- BERT (Bidirectional Encoder Representations from Transformers)
- Future of Transformers & Generative AI
- Real-world use cases and industry adoption trends
This Course Includes
- Recorded Lessons: 42
- Recorded Hours: 9
- Duration: 9 days (Avg)
- TD Assessment Available
- Access on Mobile
Course Description
Generative AI (GenAI) analyzes vast amounts of data, looking for patterns and relationships, then uses these insights to create fresh, new content that mimics the original dataset. It does this by leveraging machine learning models, especially unsupervised and semi-supervised algorithms.
So, what actually does the heavy lifting behind this capability? Neural networks. These networks, inspired by the human brain, ingest vast amounts of data through layers of interconnected nodes (neurons), which then process and decipher patterns in it. These insights can then be used to make predictions or decisions. With neural networks, we can create diverse content, from graphics and multimedia to text and even music.
These neural networks adapt and improve over time with experience, forming the backbone of modern artificial intelligence. Looping back to Transformers, it’s like the Matrix of Leadership, which allows Optimus Prime to leverage the knowledge of his ancestors to inform his decisions.
There are three popular techniques for implementing Generative AI:
1) Generative Adversarial Networks (GANs)
2) Variational Autoencoders (VAEs)
3) Transformers
Examining how the first two work helps provide insight into how transformers operate, so let’s delve a little deeper into GANs and VAEs.
What are Generative Adversarial Networks? (GANs)
Generative Adversarial Networks (GANs) are a type of generative model that has two main components: a generator and a discriminator. The generator tries to produce data while the discriminator evaluates it.
Let’s use the analogy of the Autobots and Deceptions in the Transformers franchise. Think of the Autobots as "Generators," trying to mimic and transform into any vehicle or animal on Earth. On the opposite side, the Deceptions play the role of "Discriminators," trying to identify which vehicles and animals are truly Autobots. As they engage, the Autobots fine-tune their outputs, motivated by the discerning eyes of the Decepticons. Their continuous struggle improves the generator's ability to create data so convincing that the discriminator can't tell the real from the fake.
GANs have many limitations and challenges. For instance, they can be difficult to train—because of problems such as model collapse, where the generator produces limited varieties of samples or even the same sample, regardless of the input. For example, it might repeatedly generate the same type of image rather than a diversity of outputs.
Course Content
42 Lessons | 8hr 39min
Frequently Asked Questions
₹999 ₹1999
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