Description:

This talk invites you to embark on an exploration of one of the most intriguing advancements in AI.

Ever wondered how a machine can understand human language? Or how it translates text from one language to another with remarkable accuracy? This tech talk promises to answer these questions and more as we delve into the inner workings of the Transformer architecture layer by layer.

Have you ever stopped to ponder how a machine can attend to different parts of a sentence simultaneously, much like how our brains process information? This talk will unravel the mystery behind the self-attention mechanism, a fundamental component of the Transformer model. We'll explore how this mechanism allows the Transformer to weigh the importance of each word in a sentence, enabling it to capture complex relationships and dependencies.

Furthermore, the talk will introduce attendees to the dialogue completion task, showcasing how the Transformer learns to predict the next word in a conversation based on context, thereby enhancing its language understanding capabilities.

But that's just the beginning. What about positional encoding? How does the Transformer know the order of words in a sentence without explicitly being told? We'll dive into the ingenious technique of positional encoding, which equips the Transformer with the ability to encode the position of words within a sequence, essential for maintaining context and understanding the structure of language.

"And what happens after that?" you might inquire. Well, through the magic of feedforward networks, the Transformer refines the representations of words, paving the way for accurate predictions and meaningful outputs.

Throughout this captivating journey, we'll examine real-world examples and practical applications, shedding light on how the Transformer architecture has transformed the landscape of natural language processing.

By the end of the talk, you'll emerge with a newfound appreciation for the Transformer and its role in shaping the future of AI.

Prerequisites:

As this topic delves into complexity, it is advisable for the audience to possess a prior understanding of :

• Basic understanding of Deep Learning
• Fair understanding of Neural Network architectures - Feed Forward & Convolutional Neural networks (FFNN& CNN)
• Recurrent Neural Network (RNN) & its variants LSTM / GRU
• Attention All you Need
• RNN based Encoder-Decoder model

Speaker Info:

Continuous Learner | AI & ML Enthusiast | Talks about Deep Learning | AWS | Cloud Automation | Docker | Python | REST API | Blogger| Community Speaker |

A software professional and community speaker with 13+ years of rich experience in Automation & Tool development of software applications using Python, Django & Flask. Currently, working as a Sr. Software Engineer at F5, in Cyber Security Domain and working on F5 Distributed Cloud Bot Defense solution.

An AI/ML enthusiast with an interest in Deep Learning and Computer Vision and a promoter of DL framework PyTorch.

Lots of desire to learn new exciting things!

Speaker Links:

My Deep Learning youtube channel where you can find my previous talks : https://www.youtube.com/playlist?list=PLqYDykjFMcnGPLPC7cBp9zqgXaRqoUJrC