New Tutorial — A Deep Dive into Transformers with TensorFlow and Keras: Part 3

Hi, This week you'll learn about A Deep Dive into Transformers with TensorFlow and Keras: Part 3. Till now, we have just been toying with the idea of building our own Transformer. Quite like a trainee chef (who has to understand and prepare the meal first), we initially learned about the main ingredient of our dish (attention), and then we went over prepping the necessary ingredients (connecting blocks).  Now it is time for us to bring it all together! Part 1 of our deep dive into this beautiful concept was all about the evolution of Attention, arguably the most important piece of this puzzle. Part 2 of our deep dive featured the connecting wires required to finish conceptualizing everything together.  As we enter the final part of this series, all that is left for us is to recap everything we have learned, put everything to use, and make a Transformers architecture from scratch.   The big picture: We will finally learn how to create a Transformer from scratch with the help of TensorFlow.  How it works: Our approach has been a step-by-step dissection of a Transformer's essence. The building blocks and foundations were identified as Attention and the important connecting blocks holding the entire architecture together. The final step is just to stitch what we have learned together to get what we have all been waiting to obtain.  Our thoughts: The idea of breaking down a Transformer into this format was because we believed in a ground-up approach. A concept like this can only be fully grasped when your concepts are rock-solid.  Yes, but: It is up to you to realize the limitless potential of how a Transformer can be used.  Stay smart: And don't limit your curiosity! Figure out possible problem statements where we can fit Transformers in, and stay tuned for what's cooking next.  Click here to read the full tutorial Do You Have an OpenCV Project in Mind? You can instantly access all of the code for A Deep Dive into Transformers with TensorFlow and Keras: Part 3, along with courses on TensorFlow, PyTorch, Keras, and OpenCV by joining PyImageSearch University.  Guaranteed Results: If you haven't accomplished your Computer Vision/Deep Learning goals, let us know within 30 days of purchase and get a full refund. Discover how to finish your project faster! Your PyImageSearch Team P.S. The recording of our most recent Live Stream on Transformers is available online! To ensure you keep getting these emails, please add both adrian@pyimagesearch.com and success@pyimagesearch.com to your address book or allowlist us. Want out of the loop? Unsubscribe. Our postal address: 317 Commercial St. NE, Ste A, PMB 530, Albuquerque, NM 87102

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