Build a Chatbot | Transformers Architecture| PyTorch | Tutorial

Embark on an enlightening journey through the intricate world of Natural Language Processing (NLP) with our comprehensive guide to Transformer architecture! Unravel the complexities of input embeddings, positional embeddings, and multi-head attention mechanisms as we delve deep into the theory behind these pivotal components. From understanding the foundational principles to practical implementation, this tutorial provides invaluable insights for both seasoned professionals and aspiring enthusiasts.

But we don't stop at theory alone. We roll up our sleeves and dive headfirst into the practical realm of model development. Learn how to craft a sophisticated chatbot model from scratch using PyTorch, the industry-leading deep learning framework. We leave no stone unturned as we guide you through the entire process, from meticulously cleaning and preprocessing the Cornell Dialog Corpus to crafting custom datasets and implementing efficient data loaders.

But that's just the beginning. We demystify the complexities of building a robust embedding class and transformer class, providing you with the foundational knowledge needed to construct powerful NLP models. And when it comes to training, we equip you with the tools and techniques necessary to navigate the intricacies of model optimization. Discover the inner workings of the Adam Warmup Optimizer and gain hands-on experience in fine-tuning your models for peak performance.

Whether you're a seasoned data scientist looking to expand your skill set or a curious novice eager to explore the fascinating world of NLP, this tutorial is your definitive guide to mastering Transformer architecture. Empower yourself with the knowledge and expertise needed to revolutionize natural language processing in your projects and research endeavors. Join us on this transformative journey and unlock the true potential of NLP with PyTorch.

⭐️Prerequisite:⭐️

#AIModel #MachineLearning #MNISTDataset #NeuralNetworks #PyTorch #DataScience #AI #DeepLearning #Coding #Tutorial #GKV

⭐️ Contents ⭐️
⌨️ (0:00:00) 01. Intro
⌨️ (0:01:02) 02. The Problem Statement
⌨️ (0:03:25) 03. Transformer Architecture
⌨️ (0:04:50) 04. Input Embeddings
⌨️ (0:09:33) 05. Positional Embeddings
⌨️ (0:14:38) 06. Multi-Head Attention
⌨️ (0:18:56) 07. Concatenation and Residual Learning
⌨️ (0:20:40) 08. Layer Normalization
⌨️ (0:21:18) 09. Feed-Forward Learning
⌨️ (0:22:33) 10. Masked Multi-Head Attention
⌨️ (0:25:50) 11. KL Divergence Loss Function
⌨️ (0:30:02) 12. Adam WarmUp Optimizer
⌨️ (0:31:21) 13. ChatBot PyTorch Coding
⌨️ (0:43:21) 14. Word Map Data Preparation
⌨️ (0:50:11) 15. Custom Dataset and Dataloader

⌨️ (0:54:09) 16. Mask for the Transformer Decoder
⌨️ (0:55:35) 17. Designing the Chatbot Model
⌨️ (1:07:46) 18. Adam WarmUp Optimizer Class
⌨️ (1:09:06) 19. Evaluate function to Generate text
⌨️ (1:10:41) 20. Training loop for the ChatBot


#NLP #TransformerArchitecture #PyTorchTutorial #DeepLearning #ChatbotModel #AI #MachineLearning #DataScience #NaturalLanguageProcessing #ArtificialIntelligence #NeuralNetworks #PythonProgramming #ModelTraining #DataPreprocessing #CornellDialogCorpus

[tomCatzy]

Hello Mr. Logical. Your videos are realy good and informative! Very logical in their structure step by step so that complicated stuff gets clear and understandable. Now you have been my favorite channel learning complicated NLP ML topics. Please make a lot more. Thanks for your time and effort here.

[tomCatzy]

Prepare Target Data: LongTensor at TimeStamp 50:44: dec_inp = dec[ :-1] dec_out =dec[1: ]... these (-1) and (1) are they 'marking' the paddings in the text lenght i.e. the : Start and End fields in the text field strings - so the Tensor knows where to Start and End with the Target data ? Can you explain it a little deeper ? 🙂