Build a Chatbot | Transformers Architecture| PyTorch

Build a Chatbot | Transformers Architecture| PyTorch | Tutorial

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Күн бұрын

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:⭐️
⭐️Complete AI Playlist:⭐️ • The Complete Neural Ne...
⭐️Notebook and Code ⭐️: github.com/gkv856/util_repo.git
#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

Пікірлер: 4

@tomCatzy 13 күн бұрын

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.

@logiclab10 13 күн бұрын

thank for your kind words. Much appreciated.

@tomCatzy 11 күн бұрын

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 ? 🙂

@logiclab10 11 күн бұрын

If you remember from the theory section, for the decoder we dont want to send the entire sentence. E.g. sentence-> I will share this video dec_input will be = I will share this video dec_output will be = I will share this video because when we send the padding. decoder will attend one word at a time. for the work we want it to predict I for word I we want it to predict will for word will we want to predict share and so on. I hope this helps