Developing A Fundamental Chatbot Utilizing Transformer

Unleashing Transformer Power: Mastering Fundamental Chatbot Development

What you'll learn

Gain a comprehensive understanding of Transformer projects and their applications in natural language processing

Acquire the skills to preprocess text data specifically tailored for Transformer-based chatbot machine learning models

Learn to tokenize and filter sentences effectively using Python to prepare data for model training

Develop proficiency in building multi-head attention layers for enhancing the performance of chatbot machine learning models

Master the techniques required to construct token masks to optimize neural network functionality in chatbot development

Understand and implement positional encoding mechanisms crucial for enabling Transformers to interpret sequential language data

Learn to build input encoders, laying the foundation for integrating components into a cohesive neural network architecture for chatbot ML

Requirements

No specific requirements or prerequisites

Description

Welcome to "Developing a Fundamental Chatbot Utilizing Transformer" – a transformative journey into the world of advanced conversational agents powered by state-of-the-art Transformer models. Led by expert instructors, this immersive course is meticulously crafted to equip you with the essential skills needed to create sophisticated chatbots that can engage users in natural and meaningful conversations.Our curriculum begins with a comprehensive overview of Transformer projects, offering you a deep dive into their architecture, capabilities, and real-world applications in natural language processing. Through insightful discussions and practical examples, you'll gain a profound understanding of how Transformers are revolutionizing conversational AI.Next, we'll explore the preprocessing of text data tailored specifically for Transformer-based chatbots. You'll learn indispensable techniques for cleaning and formatting text data, ensuring it is primed for efficient tokenization and model training.As we progress, you'll delve into the implementation of key components vital for building a robust Transformer-based chatbot. From tokenizing and filtering sentences with Python to constructing multi-head attention layers and developing token masks, you'll master essential skills to optimize your neural network's performance.Furthermore, you'll uncover the intricacies of building positional encoding mechanisms, essential for imbuing Transformers with an understanding of the sequential nature of language. With this foundation in place, we'll tackle the creation of input encoders, paving the way for seamless integration of your components into a cohesive neural network architecture.By the culmination of this course, you'll emerge equipped with the knowledge and skills to develop fundamental chatbots utilizing Transformer models. You'll be poised to explore more advanced applications in the dynamic field of conversational AI, empowered to shape the future of human-computer interaction.Join us on this exhilarating journey and unlock the boundless potential of Transformer technology in redefining the way we interact with machines. Your adventure begins now!

Overview

Section 1: 00 Transformer Project Overview

Lecture 1 01 Introduction to Transformer Neural Networks

Lecture 2 02 Transformer Project Overview

Section 2: 01 Preprocess text data for Transformer chatbot ML

Lecture 3 01 Connect to Google drive dataset in Colab

Lecture 4 02 Read text files in Python

Lecture 5 03 Read movie conversation text file in Python

Lecture 6 04 Clean text data for NLP

Lecture 7 05 Remove contractions from text data with Python

Lecture 8 06 Preprocess text data for Transformer chatbot ML

Section 3: 02 Tokenize and filter sentences with Python

Lecture 9 01 Build tokenizer with tfds

Lecture 10 02 Add padding to tokenized sentences with Python

Lecture 11 03 Build TensorFlow Dataset for ML

Section 4: 03 Build multi head attention layer for chatbot ML Python

Lecture 12 01 Calculate Scaled Dot Product Attention

Lecture 13 02 Set up multi head attention layer in Python NN

Lecture 14 03 Split attention layer into multiple heads

Lecture 15 04 Add scaled dot product attention and final layer

Section 5: 04 Build token masks for neural network

Lecture 16 01 Mask padding tokens with Python

Lecture 17 02 Build lookahead mask for future tokens

Section 6: 05 Build positional encoding machine learning

Lecture 18 01 Set up positional encoding layer in neural network

Lecture 19 02 Build positional encoding layer with TensorFlow Keras

Section 7: 06 Build input encoder for neural network

Lecture 20 01 Build input encoder for neural network

Lecture 21 02 Combine input and positional encoding

Lecture 22 Bonus Lecture

Absolute Beginners