Applied Text Generation Using Gpt And Kerasnlp In Python

Free Download Applied Text Generation Using Gpt And Kerasnlp In Python
Published 8/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 345.04 MB | Duration: 1h 6m
Dive into Hands-on TensorFlow and Python Programming with KerasNLP in Google Colab for an Immersive, Practical Learning

What you’ll learn
Understand the concept of text generation using deep learning models.
Learn how to build a text generation model using the Transformer architecture.
Gain familiarity with using the Keras library for implementing text generation models.
Learn how to preprocess text data for training a text generation model.
Gain experience in training a text generation model using a given dataset.
Learn different text generation techniques such as greedy search, beam search, random search, top-k sampling, and top-p sampling.
Understand how to use callbacks in Keras to generate text during model training.
Learn how to save and load trained model weights for future use.
Gain hands-on experience in fine-tuning and adapting a pre-trained text generation model to generate creative text.
Requirements
Basic knowledge of Python programming language.
Access to a stable internet connection for downloading datasets and necessary packages.
Description
Step into the exhilarating realm of text generation with deep learning! Get ready to embark on a captivating journey where you’ll unravel the secrets of training models capable of crafting human-like text from simple prompts. Whether you dream of building intelligent chatbots, creating compelling content, or exploring the world of creative writing, this course is your gateway to mastering these cutting-edge domains.No prior knowledge of deep learning or natural language processing is needed – we’ll start from the basics and lead you through the fascinating process of training text generation models using powerful deep learning techniques.Here’s what makes this course shine:1. Introduction to Text Generation: Immerse yourself in the world of text generation and its real-life applications. You’ll discover the immense power and potential that text generation models bring to various industries.2. Deep Learning Fundamentals: Build a rock-solid foundation in deep learning as we cover essential topics like neural networks, activation functions, loss functions, and optimization algorithms. Don’t worry; we’ll leverage user-friendly libraries like Keras to make the implementation process a breeze.3. NLP and Transformers: Unleash the transformative capabilities of Natural Language Processing (NLP) and delve into the revolutionary world of Transformers. Learn how these groundbreaking models have reshaped NLP tasks, including the enchanting art of text generation.4. Preprocessing and Tokenization: Master the crucial steps of text generation – preprocessing and tokenization. We’ll guide you through preparing your text data for training, covering essential techniques like cleaning, tokenization, and vocabulary building.5. Model Architecture: Get hands-on experience building a mini-Generative Pre-Trained (GPT) model using KerasNLP. Dive into the model’s architecture, including embedding layers, Transformer decoders, and the final dense layer.6. Training and Evaluation: Unravel the training process and learn how to evaluate your text generation model’s performance. We’ll delve into essential concepts like loss functions, metrics, and hyperparameter tuning to optimize your model’s brilliance.7. Text Generation Techniques: Explore an array of captivating text generation techniques – from the greedy search to beam search, random search to top-k search, and top-p search. Learn the art of choosing the perfect technique for each unique scenario.8. Real-Life Applications: Discover the immense real-world impact of text generation in applications like chatbots, content generation, language translation, and beyond. Gain insights into practical use cases that redefine industries.9. Job Opportunities: As you complete this thrilling journey, brace yourself for exciting job opportunities in the realm of Natural Language Processing and AI. Organizations are increasingly seeking professionals with text generation expertise, positioning you for roles as an NLP Engineer, AI Researcher, Data Scientist, or Software Developer.By the course’s end, you’ll possess a comprehensive understanding of text generation with deep learning. You’ll wield the power to create and train your own text generation models, applying various techniques for astonishing results in real-world applications. Join us on this enthralling learning journey and unlock doors to extraordinary opportunities in the rapidly evolving world of text generation!
Overview
Section 1: Fundamentals
Lecture 1 Introduction
Lecture 2 About this Project
Lecture 3 Why Should we Learn?
Lecture 4 Applications
Lecture 5 Why Python, Keras and Google Colab?
Section 2: Build and Train Model
Lecture 6 Setup the Working Directory
Lecture 7 What is inside the train.txt and valid.txt?
Lecture 8 What is inside the code.ipynb?
Lecture 9 Open the Project
Lecture 10 Activate GPU
Lecture 11 Checks the availability of the GPU
Lecture 12 Mounts Google Drive
Lecture 13 Install Keras NLP
Lecture 14 Importing necessary libraries
Lecture 15 Define the paths to the training and validation text files
Lecture 16 Loads training and validation datasets and applies filtering
Lecture 17 Computes the vocabulary
Lecture 18 Initializes the WordPieceTokenizer
Lecture 19 Initializes the StartEndPacker layer
Lecture 20 Defines a preprocess function
Lecture 21 Preprocesses the training dataset
Lecture 22 Preprocesses the validation dataset
Lecture 23 Creates an embedding layer
Lecture 24 Building the TransformerDecoder layers
Lecture 25 Creating and compiling the model
Lecture 26 Summary of the model’s architecture
Lecture 27 Training the model
Lecture 28 Saving the trained model weights
Lecture 29 Generates a prompt token
Lecture 30 Generate the logits for the next token
Lecture 31 Creates a GreedySampler instance for text generation
Lecture 32 Creates a BeamSampler instance for text generation
Lecture 33 Creates a RandomSampler instance for text generation
Lecture 34 Creates a TopKSampler instance for text generation
Lecture 35 Creates a TopPSampler instance for text generation
Lecture 36 Define a custom callback
Data scientists or machine learning practitioners interested in text generation techniques.,Natural language processing (NLP) enthusiasts who want to explore advanced text generation models.,Deep learning practitioners looking to expand their knowledge in sequence generation tasks.,Students or researchers in the field of artificial intelligence (AI) and NLP.,Developers interested in building creative applications involving text generation.,Professionals working on chatbot development or language modeling projects.,Anyone with a curiosity and passion for exploring the capabilities of text generation models.
Homepage

https://www.udemy.com/course/text-generation/

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