Applied Text Generation using GPT and KerasNLP in Python

Focused View

Karthik K

1:05:32

31 View

1 - Fundamentals

1 - Introduction.mp4

01:19

2 - About this Project.mp4

02:55

3 - Why Should we Learn.mp4

03:35

4 - Applications.mp4

03:55

5 - Why Python Keras and Google Colab.mp4

03:09

2 - Build and Train Model

6 - Setup the Working Directory.mp4

01:31

6 - text-generation.rar

7 - What is inside the traintxt and validtxt.mp4

02:37

8 - What is inside the codeipynb.mp4

00:46

9 - Open the Project.mp4

01:03

10 - Activate GPU.mp4

01:10

11 - Checks the availability of the GPU.mp4

01:00

12 - Mounts Google Drive.mp4

01:04

13 - Install Keras NLP.mp4

01:11

14 - Importing necessary libraries.mp4

01:41

15 - Define the paths to the training and validation text files.mp4

01:21

16 - Loads training and validation datasets and applies filtering.mp4

02:20

17 - Computes the vocabulary.mp4

02:20

18 - Initializes the WordPieceTokenizer.mp4

02:09

19 - Initializes the StartEndPacker layer.mp4

01:41

20 - Defines a preprocess function.mp4

01:51

21 - Preprocesses the training dataset.mp4

01:30

22 - Preprocesses the validation dataset.mp4

01:18

23 - Creates an embedding layer.mp4

01:45

24 - Building the TransformerDecoder layers.mp4

02:17

25 - Creating and compiling the model.mp4

01:52

26 - Summary of the models architecture.mp4

01:32

27 - Training the model.mp4

02:02

28 - Saving the trained model weights.mp4

02:22

29 - Generates a prompt token.mp4

01:09

30 - Generate the logits for the next token.mp4

02:03

31 - Creates a GreedySampler instance for text generation.mp4

02:12

32 - Creates a BeamSampler instance for text generation.mp4

00:58

33 - Creates a RandomSampler instance for text generation.mp4

00:44

34 - Creates a TopKSampler instance for text generation.mp4

00:46

35 - Creates a TopPSampler instance for text generation.mp4

00:46

36 - Define a custom callback.mp4

03:38

Description

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.

Who is this for?

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.

What You Need to Know?

Basic knowledge of Python programming language.

Access to a stable internet connection for downloading datasets and necessary packages.

More details

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 â€“ 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 â€“ 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 â€“ 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!
Who this course is for:
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.

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Engineer dedicated to utilizing the power of Machine learning and Deep learning to solve real-world problems, improve design and performance assessment. Over ten years of experience in engineering and R&D environment. Engineering professional with a focus on Multi-physics CFD-ML from IIT Madras. Experienced in implementing action-oriented solutions to complex business problem.

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