https://github.com/pratikshk16/nlp_in_tensorflow

I completed the TensorFlow NLP course on Coursera, covering sentiment analysis, word embeddings, and sequence models. This repo contains Jupyter notebooks for each week's assignments, along with notes and data used throughout the course. Dive into my work on text processing and text generation!
https://github.com/pratikshk16/nlp_in_tensorflow

cnn keras lstm natural-language-generation natural-language-processing neural-network tensorflow

Last synced: 5 days ago
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I completed the TensorFlow NLP course on Coursera, covering sentiment analysis, word embeddings, and sequence models. This repo contains Jupyter notebooks for each week's assignments, along with notes and data used throughout the course. Dive into my work on text processing and text generation!

• Host: GitHub
• URL: https://github.com/pratikshk16/nlp_in_tensorflow
• Owner: Pratikshk16
• License: apache-2.0
• Created: 2024-07-30T13:34:30.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-07-30T15:06:42.000Z (3 months ago)
• Last Synced: 2024-10-10T08:23:11.096Z (29 days ago)
• Topics: cnn, keras, lstm, natural-language-generation, natural-language-processing, neural-network, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 5.54 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Natural Language Processing with TensorFlow

Welcome to my repository for the Natural Language Processing (NLP) course from Coursera's deeplearning.ai TensorFlow Specialization. This repository includes:

- **Jupyter Notebooks**: Weekly assignments demonstrating various NLP techniques and models.

- **Course Notes**: Images and summaries of key concepts from each week.

- **Data**: Datasets used in the course for sentiment analysis, word embeddings, and sequence modeling.

### Course Overview

In this course, I learned to:

- Tokenize and vectorize text for neural network input.

- Use embeddings to map words into high-dimensional vectors.

- Implement sequence models like RNNs, GRUs, and LSTMs for text prediction.

- Create a poetry generator trained on traditional Irish lyrics.

### Folder Structure

- `Week_1`: Sentiment analysis and text tokenization.

- `Week_2`: Word embeddings and sentiment classification.

- `Week_3`: Sequence models and context understanding.

- `Week_4`: Predicting the next word.

Feel free to explore the notebooks and data. Contributions and feedback are welcome!

### How to Use

1. Clone the repository: `git clone https://github.com/yourusername/repo-name.git`

2. Download the necessary data.

3. Open the Jupyter notebooks to explore and run the code.

### License

This project is licensed under the MIT License.

Enjoy exploring NLP with TensorFlow!