https://github.com/trandangtrungduc/basicdeeplearningtask

Code, Resources - Personal Project - CBD Robotics Company - August 1, 2021.
https://github.com/trandangtrungduc/basicdeeplearningtask

classification computer-vision deep-learning natural-language-processing

Last synced: 12 months ago
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Code, Resources - Personal Project - CBD Robotics Company - August 1, 2021.

• Host: GitHub
• URL: https://github.com/trandangtrungduc/basicdeeplearningtask
• Owner: trandangtrungduc
• License: mit
• Created: 2021-10-04T05:03:20.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2024-08-17T05:44:21.000Z (almost 2 years ago)
• Last Synced: 2025-07-08T04:56:09.296Z (12 months ago)
• Topics: classification, computer-vision, deep-learning, natural-language-processing
• Language: Jupyter Notebook
• Homepage:
• Size: 3.59 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Basic Deep Learning Task

### Overview

- These are useful projects for beginners and intermediates to approaching Deep Learning. Each ipynb file is a different topic (lesson).

- Dependency: Python and some other libraries are listed in each document (ipynb files).

### Implementation

1. Natural language processing project: Exploratory data analysis, pre-process, classification models, unsupervised technique, including GridSearchCV, topic modeling (Author_Classification.ipynb).

    - Pre-process:

        - Bag of word.

        - Term Frequence-Inverse Document Frequency.

        - Word to vector.

    - Classification:

        - Naive Bayes.

        - Logistic Regression.

        - Decision Tree

        - Random Forest.

        - K - Nearest Neighbors.

        - Supoprt Vector Machine

        - Gradient Boosting.

        - Recurrent Neural Networks.

    - Unsupervised technique:

        - K - Means.

        - Agglomerative.

        - Gaussian Mixture.

    - Topic modeling:

        - Latent Dirichlet Allocation.

        - Latent Semantic Analysis.

        - Non-Negative Factorization

3. Image processing project: Exploratory data analysis and fruit classification with Convolution and LSTM (Fruit_Classification.ipynb).

4. Natural language processing project: Exploratory data analysis, pre-process, apply sequence to sequence and BERT models to data(Watson_project.ipynb).

5. Natural language processing project: Rule-based chat bot with TD-IDF and Bag of words(Chatbot.ipynb).

### Maintainers

* Tran Dang Trung Duc