Welcome to the repository for the Deep Learning (410255) laboratory course, part of the Fourth Year Computer Engineering curriculum (2019 Course) at Savitribai Phule Pune University. This repository hosts practical implementations and resources to explore the fundamentals of neural networks, various deep learning models, and their applications.
🏛️ Course Information:
| Feature | Description |
|---|---|
| University | Savitribai Phule Pune University |
| Course Name | Deep Learning |
| Course Code | 410255 |
| Companion Course | Laboratory Practice V (410254) |
| Credit | 01 |
| Practical Sessions | 02 Hours/Week |
| Examination Scheme | Term Work: 50 Marks Practical Exam: 50 Marks |
🎯 Learning Objectives:
- To understand the basics of neural networks.
- Comparing different deep learning models.
- To understand the Recurrent and Recursive nets in Deep Learning.
- To understand the basics of deep reinforcement Learning models.
- To analyze Types of Networks.
- To Describe Reinforcement Learning.
💡 Course Outcomes:
Upon successful completion of this laboratory course, students will be able to:
- CO1: Understand the basics of Deep Learning and apply the tools to implement deep learning applications.
- CO2: Evaluate the performance of deep learning models (e.g., with respect to the bias-variance trade-off, overfitting and underfitting, estimation of test error).
- CO3: To apply the technique of Convolution (CNN) and Recurrent Neural Network (RNN) for implementing Deep Learning models.
- CO4: To implement and apply deep generative models.
- CO5: Construct and apply on-policy reinforcement learning algorithms.
- CO6: To Understand Reinforcement Learning Process.
💻 Practical Implementations:
| Practical No. | Description |
|---|---|
| 1 | Linear Regression with DNN (Boston Housing): Implement Boston housing price prediction using Linear Regression with a Deep Neural Network. Utilizes the Boston House Price Prediction dataset. |
| 2 | Binary Text Classification with DNN (IMDB Reviews): Classify movie reviews as "positive" or "negative" based on text content using Deep Neural Networks. Employs the IMDB dataset. |
| 3 | Image Classification (MNIST Fashion): Develop a classifier for the MNIST Fashion Dataset to categorize various fashion clothing items. |
🚀 Getting Started:
Navigate to the specific practical implementation directory for detailed instructions, code examples, datasets (or links to them), and further details.
🙌 Contributions:
Contributions, suggestions, and feedback are always welcome! If you have improvements, bug fixes, or alternative implementations to share, please feel free to open a pull request. Please refer to the CONTRIBUTING.md file for detailed guidelines.
📄 License:
This repository is distributed under the MIT License. You are free to use, modify, and distribute the code for educational and personal projects. See the LICENSE file for more details. (Ensure you have a LICENSE file in your repo, e.g., an MIT license file).
Let's delve into the exciting world of neural networks and build intelligent applications!