https://github.com/karthik-k11/deep-learning
A repository documenting my journey as I learn and implement deep learning models using Tensorflow and Keras
https://github.com/karthik-k11/deep-learning
deep-learning deep-neural-networks keras python tensorflow
Last synced: 2 months ago
JSON representation
A repository documenting my journey as I learn and implement deep learning models using Tensorflow and Keras
- Host: GitHub
- URL: https://github.com/karthik-k11/deep-learning
- Owner: karthik-k11
- Created: 2025-09-18T16:38:16.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-18T17:20:23.000Z (10 months ago)
- Last Synced: 2025-09-18T20:02:04.200Z (10 months ago)
- Topics: deep-learning, deep-neural-networks, keras, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 52.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# My Deep Learning Journey
Welcome to my repository of deep learning projects! This space serves as a portfolio of my work and a log of my progress in the field of Artificial Intelligence. Each project here represents a new concept learned and a new problem solved.
## 🚀 Projects
Here is a summary of the projects completed so far. As I continue to learn, this list will grow.
| # | Project Name | Description | Key Concepts Learned |
|---|---|---|---|
| 1 | **Handwritten Digit Recognition (MNIST)** | A foundational "Hello, World!" project for image classification. This notebook builds a simple feed-forward neural network to recognize handwritten digits from 0 to 9 with ~98% accuracy. | `TensorFlow/Keras`, `Sequential Model`, `Dense Layers`, `Data Preprocessing & Normalization`, `Model Training & Evaluation` |
| 2 | **IMDb Movie Review Sentiment Analysis** | An introductory NLP project performing binary classification on movie reviews. The model uses word embeddings and dense layers with **Dropout** regularization to fix overfitting, achieving ~87.8% accuracy. | `NLP & Word Embeddings`, `Sequence Padding`, `Binary Classification`, `Dropout Regularization`, `GlobalAveragePooling1D`, `Overfitting Analysis` |
## 🛠️ Technologies & Tools
* **Primary Framework:** TensorFlow & Keras
* **Core Libraries:** NumPy, Matplotlib
* **Development Environment:** Kaggle Notebooks
* **Version Control:** Git & GitHub
## Usage
Each project is contained within its own Jupyter Notebook (`.ipynb` file). To explore a project, you can clone this repository and run the notebook in an environment like Kaggle, Google Colab, or a local setup with the required libraries installed.