https://github.com/sabtain-dev/tensorflow
Learn and implement CNNs using TensorFlow and Keras, covering image preprocessing, classification, callbacks, visualizations from foundational concepts to real-world applications.
https://github.com/sabtain-dev/tensorflow
tensorflow tensorflow-examples tensorflow-tutorials tensorflow2
Last synced: about 2 months ago
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Learn and implement CNNs using TensorFlow and Keras, covering image preprocessing, classification, callbacks, visualizations from foundational concepts to real-world applications.
- Host: GitHub
- URL: https://github.com/sabtain-dev/tensorflow
- Owner: Sabtain-Dev
- License: mit
- Created: 2025-07-02T09:12:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-28T17:11:01.000Z (11 months ago)
- Last Synced: 2025-07-28T19:12:00.825Z (11 months ago)
- Topics: tensorflow, tensorflow-examples, tensorflow-tutorials, tensorflow2
- Language: Jupyter Notebook
- Homepage:
- Size: 12.2 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepLearning.AI TensorFlow Developer Specialization
This repository contains my work and notes from courses in the [DeepLearning.AI TensorFlow Developer Specialization](https://www.coursera.org/professional-certificates/tensorflow-in-practice) on Coursera.
## Overview
In this courses, I learned to build and train **Convolutional Neural Networks (CNNs)** using TensorFlow and Keras. The course involved working with real-world image datasets and implementing neural networks that can recognize patterns in complex visual data.
### Key Concepts Covered
- TensorFlow & Keras fundamentals for computer vision tasks
- Implementing CNNs using `Conv2D`, `MaxPooling2D`, `Dense`, `Flatten`, and `Dropout`
- Image preprocessing and normalization using `Rescaling` layers
- Custom training callbacks for early stopping
- Dataset creation using `image_dataset_from_directory` and `tf.data`
- Feature map visualization to interpret model behavior
- Real-time image classification using Jupyter widgets
---
## 🛠️ Tools & Libraries
- TensorFlow / Keras
- Matplotlib
- NumPy
- Ipywidgets (for interactive image upload)
- tf.data.Dataset API
---
## Repository Structure
Tensorflow/
- ├── Week1/
- ├── Week2/
- ├── Week3/
- ├── Week4/
- ├── Week5/
- ├── Week6/
- ├── Week7/
- ├── Week8/
- ├── Week9/
- ├── Week10/
- └── README.md
## How to Use
1. Clone this repository:
```bash
git clone https://github.com/yourusername/Tensorflow.git
cd tensorflow
2. Install Requiremnents:
```bash
pip install -r requirements.txt
## Links
- [TensorFlow Developer Specialization on Coursera](https://www.coursera.org/professional-certificates/tensorflow-in-practice)
- https://www.tensorflow.org/api_docs
- https://keras.io/api/
## Author
M. Sabtain Khan
- Connect with me on
- Linkedin: https://www.linkedin.com/in/msabtainkhan/
- GitHub : [@Sabtain-Dev](https://github.com/Sabtain-Dev)