🔗 GitHub Repo Link: https://github.com/Waqas56jb/GPU-bench-marking-with-image-classification
I am excited to share my recent project where I developed a neural network model to classify images from the Fashion MNIST dataset. This project demonstrates the practical application of machine learning techniques in image classification, showcasing the power and flexibility of neural networks.
- 🐍 Python: The core programming language used for implementing the neural network.
- 🔢 TensorFlow: A powerful open-source library for numerical computation and machine learning.
- 📊 Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow.
- 📉 Matplotlib: A library for creating static, animated, and interactive visualizations.
- 📊 Data Preprocessing: Techniques for preparing the Fashion MNIST dataset for training, including normalization and reshaping.
- 🏗️ Model Building: Constructing a multi-layer perceptron (MLP) model using Keras.
- 🏋️♂️ Training & Validation: Training the neural network model and evaluating its performance using validation data.
- 📈 TensorBoard: Utilizing TensorBoard for real-time monitoring and visualization of training metrics.
- 🧪 Model Evaluation: Assessing the model’s accuracy and loss on test data to ensure its robustness.
The trained neural network achieved impressive accuracy on the test set, demonstrating its capability to effectively classify images of clothing items. Advanced neural network techniques and careful tuning of hyperparameters contributed to the model's success.
I invite you to check out the video where I walk through the entire code, explaining each step in detail. This project underscores my proficiency in machine learning and my commitment to leveraging cutting-edge technologies for solving complex problems.
🔗 GitHub Repo Link: https://github.com/Waqas56jb/CatDogClassification
I am thrilled to share my recent project where I developed a neural network model to classify images of cats and dogs. This project showcases the practical application of machine learning techniques in image classification, emphasizing the power and flexibility of neural networks in solving real-world problems.
- 🐍 Python: The core programming language used for implementing the neural network.
- 🔢 TensorFlow: A powerful open-source library for numerical computation and machine learning.
- 📊 Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow.
- 📉 Matplotlib: A library for creating static, animated, and interactive visualizations.
- 📊 Data Preprocessing: Techniques for preparing the cat and dog image dataset for training, including normalization and reshaping.
- 🏗️ Model Building: Constructing a convolutional neural network (CNN) model using Keras.
- 🏋️♂️ Training & Validation: Training the neural network model and evaluating its performance using validation data.
- 📈 TensorBoard: Utilizing TensorBoard for real-time monitoring and visualization of training metrics.
- 🧪 Model Evaluation: Assessing the model’s accuracy and loss on test data to ensure its robustness.
The trained neural network achieved high accuracy in classifying cat and dog images, demonstrating its capability to effectively distinguish between the two. The use of convolutional layers and careful tuning of hyperparameters contributed to the model's success.
I invite you to check out the video where I walk through the entire code, explaining each step in detail. This project underscores my proficiency in machine learning and my commitment to leveraging cutting-edge technologies for solving complex problems.