Build a simple image classifier using fashion dataset.
Introduction
Introducing a straightforward yet powerful fashion classifier! Built upon the “Fashion MNIST” dataset, comprising compact black and white images of various apparel items, this training script employs a neural network for classification, initially achieving around 15% accuracy. However, our goal is ambitious: we’re striving to enhance its performance to a remarkable 80% accuracy. How? By implementing two key strategies: normalizing the input data and incorporating a softmax activation function. Join us on this journey to witness the transformation from modest beginnings to impressive results!
😇 Motivation
Embarking on this project isn’t just about improving a fashion classifier; it’s about showcasing the transformative power of machine learning. By aiming to boost our classifier’s accuracy from 15% to an impressive 80%, we’re not only demonstrating the effectiveness of techniques like data normalization and softmax activation but also embracing the iterative process of problem-solving in AI. This project is a celebration of creativity, innovation, and our unwavering commitment to pushing the boundaries of what’s possible.
⭐ Features
- Fashion MNIST Dataset: Utilizing a dataset consisting of small black-and-white images of apparel items for training the classifier.
- Neural Network Classifier: Implementing a neural network architecture to classify the fashion items.
- Initial Accuracy: Starting with a baseline accuracy of approximately 15% with the existing classifier.
- Normalization: Employing data normalization techniques to preprocess the input data and enhance model performance.
- Softmax Activation Function: Incorporating the softmax activation function to improve the model’s ability to make probabilistic predictions across multiple classes.
- Accuracy Improvement Goal: Setting a target of achieving an 80% accuracy rate, signifying a substantial enhancement from the initial performance level.
- Iterative Optimization: Embracing an iterative approach to fine-tune the model parameters, experiment with different configurations, and continuously improve performance.
- Machine Learning Principles: Demonstrating the application of fundamental machine learning concepts such as preprocessing, activation functions, and optimization strategies in real-world scenarios.
- Creative Problem-Solving: Encouraging creative thinking and innovation to overcome challenges and achieve ambitious goals in the field of AI and machine learning.
- Motivation for Project: Highlighting the project’s broader significance in showcasing the capabilities of AI, driving meaningful change, and inspiring others in the AI community to push the boundaries of innovation.
🔑 Results
Model gave 80% accuracy for Fashion cloth Prediction using Neural Network
Github
Here you can find the complete code of project.
Github Link
Please Follow and 👏 Clap for the story courses teach to see latest updates on this story
🚀 Elevate Your Data Skills with Coursesteach! 🚀
Ready to dive into Python, Machine Learning, Data Science, Statistics, Linear Algebra, Computer Vision, and Research? Coursesteach has you covered!
🔍 Python, 🤖 ML, 📊 Stats, ➕ Linear Algebra, 👁️🗨️ Computer Vision, 🔬 Research — all in one place!
Don’t Miss Out on This Exclusive Opportunity to Enhance Your Skill Set! Enroll Today 🌟 at
Machine Learning projects course
🔍 Explore Free world top University computer Vision ,NLP, Machine Learning , Deep Learning , Time Series and Python Projects, access insightful slides and source code, and tap into a wealth of free online websites, github repository related Machine Learning Projects. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait — enroll now and unleash your Machine Learning projects potential!”
Stay tuned for our upcoming articles because we reach end to end ,where we will explore specific topics related to Deep Learning in more detail!
Remember, learning is a continuous process. So keep learning and keep creating and Sharing with others!💻✌️
Ready to dive into data science and AI but unsure how to start? I’m here to help! Offering personalized research supervision and long-term mentoring. Let’s chat on Skype: themushtaq48 or email me at mushtaqmsit@gmail.com. Let’s kickstart your journey together!
Contribution: We would love your help in making coursesteach community even better! If you want to contribute in some courses , or if you have any suggestions for improvement in any coursesteach content, feel free to contact and follow.
Together, let’s make this the best AI learning Community! 🚀
