Build and Train an Image Classifier with TensorFlow and Keras | Data Augmentation and Classification
In this step-by-step tutorial, we'll guide you through the process of building, training, and evaluating a Convolutional Neural Network (CNN) model for image classification. We'll use data augmentation techniques to improve our model's performance and create a robust image classifier. By the end of this video, you'll have a solid understanding of how to preprocess images, augment data, and build a powerful CNN model for image classification tasks.
---
🔍 *What you'll learn:*
1. *Data Preprocessing and Augmentation:* Learn how to augment your dataset by mirroring images to create a larger and more diverse training set.
2. *Building a CNN Model:* Step-by-step guide to constructing a CNN model with Keras, including convolutional layers, pooling layers, and fully connected layers.
3. *Training and Evaluating the Model:* Understand the process of compiling, training, and evaluating your model to ensure it performs well on unseen data.
4. *Classifying New Images:* Implement a function to classify new images using the trained model and see it in action.
---
📁 *Code and Resources:*
Get access to the complete code and resources used in this tutorial:
[GitHub Repository]https://github.com/vedprakash11/Image_clas...
Dataset: https://figshare.com/articles/figure/Image...
---
---
💬 *Have questions or feedback?*
Drop a comment below or join the discussion on our [Discord Server](#)
👍 *Like, Share, and Subscribe!*
If you found this video helpful, don't forget to give it a thumbs up, share it with your friends, and subscribe to our channel for more tutorials and tech content!
---
🔔 *Hit the bell icon* to get notified whenever we post a new video!
---
*Tags:*
#ImageClassification #TensorFlow #Keras #DeepLearning #DataAugmentation #CNN #MachineLearning #AI #Python #CodingTutorial
---
*About Us:*
Welcome toGeekyCodes! We're here to help you navigate the exciting world of AI, machine learning, and data science. Whether you're a beginner or an experienced developer, we've got something for you. Stay tuned for more tutorials, project walkthroughs, and industry insights!
---
Thank you for watching and happy coding! 🚀
---