This tutorial provides a step-by-step easy guide on how to implement and train a CNN model for Malaria cell classification using TensorFlow and Keras.
🔍 What You’ll Learn 🔍:
Data Preparation — In this part, you’ll download the dataset and prepare the data for training. This involves tasks like preparing the data , splitting into training and testing sets, and data augmentation if necessary.
CNN Model Building and Training — In part two, you’ll focus on building a Convolutional Neural Network (CNN) model for the binary classification of malaria cells. This includes model customization, defining layers, and training the model using the prepared data.
Model Testing and Prediction — The final part involves testing the trained model using a fresh image that it has never seen before. You’ll load the saved model and use it to make predictions on this new image to determine whether it’s infected or not.
Check out our tutorial here :
https://youtu.be/WlPuW3GGpQo&list=UULFTiWJJhaH6BviSWKLJUM9sg
Link for the code : https://ko-fi.com/s/98d022c834
If you are interested in learning modern Computer Vision course with deep dive with TensorFlow , Keras and Pytorch , you can find it here : http://bit.ly/3HeDy1V
Perfect course for every computer vision enthusiastic
Before we continue , I actually recommend this book for deep learning based on Tensorflow and Keras : https://amzn.to/3STWZ2N
You can find more tutorials, and join my newsletter here : https://eranfeit.net/
Enjoy
Eran