TensorFlow tutorials

Introduction This end-to-end tutorial shows how to build a complete image classification pipeline in Python using TensorFlow Keras, focusing specifically on image classification with keras.You will prepare the dataset folders, split images into train and validation sets, build and train a convolutional neural network (CNN) with augmentation and callbacks, and finally run single-image predictions with

Introduction This tutorial shows a complete TensorFlow image classification pipeline using Keras CNN.You will learn how to split a weather photo dataset into train and validation sets.You will apply image data augmentation with Keras ImageDataGenerator to improve generalization.You will build and train a deep convolutional neural network for five weather classes.You will visualize accuracy and

Brain Tumor Classification with CNN and Keras This tutorial builds a complete deep learning pipeline for brain tumor classification from brain MRI images using Python and Keras.You will organize the dataset into train, validation, and test splits to ensure reliable medical image classification results.A compact CNN architecture will be trained with image augmentation, binary cross-entropy,

TensorFlow Image Classification with Keras: Flower Recognition, Data Augmentation, and OpenCV Prediction This tutorial demonstrates end-to-end image classification with TensorFlow and Keras, focusing on a real dataset of flowers.You’ll prepare data with ImageDataGenerator, train two CNN variants (a deeper 224×224 model and a lightweight 64×64 baseline), visualize accuracy and loss with Matplotlib, and finally run

Build a Food Classification Pipeline with Python and TensorFlow Introduction Food classification is the process of teaching a model to recognize different food items from images.This tutorial shows how to prepare image data so a model can classify food reliably.You will organize labels, standardize image sizes, and serialize arrays for fast training.By the end, you