Transfer Learning for Food Classification

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Transfer Learning for Food Classification Transfer Learning for Food Classification. In this hands-on project, we will train a deep learning model to predict the type of food and then fine tune the model to improve its performance. This project could be practically applied in food industry to detect the type and quality of food.

In this Guided Project, you will:

Understand the theory and intuition behind Convolutional Neural Networks (CNNs) and transfer learning

Build and train a Deep Learning Model using Pre-Trained InceptionResnetV2

Assess the performance of trained CNN using various Key performance indicators

In this hands-on project, we will train a deep learning model to predict the type of food and then fine tune the model to improve its performance. This project could be practically applied in food industry to detect the type and quality of food. In this 2-hours long project-based course, you will be able to:

– Understand the theory and intuition behind Convolutional Neural Networks (CNNs).

– Understand the theory and intuition behind transfer learning. – Import Key libraries, dataset and visualize images.

– Perform data augmentation. – Build a Deep Learning Model using Pre-Trained InceptionResnetV2.

– Compile and fit Deep Learning model to training data.

– Assess the performance of trained CNN and ensure its generalization using various KPIs.

Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

Understand the Problem Statement and Business Case

Import Libraries and Datasets

Perform Data Exploration and Visualization

Perform Image Augmentation and Create Data Generator

Understand the theory and intuition behind Transfer Learning

Build Deep Learning model using Pre-trained Inception ResNet

Compile and Train Deep Learning Model

Fine Tune the Trained Model

Assess the Performance of the Trained Model

This project is a food classification transfer learning project based on pretrained VGG16, VGG19

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