Transfer Learning for Food Classification
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.
Skills you will develop
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
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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