Transfer Learning for Food Classification

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在此指导项目中,您将:

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

Clock2 hours
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

Deep LearningMachine LearningPython ProgrammingArtificial Intelligence(AI)Computer Vision

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Understand the Problem Statement and Business Case

  2. Import Libraries and Datasets

  3. Perform Data Exploration and Visualization

  4. Perform Image Augmentation and Create Data Generator

  5. Understand the theory and intuition behind Transfer Learning

  6. Build Deep Learning model using Pre-trained Inception ResNet

  7. Compile and Train Deep Learning Model

  8. Fine Tune the Trained Model

  9. Assess the Performance of the Trained Model

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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