Deep Learning: Image Recognition

سرفصل های دوره

Thanks to deep learning, image recognition systems have improved and are now used for everything from searching photo libraries to generating text-based descriptions of photographs. In this course, learn how to build a deep neural network that can recognize objects in photographs. Find out how to adjust state-of-the-art deep neural networks to recognize new objects, without the need to retrain the network. Explore cloud-based image recognition APIs that you can use as an alternative to building your own systems. Learn the steps involved to start building and deploying your own image recognition system.

1. Introduction

001. Build cutting-edge image recognition systems

002. What you should know

003. Exercise files

2. Setting Up Your Development Environment

004. Installing Python 3, Keras, and TensorFlow on macOS

005. Installing Python 3, Keras, and TensorFlow on Windows

3. How Image Classification Works

006. What is a neural network

007. Coding a neural network with Keras

008. Feeding images into a neural network

009. Recognizing image contents with a neural network

010. Adding convolution for translational invariance

4. Designing a Deep Neural Network for Image Recognition

011. Designing a neural network architecture for image recognition

012. Exploring the CIFAR-10 data set

013. Loading an image data set

014. Dense layers

015. Convolution layers

016. Max pooling

017. Dropout

018. A complete neural network for image recognition

5. Building and Training the Deep Neural Network

019. Setting up a neural network for training

020. Training a neural network and saving weights

021. Making predictions with the trained neural network

6. Fine-Tuning Pre-trained Neural Networks

022. Pre-trained neural networks included with Keras

023. Using a pre-trained network for object recognition

024. Transfer learning as an alternative to training a new neural network

025. Extracting features with a pre-trained neural network

026. Training a new neural network with extracted features

027. Making predictions with transfer learning

7. Using an Image Recognition API

028. When to use an API instead of building your own solution

029. Introduction to the Google Cloud Vision API

030. Setting up Google Cloud Vision account credentials

031. Recognizing objects in photographs with Google Cloud Vision

032. Extracting text from images with Google Cloud Vision

8. Conclusion

033. Next steps

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تولید کننده: LinkedIn Learning (Lynda)