Deep Learning: Image Recognition

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Adam Geitgey

1:43:51

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1. Introduction

001. Build cutting-edge image recognition systems.mp4

00:57

002. What you should know.mp4

00:30

003. Exercise files.mp4

00:31

2. Setting Up Your Development Environment

004. Installing Python 3, Keras, and TensorFlow on macOS.mp4

04:35

005. Installing Python 3, Keras, and TensorFlow on Windows.mp4

04:38

3. How Image Classification Works

006. What is a neural network.mp4

01:32

007. Coding a neural network with Keras.mp4

02:58

008. Feeding images into a neural network.mp4

02:40

009. Recognizing image contents with a neural network.mp4

02:51

010. Adding convolution for translational invariance.mp4

02:49

4. Designing a Deep Neural Network for Image Recognition

011. Designing a neural network architecture for image recognition.mp4

04:07

012. Exploring the CIFAR-10 data set.mp4

02:50

013. Loading an image data set.mp4

04:06

014. Dense layers.mp4

03:27

015. Convolution layers.mp4

05:15

016. Max pooling.mp4

01:40

017. Dropout.mp4

01:54

018. A complete neural network for image recognition.mp4

02:30

5. Building and Training the Deep Neural Network

019. Setting up a neural network for training.mp4

03:11

020. Training a neural network and saving weights.mp4

04:37

021. Making predictions with the trained neural network.mp4

08:54

6. Fine-Tuning Pre-trained Neural Networks

022. Pre-trained neural networks included with Keras.mp4

03:28

023. Using a pre-trained network for object recognition.mp4

03:40

024. Transfer learning as an alternative to training a new neural network.mp4

03:53

025. Extracting features with a pre-trained neural network.mp4

05:40

026. Training a new neural network with extracted features.mp4

01:49

027. Making predictions with transfer learning.mp4

03:30

7. Using an Image Recognition API

028. When to use an API instead of building your own solution.mp4

03:33

029. Introduction to the Google Cloud Vision API.mp4

02:14

030. Setting up Google Cloud Vision account credentials.mp4

02:40

031. Recognizing objects in photographs with Google Cloud Vision.mp4

03:03

032. Extracting text from images with Google Cloud Vision.mp4

03:22

8. Conclusion

033. Next steps.mp4

00:27

Description

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.

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Deep Learning

Neural Networks

Adam Geitgey

Instructor's Courses

Co-founder at Turquoise Health, previously AI/ML/NLP Consultant and a Software Engineer with 15+ years of experience. Writes about Machine Learning, AI and Software Engineering at http://www.machinelearningisfun.com/

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