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Deep Learning: Image Recognition

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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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