1. Apprendre
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  2. Cours
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  3. Introduction to Deep Learning with Keras
  • 1

    Introducing Keras

    Gratuit

    In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

  • 2

    Going Deeper

    By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

  • 3

    Improving Your Model Performance

    In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

  • 4

    Advanced Model Architectures

    It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

Initializing

Exercice

Using a real world model

Okay, so Ivy's picture is ready to be used by ResNet50. It is stored in img_ready and now looks like this:

ResNet50 is a model trained on the Imagenet dataset that is able to distinguish between 1000 different labeled objects. ResNet50 is a deep model with 50 layers, you can check it in 3D here.

ResNet50 and decode_predictions have both been imported from tensorflow.keras.applications.resnet50 for you.

It's time to use this trained model to find out Ivy's breed!

Instructions

100 XP
  • Instantiate a ResNet50 model, setting the weights parameter to be 'imagenet'.
  • Use the model to predict on your processed image.
  • Decode the first 3 predictions with decode_predictions().