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Introduction to Deep Learning with Keras

Learn to start developing deep learning models with Keras.

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4 Hours15 Videos59 Exercises22,897 Learners4950 XPDeep Learning TrackMachine Learning Scientist Track

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Course Description

Deep learning is here to stay! It's the go-to technique to solve complex problems that arise with unstructured data and an incredible tool for innovation. Keras is one of the frameworks that make it easier to start developing deep learning models, and it's versatile enough to build industry-ready models in no time. In this course, you will learn regression and save the earth by predicting asteroid trajectories, apply binary classification to distinguish between real and fake dollar bills, use multiclass classification to decide who threw which dart at a dart board, learn to use neural networks to reconstruct noisy images and much more. Additionally, you will learn how to better control your models during training and how to tune them to boost their performance.

  1. 1

    Introducing Keras


    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!

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    What is Keras?
    50 xp
    Describing Keras
    50 xp
    Would you use deep learning?
    50 xp
    Your first neural network
    50 xp
    Hello nets!
    100 xp
    Counting parameters
    100 xp
    Build as shown!
    100 xp
    Surviving a meteor strike
    50 xp
    Specifying a model
    100 xp
    100 xp
    Predicting the orbit!
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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In the following tracks

Deep LearningMachine Learning Scientist


sara-billenSara Billenhillary-green-lermanHillary Green-Lerman
Miguel Esteban Headshot

Miguel Esteban

Data Scientist & Founder

Miguel is an entrepreneur and data scientist. He has worked at companies like Endesa, where he applied Deep Learning to solve and automate problems related to electrical consumption curves. He is currently one of the founders at XtremeAI, where he is working in building products delivering automatic data extraction from complex documents. In it's free time he plays music, takes pictures, experiments with AI and thinks about the next big idea to built. You can follow or contact him on Twitter and LinkedIn.
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