https://github.com/trainingbypackt/applied-deep-learning-with-keras-elearning

Solve complex real-life problems with the simplicity of Keras
https://github.com/trainingbypackt/applied-deep-learning-with-keras-elearning

auc-roc-score cnn convolutional-neural-network cross-validation deep-learning feature-detection feature-map flattening keras keras-wrapper max-pooling null-accuracy precision python recurrent-neural-networks rnn scikit-learn sensitivity sequential-modelling specificity

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Solve complex real-life problems with the simplicity of Keras

• Host: GitHub
• URL: https://github.com/trainingbypackt/applied-deep-learning-with-keras-elearning
• Owner: TrainingByPackt
• License: mit
• Created: 2019-08-27T10:00:50.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-08-28T02:35:30.000Z (over 5 years ago)
• Last Synced: 2023-10-20T23:32:15.363Z (about 1 year ago)
• Topics: auc-roc-score, cnn, convolutional-neural-network, cross-validation, deep-learning, feature-detection, feature-map, flattening, keras, keras-wrapper, max-pooling, null-accuracy, precision, python, recurrent-neural-networks, rnn, scikit-learn, sensitivity, sequential-modelling, specificity
• Language: Jupyter Notebook
• Size: 7.55 MB
• Stars: 2
• Watchers: 4
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Applied-Deep-Learning-with-Keras

Applied Deep Learning with Keras takes you from a basic knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. This course teaches you new techniques to handle neural networks, and in turn, broadens your options as a data scientist. 	 

## What you will learn

* Understand the difference between single-layer and multi-layer neural network models

* Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks

* Apply L1, L2, and dropout regularization to improve the accuracy of your model

* Implement cross-validate using Keras wrappers with scikit-learn

* Understand the limitations of model accuracy

### Hardware requirements

For an optimal experience, we recommend the following hardware configuration:

* **Processor**: Intel Core i5 or equivalent

* **Memory**: 4 GB RAM (8 GB Preferred)

* **Storage**: 35 GB available space

### Software requirements

You'll also need the following software installed in advance:

* OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X

* Browser: Google Chrome/Mozilla Firefox Latest Version

* Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter notecourse on your browser)

* Python 3.4+ (latest is Python 3.7) installed (from https://python.org)

* Python libraries as needed (Jupyter, Numpy, Pandas, Matplotlib, BeautifulSoup4, and so on)