Applied Deep Learning with Python

This course demonstrates an applied approach to deep learning, through image identification and image generation-based applications.

 

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Includes:
  • 2.4 Hours of Video
  • Certificate of Completion
  • Source Code
  • Closed Captions
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Benefits

  • On-demand, 24/7 access
  • 2.4 hours of video
  • Certificate of completion
  • Source code and PDF notes
  • Closed captions

Overview

This course takes an applied approach to deep learning, and features a step-by-step guide that shows you how to build two web apps – one which identifies uploaded images by comparing it to a library of images, and another which generates images based upon a training set. Using Python, Flask, and Keras backed by Tensorflow, you will learn skills that will enable you to better identify, understand, and debug issues that can arise during the training process, ensuring that your deep learning models will perform better ever before.

What you will learn

  • Dataset Augmentation – making the most out of a limited dataset
  • Parameter Updating – ensure your models remain state of the art
  • Tips and tricks to deal with common problems, such as overfitting, or dealing with convolutional networks of varying sizes
  • An introduction to Flask – a framework that allows you to create these web apps

 

Frameworks and tools covered: Python 3.6, Anaconda 5.0, Flask 0.12.2, NumPy 1.13, Matplotlib 2.1, Tensorflow 1.4, Keras 2.1

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Lessons

Requirements

  • Intermediate Python programming skills
  • Familiarity with Artificial Neural Networks
  • Familiarity with Convolutional Neural Networks
  • Familiarity with Generative Adversarial Networks
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