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CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION

Convolutional Neural Networks for Image Classification

Course Description

Discover convolutional neural networks (CNNs) – the image recognition technology behind self-driving cars, facial recognition, fingerprint matching, and more. Learn all about this popular type of neural network while building two models for identifying handwritten numbers – one using TensorFlow 2.0 (a highly popular machine learning library), and another using Keras (a modular library specifically for neural networks).


Goals

  • How image recognition works
  • Real-world applications of image recognition
  • What the MNIST dataset is, and how to access and use it
  • Building, training, and testing CNN models with TensorFlow
  • Building, training, and testing CNN models with Keras


…and more!

Prerequisites

  • Basic knowledge of Python & Numpy and familiarity with Machine Learning. We recommend completing Machine Learning for Beginners with TensorFlow before taking this course.
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Curriculum

  • Resources
  • Introduction
    05:19
    Preview
  • Intro to Image Recognition – Part 1
    09:12
    Preview
  • Intro to Image Recognition – Part 2
    09:43
  • Tools for Image Recognition – Part 1
    08:05
  • Tools for Image Recognition – Part 2
    09:11
  • MNIST
    07:58
  • Building a CNN – Part 1
    10:27
  • Building a CNN – Part 2a
    09:25
  • Building a CNN – Part 2b
    09:49
  • Building a CNN – Part 3
    08:46
  • Building a CNN – Part 4
    09:56
  • Building a CNN – Part 5
    09:38
  • Building a CNN – Part 6a
    08:45
  • Building a CNN – Part 6b
    01:59
  • Intro to Keras
    04:56
  • Building a CNN with Keras
    10:04
  • Conclusion
    04:22
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CONVOLUTIONAL NEURAL NETWORKS FOR IMAGE CLASSIFICATION
This Course Includes
  • 2 hours
  • 18 Lectures
  • 1 Resources
  • Completion Certificate Sample Certificate
  • Lifetime Access Yes
  • Language English
  • 30-Days Money Back Guarantee
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