Image Recognition with Neural Networks From Scratch

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Image Recognition with Neural Networks From Scratch provided by Udemy is a comprehensive online course, which lasts for 3-4 hours worth of material. Image Recognition with Neural Networks From Scratch is taught by Long Nguyen. Upon completion of the course, you can receive an e-certificate from Udemy. The course is taught in Englishand is Paid Course. Visit the course page at Udemy for detailed price information.

Overview
  • Write An Image Recognition Program in Python

    What you'll learn:

    • Write a Python program that recognizes images from scratch without using any libraries!
    • Understand A Neural Network is.
    • Understand some important mathematical prerequisites such as functions and their computational graphs.
    • Understand conceptually what a derivative and a gradient is to fully appreciate the Gradient Descent Algorithm.
    • Understand the Gradient Descent Algorithm, the central algorithm in machine learning with Neural Networks.
    • Understand Backpropagation and its importance in computing gradients.
    • Be able to implement the full Python program in 50 lines of code that recognizes images.

    This is an introduction to Neural Networks. The course explains the math behind Neural Networks in the context of image recognition. By the end of the course, we will have written a program in Python that recognizes images without using any autograd libraries. The only prerequisite is some high school precalculus. Although the prerequisite is minimal, we will discuss many advanced topics including:

    1) functions and their computational graphs.

    2) neural networks

    3) conceptually understand the derivative and the gradient.

    4) gradient descent and backpropagation

    5) the multivariable chain rule

    6) mini-batch gradient descent