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    Neural Networks in Python from Scratch: Learning by Doing

    Posted By: BlackDove
    Neural Networks in Python from Scratch: Learning by Doing

    Neural Networks in Python from Scratch: Learning by Doing
    Published 06/2022
    Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 452 MB | Duration: 29 lectures • 3h 32m


    From intuitive examples to image recognition in 3 hours - Experience neuromorphic computing & machine learning hands-on

    What you'll learn
    Program neural networks for 3 different problems from scratch in plain Python
    Start simple: Understand input layer, output layer, weights, error function, accuracy, training & testing at an intuitive example
    Complicate the problem: Introduce hidden layers & activation functions for building more useful networks
    Real-life application: Use this network for image recognition

    Requirements
    Basic programing skills are desired if you want to program along with me. We use Python3 without any advanced modules.
    Description
    This course is for everyone who wants to learn how neural networks work by hands-on programming!

    Everybody is talking about neural networks but they are hard to understand without setting one up yourself. Luckily, the mathematics and programming skills (python) required are on a basic level so we can progam 3 neural networks in just over 3 hours. Do not waste your time! This course is optimized to give you the deepest insight into this fascinating topic in the shortest amount of time possible.

    The focus is fully on learning-by-doing and I only introduce new concepts once they are needed.

    What you will learn

    After a short introduction, the course is separated into three segments - 1 hour each

    1) Set-up the most simple neural network: Calculate the sum of two numbers.
    You will learn about

    Neural network architecture

    Weights, input & output layer

    Training & test data

    Accuracy & error function

    Feed-forward & back-propagation

    Gradient descent

    2) We modify this network: Determine the sign of the sum.
    You will be introduced to

    Hidden layers

    Activation function

    Categorization

    3) Our network can be applied to all sorts of problems, like image recognition: Determine hand-written digits!
    After this cool and useful real-life application, I will give you an outlook

    How to improve the network

    What other problems can be solved with neural networks?

    How to use pre-trained networks without much effort

    Why me?

    My name is Börge Göbel and I am a postdoc working as a scientist in theoretical physics where neural networks are used a lot.
    I have refined my advisor skills as a tutor of Bachelor, Master and PhD students in theoretical physics and have other successful courses here on Udemy.

    I hope you are excited and I kindly welcome you to our course!

    Who this course is for
    This beginner-friendly course is for everyone! Especially if you
    Are curious about neural networks and want to really understand how they operate
    Work in machine learning or data science but have not yet programed a neural network yourself from scratch
    Want to really learn about machine learning without fancy frameworks/modules - Just you, me & standard python