Building a Neural Network from Scratch in Python – Neural Networks from Scratch Part 3


In this video, we create a Neural Network by creating a Layer class, in which we define the feedforward and backpropagation functions. Then we implement the XOR function by training on this network, and finally plot the cost function.
This is the third video of my series Neural Networks from Scratch in Python – where I explain the inner workings of a neural network and finally implement it from scratch in python.

Part 1: Feedforward Explained
Part 2: Gradient Descent and Backpropagation Explained


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Comment List

  • Adarsh Menon
    November 23, 2020

    Great explanation…the series helped me a lot to understand NN. Pls also make more videos on activation and loss functions. Also, if you could make a video to construct a NN on a real example, it helps beginners like me. Thank you very much.

  • Adarsh Menon
    November 23, 2020

    u really helped me understand NN. I also got into optimizing the algorithm by changing the way in which one initialize the weights, or dynamically changing the learning rate. Thanks a lot man u deserve more views. I want to thank towardsdatascience too cause it is an amazing website where begginers like me can find well explained examples.

  • Adarsh Menon
    November 23, 2020

    Keep it up dost, please make sone videos on Svm and it's implementation

  • Adarsh Menon
    November 23, 2020

    how can i change XOR to continues values ?

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