Neural Network from Scratch – Machine Learning Python




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From scratch implementation of neural network in Python using only the numpy library. There’s a lot of mathematics behind Neural Networks particularly for back-prop and I’ve made previous videos going through the mathematics for NN and a blog post to enable us to focus on the implementation in this video.

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Github repository:
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Blog post: https://aladdinperzon.github.io/2018/09/20/Backpropogation-for-2-layers-softmax-neural-network/

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Comment List

  • Aladdin Persson
    November 15, 2020

    where is the math you said you go through in the first minute? you are talking there about previous videos. I don't find them.
    thanks in advance and thanks for all your videos.

  • Aladdin Persson
    November 15, 2020

    Could you help: How can I assign the accuracy to a variable, like a=accuracy or a = binary_accuracy. This is part of the code of the neural network:

    model = Sequential()

    model.add(Dense(48, input_dim=48, activation='relu'))

    model.add(Dense(24, activation='relu'))

    model.add(Dense(2, activation='sigmoid'))

    model.compile(loss='mean_squared_error',

    optimizer='adam',

    metrics=['binary_accuracy'])

    model.fit(training_data, target_data, epochs=1000)

    scores = model.evaluate(training_data, target_data)

    "training_data, target_data are arrays"

    Result of evaluation:

    binary_accuracy: 0.5000

    binary_accuracy: 50.00%

  • Aladdin Persson
    November 15, 2020

    I just have a question. Can we make the number of layers to be added parameterised?

  • Aladdin Persson
    November 15, 2020

    Very Informative. Really loved it.

  • Aladdin Persson
    November 15, 2020

    Just a small correction in the video I said 3 layered where I was counting the input layer as well: this is not correct, as you don't normally count the input layer. This implementation is then of a 2 layered Neural net.

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