<ul><li>The article explains how to build a neural network from scratch, step-by-step and without shortcuts.&nbsp;</li><li>The process of training a neural network involves calculating errors and adjusting guesses based on those errors.&nbsp;</li><li>A learning rate is used to control the size of adjustments, preventing overshoot and zigzagging.&nbsp;</li><li>Activation functions are essential for neural networks, allowing them to model complex patterns.&nbsp;</li><li>Bias is used to adjust the baseline of a network, enabling it to capture patterns that don't naturally pass through the origin.&nbsp;</li><li>The article provides an example of building a neural network to approximate the function y = x + 1.&nbsp;</li><li>The article explains forward and backpropagation, which respectively involve making predictions and learning from mistakes to adjust the network's parameters.&nbsp;</li><li>The article also explains derivatives and the chain rule which are crucial to calculating gradients and improving a neural network's predictions.&nbsp;</li><li>The code for a simple neural network is provided on GitHub for readers to experiment with.&nbsp;</li></ul>

Building Neural Networks Manually from Scratch: A Beginner’s Hello World

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