5. Exercise Bookmark this page Projecto due Feb 17, 2021 01:59 EET As introduced in the previous section, a neural network is a powerful tool often utilized in machine learning. Because neural networks are, fundamentally, very mathematical, we'll use them to motivate Numpy! We review the simplest neural network here: * 2 X2 The output of the neural network, 21. is dependent on the inputs 21 and 22. The importance of each of the inputs is given by values called weights. There is one weight from each input to each output. We show this here: w X, W 11 Oz, X2 The inputs are given by 2, and the outputs are given by 21. Here, Wu is the weight of input 1 on output 1 (our only output in this case), and W21 is the weight of input 2 on output 1. In general, Wy repr sents the weight of input i on output j. The output, 21, is given by my = f(wuI1 + W21:22) 0=wirki + Wan 5. Exercise Bookmark this page Projecto due Feb 17, 2021 01:59 EET As introduced in the previous section, a neural network is a powerful tool often utilized in machine learning. Because neural networks are, fundamentally, very mathematical, we'll use them to motivate Numpy! We review the simplest neural network here: * 2 X2 The output of the neural network, 21. is dependent on the inputs 21 and 22. The importance of each of the inputs is given by values called weights. There is one weight from each input to each output. We show this here: w X, W 11 Oz, X2 The inputs are given by 2, and the outputs are given by 21. Here, Wu is the weight of input 1 on output 1 (our only output in this case), and W21 is the weight of input 2 on output 1. In general, Wy repr sents the weight of input i on output j. The output, 21, is given by my = f(wuI1 + W21:22) 0=wirki + Wan