Neural Network python from scratch | MultiClass Classification with Softmax

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Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in models, but we will understand the Mathematics and Code behind the Neural Network in Python. ➖➖➖➖IMPORTANT NOTE➖➖➖➖ I made a small mistake while typing the code for derivative_tanh(x) function. ✔ The correct code will be : def derivative_tanh(x): return 1 - np.power(np.tanh(x), 2) ❌ And not: def derivative_tanh(x): return 1 - np.power(x, 2) I have explained it in this video at 19:05 : https://www.youtube.com/watch?v=URJ9pP1aURo&list=PLuhqtP7jdD8CftMk831qdE8BlIteSaNzD&index=5 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📕 Download the ASSIGNMENT and Implementation Code : https://github.com/Coding-Lane/Neural-Network---MultiClass-Classifcation-with-Softmax ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ✔ Complete Neural Network Playlist : https://www.youtube.com/watch?v=mlk0rddP3L4&list=PLuhqtP7jdD8CftMk831qdE8BlIteSaNzD ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📕 LEARNINGS : Why did Cost increased at the beginning of training the model? ✔ Answer : It is because our weights W were very large. We multiplied 0.01 with them, but still it was not enough. ✔ Solution : Multiply 0.001 with weights W, instead of 0.01. You will that problem will be solved and you will have a steady decreasing cost function. There are many weight initialization techniques in Deep Learning to solve such problems and more. We will discuss them in the upcoming videos. We will see why is it necessary to initialize weights randomly and not with zeros. And also why weights need to be small ! ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ✔ How Neural Network works ? : https://www.youtube.com/watch?v=mlk0rddP3L4&list=PLuhqtP7jdD8CftMk831qdE8BlIteSaNzD&index=1 ✔ Forward and Backpropagation in Neural Network : https://www.youtube.com/watch?v=Tb23YtZ92AE&list=PLuhqtP7jdD8CftMk831qdE8BlIteSaNzD&index=2 ✔ Activation Functions in Neural Network : https://www.youtube.com/watch?v=Y9qdKsOHRjA

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