Are you a data scientist planning to create an artificial neural network from scratch? Then you must try reading this trending book for sure!
What will you understand from this definitive guide?
Learn how to code a neuron
Learn how to connect these neurons in ANN layers
You will know about program activation functions such as Rectified Linear (ReLU), Softmax, Sigmoid, and Linear
You would learn to calculate cross-entropy loss
You will code and perform gradient computations using backpropagation and parameter updates using optimizers: Stochastic Gradient Descent (SGD), AdaGrad, RMSprop, and Adam.
And most importantly, you would build and train a fully working neural network, from scratch, in Python while learning a lot along the way!
How to build your neural network model in python.
Neural Networks from Scratch book will teach you about building a neural network without any libraries, so as to get a grip on deep learning concepts.
Also,the above mentioned book would prepare you in every stage of coding and computations in very definitive deep learning methods. You will have the clear idea on LAYERS OF NEURAL NETWORK.
In this part of the book you will come to know various problem solving mathematical computations. This phase is the training phase. And you would learn about optimization. The book finishes with the final phase of the neural network that is PREDICTION PHASE. Try reading this book to have a clear idea on building a NN from scratch.
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Neural Networks from Scratch:
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