Implementing from scratch versus a library/framework
We implemented the machine learning algorithms of neural networks in Chapter 2, Algorithms for Machine Learning – Preparing for Deep Learning, and many deep learning algorithms from scratch in Chapter 3, Deep Belief Nets and Stacked Denoising Autoencoders and Chapter 4, Dropout and Convolutional Neural Networks. Of course, we can apply our own code to practical applications with some customizations, but we have to be careful when we want to utilize them because we can't deny the possibility that they might cause several problems in the future. What could they be? Here are the possible situations:
The code we wrote has some missing parameters for better optimization because we implemented just the essence of the algorithms for simplicity and so you better understand the concepts. While you can still train and optimize the model with them, you could get higher precision rates by adding another parameter of your own implementation.
As mentioned...
