I prefer Option 2 and take that approach to learning any new topic. I might not be able to tell you the entire math behind an algorithm, but I can tell you the intuition. I can tell you the best scenarios to apply an algorithm based on my experiments and understanding. In my interactions with people, I find that people don't take time to develop this intuition and hence they struggle to apply things in the right manner. In this article, I will discuss the building block of a neural network from scratch and focus more on developing this intuition to apply Neural networks.

A one-layer network with R input elements and S neurons follows. In this network, each element of the input vector p is connected to each neuron input through the weight matrix W. The ith neuron has a summer that gathers its weighted inputs and bias to form its own scalar output n(i). The various n(i) taken together form an S-element net input vector n. Finally, the neuron layer outputs form a column vector a. The expression for a is shown at the bottom of the figure.

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The GAN architecture is comprised of both a generator and a discriminator model. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The generator model is typically implemented using a deep convolutional neural network and results-specialized layers that learn to fill in features in an image rather than extract features from an input image. Two common types of layers that can be used in the generator model are a upsample layer (UpSampling2D) that simply doubles the dimensions of the input and the transpose convolutional layer (Conv2DTranspose) that performs an inverse convolution operation. In this tutorial, you will discover how to use UpSampling2D and Conv2DTranspose Layers in Generative Adversarial Networks when generating images. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.

The human brain processes information flows continuously from the external environment. However, it can modify and update the stored images, and create new, without destroying what previously memorized. Thus it differs significantly from the majority of neural networks as neural networks (NN), trained by back propagation, genetic algorithms, in bidirectional associative memory, Hopfield networks, etc. very often a new way of learning, situation or association significantly distorts or even destroys the fruits of prior learning, requiring a change in a significant part of weights of connections or complete ret raining of the network [1-4]. Impossibility of using the specified NN solve the problem of stability-plasticity, that is a problem of perception and memorization of new information without loss or distortion of existing, was one of the main reasons for the development of fundamentally new configurations of neural networks. Examples of such networks are neural networks, derived from the adaptive resonance theory (ART), developed by Carpenter and Grossberg [5, 6].

Tensorflow is arguably the most popular package in deep learning and the neural network domain. I wrote a few different tutorials before on Regular Dense Neural Networks, CNN structure, and RNNs. But all my tutorials on Tensorflow were on classification problems. In this article, I would like to work on a regression problem and demonstrate some models of both Sequential and Function APIs. I already did all the data cleaning.