We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Although neural networks have been studied for decades, over the past couple of years there have been many small but significant changes in the default techniques used. For example, ReLU (rectified ...

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 ...

The upside is that the neural network is effective and lower effort than trying to develop heuristics manually, while the downside is that you aren’t really in control of what the code is doing.

It's the complexity of neural networks that seems to confuse people. The many layers and the nodes in each layer makes explanations difficult, especially since many coders don’t want to explain ...

There’s tinn — the tiny neural network. If you can compile 200 lines of standard C code with a C or C++ compiler, you are in business. There are no dependencies on other code.

Spiking neural networks, however, face their own challenges in the training of the models. Many of the optimization strategies that have been developed for regular neural networks and modern deep ...

Understanding Neural Network Input-Output Before looking at the demo code, it's important to understand the neural network input-output mechanism. The diagram in Figure 2 corresponds to the demo ...