Category: 4. Deep Learning

While Marcus has pointed out the flaws in deep learning, the fact is that this AI approach is still extremely powerful. In less than a decade, it has revolutionized the tech world—and is also significantly impacting areas like finance, robotics, and healthcare. With the surge in investments from large tech companies and VCs, there will be further…

Given all the innovations and breakthroughs, it’s reasonable that many people consider deep learning to be a silver bullet. It will mean we no longer have to drive a car. It may even mean that we’ll cure cancer. How is it not possible to be excited and optimistic? This is natural and reasonable. But it…

Regarding chip systems for deep learning, GPUs have been the primary choice. But as AI gets more sophisticated—such as with GANs—and the datasets much larger, there is certainly more room for new approaches. Companies also have custom needs, such as in terms of functions and data. After all, an app for a consumer is usually…

With so much money and resources being devoted to deep learning, there has been a surge in innovations. It seems that every day there is something amazing that is being announced. Then what are some of the applications? Where has deep learning proven to be a game changer? Let’s take a look at some that…

The most basic type of a neural network is a fully connected neural network. As the name implies, it is where all the neurons have connections from layer to layer. This network is actually quite popular since it means having to use little judgment when creating the model. Then what are some of the other…

One of the major drawbacks with artificial neural networks is the process of making adjustments to the weights in the model. Traditional approaches, like the use of the mutation algorithm, used random values that proved to be time consuming. Given this, researchers looked for alternatives, such as backpropagation. This technique had been around since the 1970s but…

At the most basic level, an artificial neural network (ANN) is a function that includes units (which may also be called neurons, perceptrons, or nodes). Each unit will have a value and a weight, which indicates the relative importance, and will go into the hidden layer. The hidden layer uses a function, with the result…

Weighing only about 3.3 pounds, the human brain is an amazing feat of evolution. There are about 86 billion neurons—often called gray matter—that are connected with trillions of synapses. Think of neurons as CPUs (Central Processing Units) that take in data. The learning occurs with the strengthening or weakening of the synapses. The brain is made up…

There is often confusion between deep learning and machine learning. And this is reasonable. Both topics are quite complex, and they do share many similarities. So to understand the differences, let’s first take a look at two high-level aspects of machine learning and how they relate to deep learning. First of all, while both usually…