It could be argued that one of the most influental technologies of the past decades is (artificial) neural networks.
Artificial neural networks is the fundamental piece of deep learning algorithms and machine learning. In this series, you will find beginner-friendly blogs which aim to explain what neural networks are, the pros and cons of neural networks, & more.
The concept and science behind artificial neural networks have existed for many decades. But it has only been in the past few years that the promises of neural networks have turned to reality and helped the AI industry emerge from an extended winter. While neural networks have helped the AI take great leaps, they are also often misunderstood. Here’s everything you need to know about neural networks. Artificial neural networks are inspired from their biological counterparts.
It is important to know which activation functions to use within your neural network. Be aware of the fact that you can use different activation functions at different layers. Most often the sigmoid function is used but often other functions can work much better. In this post you will learn the most common Activation Functions within Deep Learning and when you should use them. You will also discover why you mostly need to use non-linear activation functions.
Keras is one of the most popular Deep Learning libraries out there at the moment and made a big contribution to the commoditization of artificial intelligence. It is simple to use and it enables you to build powerful Neural Networks in just a few lines of code. In this post, you will discover how you can build a Neural Network with Keras that predicts the sentiment of user reviews by categorizing them into two categories: positive or negative.
Introduction Access to and control of data is one of the biggest challenges faced by data analysts and data scientists. Creative, persistent analysts find ways to get access to at least some of this data but doing that efficiently in a way that is also standardized and centralized for everyone on the team is difficult.
Introduction: Prediction is a tricky business. You have to step outside of your comfort zone, your fainted vision of the world and see it thorough across all possible dimensions. In this series, we will discuss the future of “AI”, applications that are yet unexplored.
Introduction: Humans are wired to make tough decisions bringing all the context and principles to bear. Similarly, can devices apply the available information to make the right judgment calls? In this series, we shall discuss some ethical dilemmas faced by emerging technologies.
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