In this detailed guide, I will explain everything there is to know about activation functions in deep learning. Especially what activation functions are and why we must use them when implementing neural networks.
Artificial Intelligence is on the rise. The pace of growth for artificial intelligence within the consumer, enterprise, government, and defense sectors continues. In this article, we will analyze the current size of the AI market and make forecasts for the future.
In this article, I will present you the most sophisticated optimization algorithms in Deep Learning that allow neural networks to learn faster and achieve better performance. These algorithms are: Stochastic Gradient Descent with Momentum, AdaGrad, RMSProp, and Adam Optimizer.
I this article I will present you the theory and practical implementation of a very useful and effective technique called Batch Normalization, which can significantly accelerate the training of a neural network model.
This is a beginner's guide to Deep Learning and Neural networks. In the following article, we are going to discuss the meaning of Deep Learning and Neural Networks. In particular, we will focus on how Deep Learning works in practice.
In this article we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning. Furthermore we will adress the question why Deep Learning as a young emerging field is far supirior to tradional Machine Learning.