Luca Mueller

This article is too short. It can’t even begin to describe the ways in which deep learning will affect you in the future. Consider this article to be offering a tantalizing tidbit — an appetizer that can whet your appetite for exploring the world of deep learning further.These deep learning applications are already common in some cases.

Convolutional neural networks (CNN) are the building blocks of deep learning–based image recognition, yet they answer only a basic classification need: Given a picture, they can determine whether its content can be associated with a specific image class learned through previous examples. Therefore, when you train a deep neural network to recognize dogs and cats, you can feed it a photo and obtain output that tells you whether the photo contains a dog or cat.

Neural networks provide a transformation of your input into a desired output. Even in deep learning, the process is the same, although the transformation is more complex. In contrast to a simpler neural network made up of few layers, deep learning relies on more layers to perform complex transformations. The output from a data source connects to the input layer of the neural network, and the input layer starts processing the data.

As a simplification, you can view language as a sequence of words made of letters (as well as punctuation marks, symbols, emoticons, and so on). Deep learning processes language best by using layers of RNNs, such as LSTM or GRU. However, knowing to use RNNs doesn't tell you how to use sequences as inputs; you need to determine the kind of sequences.

Sentiment analysis computationally derives from a written text using the writer’s attitude (whether positive, negative, or neutral), toward the text topic. This kind of analysis proves useful for people working in marketing and communication because it helps them understand what customers and consumers think of a product or service and thus, act appropriately (for instance, trying to recover unsatisfied customers or deciding to use a different sales strategy).

Given the embarrassment of riches that pertain to AI as a whole, such as large amounts of data, new and powerful computational hardware available to everyone, and plenty of private and public investments, you may be skeptical about the technology behind deep learning, which consists of neural networks that have more neurons and hidden layers than in the past.

Once you know how neural networks basically work, you need a better understanding of what differentiates them to understand their role in deep learning. Beyond the different neural network architectures, the choice of the activation functions, optimizers and the neural network's learning rate can make the difference.

Machine learning is an application of AI that can automatically learn and improve from experience without being explicitly programmed to do so. The machine learning occurs as a result of analyzing ever increasing amounts of data, so the basic algorithms don’t change, but the code's internal weights and biases used to select a particular answer do.

What is deep learning? Deep learning is a subcategory of machine learning. With both deep learning and machine learning, algorithms seem as though they are learning. This is accomplished when the algorithms analyze huge amounts of data and then take actions or perform a function based on the derived information.