Deep Learning

Artificial Intelligence (AI)  is a thriving field that everybody talks about. Thanks to AI we can now solve problems that were impossible to imagine before: we can build models that can translate sentences in a lot of languages, we can help doctors make better diagnoses and we can even build self-driving cars. These amazing results are due to the spread of an AI subfield called Deep Learning. But what is deep learning precisely and how does it work?

02/ What is deep learning?

Deep learning

Deep learning is a type of machine learning, a group of techniques that mimics the human brain’s capability of learning from experience. Machine learning algorithms allow computers to solve problems using data as examples instead of coding an explicit set of rules. In this way, machine learning can tackle problems that are too complex for traditional software development.

Deep learning in the future of AI

Deep learning is especially good at working with image, video, audio, and natural language data. For example, deep learning can be used to automate the detection of production defects, to improve customer experience with sentiment analysis, or to detect malfunctioning machinery from audio recordings. This field is achieving results that were not possible before and is a key technology for the future of automation.

03/ How does deep learning work?

Deep learning uses data to incrementally improve its performance. But how does this learning process work?

Learning from mistakes

Let’s consider an example. You want to develop a deep learning model that is able to detect whether a car is present in an image or not. You show the deep learning model thousands of labeled images and the deep learning algorithm tries to identify what are the features that make up a car.
The model compares its predictions with the correct answers and tries to improve.  Repeating this task many times, the model learns what a car looks like. Now, when the model comes across a car picture it will be able to identify it.

04/ Why use deep learning?

You now have an insight on what deep learning is and on how it works. But why should you use deep learning models in your projects?

Detect complex patterns automatically

One of the main advantages of deep learning lies in being able to detect complex patterns in data. This feature allows deep learning to work with unstructured data like text and images and build efficient decision rules that do not have to be designed by hand. This also means that you don't need to rely on domain expertise for feature extraction, the algorithm will do everything by itself.

The best at scaling

Data is everywhere and deep learning is the best technique to take advantage of large amounts of information. Deep learning algorithms outperform older machine learning techniques when they have lots of examples to learn from. Sometimes deep learning can even overcome human performance!

Deep learning is reshaping business across industries, helping companies and workers to reduce production costs and improve speed and accuracy.

05/ How to use deep learning

Deep learning is a thriving research field and its list of applications is getting longer and more impressive.  It is already vastly employed in the solution of complex business problems. Here is a collection of tangible use cases which can be solved with deep learning.

Example 1: Detect defect products

You can use deep learning to detect defect products on a production line through images. Feed the deep learning systems images of the possible defects and let it automatically find the patterns it needs to detect the defective products in the future.

Example 2: Detect similarity

You can use deep learning to determine how similar the content of two documents is. Show the deep learning system thousands of sentences from different contexts (newspapers, articles, books, social media messages, etc.) and let it automatically understand how written language works and what is considered to be similar content.

Example 3: Analyze sound

You can determine the type of failure of a motor by sound only.  Feed the deep learning systems audio clips of the different kinds of failure modes in normal operation (i.e. including normal background sounds) and let it automatically find the patterns it needs to differentiate the different failure modes.

06/ 5 things you need to know about deep learning

-The frontier of AI: Deep learning is a way to automatically make models that can solve complex problems with images and text data.  

-It looks like magic but it’s not: Deep learning systems learn through examples, so it’s pivotal to make sure that you have the right data for the problem at hand. 

- A new way of  working: Deep learning is reshaping business across industries with a growing list of applications.

- Easy to try yourself: The best way to learn how deep learning works is to try it out yourself. With our platform, you can build an operational AI model that can recognize handwritten numbers.

- The next step: You can also work with text data and develop a model that can classify customer complaints.