Artificial Intelligence (AI) is changing our world rapidly and Deep Learning (DL) is one of its core contributors. The hype around DL is real and the technology has been evolving fast in the last few years. However, can it live up to the hype?
The terms AI and DL are often used interchangeably but they are not quite the same. AI is referred to as the vast field of research where the goal is to make a device interact with its environment like an intelligent being. Machine Learning (ML) is a subset of AI in which a machine learns to perform a certain task without being explicitly programmed. Deep learning is a sub-field of ML dealing with algorithms that uses deep artificial neural networks.
When applied to the right use cases, AI-based solutions can be highly valuable, in particular, we know that Deep learning is already a reality in our daily lives: it’s the secret sauce behind driverless cars, it’s widely used to improve and automate disease detection from medical images or even for language processing for computers, and the list goes on. So it’s very comfortable to say that the technology has surpassed the hype.
Although Deep Learning has been achieving quite impressive results in real-world applications, it’s important to keep in mind that’s not magic and, for those results to be achieved, vast amounts of data are required. Besides that, learning from this amount of data is a very time-consuming and computationally demanding process.
Nevertheless, it’s awesome how these algorithms can “learn” without the need to have someone telling the model what to look for — it learns based on the experience and the given examples. And as mentioned, the progress in this field has led to the development of astonishing and useful applications that I’ll cover next.
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Understanding the complexities associated with a language like its syntax, semantics, expressions, or even sarcasm, is one of the hardest tasks for machines to learn. Natural Language Processing through Deep Learning (Deep NLP) is trying to achieve the same. Earlier Bayesian model and SVM were used to build time and memory-consuming complex models but now vectors representations of words, convolutional neural networks (CNN), recurrent and recurrent neural networks (RNNs), and memory augmenting strategies are helping achieve new heights in NLP. With the help of DL, machines can now recognize human voices, translate languages, summarize large text, and can even generate human-like text. Google’s Assistant, Amazon’s Alexa, and Apple’s Siri are some of the most popular applications of Deep NLP.
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Recently DL has been proven to be a very important tool in medical care. Deep Learning models can predict the risk of life-threatening diseases (like cancer, pneumonia, etc) by analyzing medical images (X-ray, CT scan, MRI, ECG, etc.) of a particular patient. These DL models are helping doctors to diagnose diseases accurately and at an early stage thus saving the lives of countless patients.
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DL can now create artworks similar to famous artists like Van Gogh or Picasso thanks to the neural style transfer. The idea is to separate the style representation and content representations in a CNN learned during a computer vision task. Following this concept, the model employs a pre-trained convolutional neural network to transfer styles from a given image to another. When we provide two images (one as context and the other as style) the model will return an image that will have the shape (outline) of the content image but the color and texture of the style image. This is how we can make any picture look like a painting.
This already gives a good idea of what Deep Learning is, proving to be much more than the hype and how it has revolutionized modern life. Nevertheless, that is much more related to DL when it comes to solving real-world problems, mainly related to data, which will be covered in the next posts.
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