What is deep learning?

Deep learning is a powerful tool for tackling complex problems. It has transformed many areas of science and industry thanks to its ability to learn rich, hierarchical representations of data.

Working in this field requires advanced knowledge of mathematics, statistics and programming, all of which you can become very familiar with in the Master in Big Data Analytics at Universidad Europea.

Definition of deep learning

Deep learning is a sub-discipline of machine learning. It uses multi-layered artificial neural networks to model and understand complex patterns in data. This approach is inspired by the structure and functioning of the human brain.

The ability of deep learning to handle complex, unstructured data has opened up new opportunities and significantly improved efficiency and accuracy in multiple applications.

What deep learning is for: applications

Deep learning provides advanced tools for analysis and decision-making. Here are some of its most outstanding applications, examples of machine learning in its most complex version.

• Computer vision: deep learning has significantly improved image and object recognition. This technology is used, for example, in security systems to identify intruders using cameras and also to develop autonomous vehicles.
• Natural Language Processing (NLP): enables the development of more accurate chatbots and machine translation systems. It is also used to better understand opinions expressed on social networks and product reviews.
• Voice recognition: virtual assistants such as Siri, Alexa and Google Assistant use deep learning. It is also used in customer service centres to automate responses.
• Automation and robotics: thanks to this technology, industrial robots can learn to assemble products accurately from visual and tactile data.
• Medicine: deep learningmodels are applied in the diagnosis and prognosis of diseases, helping to personalise treatments.
• Content generation: Generative antagonistic networks (GANs) can generate realistic images, videos and music.
• Finance: in the financial sector, deep learning is used for fraud detection and risk analysis.

How does deep learning work?

Deep learning uses networks of interconnected nodes (neurons) to process and transform data to obtain features and predictions. To understand how it works, we need to look at each of its parts.

Neural network architecture

Neural networks are composed of an input layer, one or more hidden layers and an output layer. The input layer receives data, the hidden layer performs transformations and calculations, and the output layer provides the final prediction. Each connection between neurons has a weight that is adjusted during the training process.

Training process

Training a neural network involves adjusting the connection weights to minimise the error between model predictions and actual values. This process is done in several stages:

1. Forward propagation: input data is passed through the network, layer by layer. Each neuron performs a mathematical operation (usually a weighted sum followed by an activation function) and passes the result to the next layer.
2. Error calculation: on reaching the output layer, the model generates a prediction. This prediction is compared to the actual value using a loss (or cost) function, which quantifies the model error.
3. Backpropagation: the calculated error is propagated backwards through the network, adjusting the connection weights to reduce the error. This adjustment is done using an optimisation algorithm, such as gradient descent, which updates the weights in the direction that minimises the loss function.
4. Iteration and convergence: the forward and backward propagation steps are repeated for many iterations (epochs) until the model error reaches an acceptable level or stops decreasing significantly.

Trigger functions

Trigger functions introduce non-linearities into the network, allowing it to learn and represent complex relationships. Some common activation functions include the sigmoid function, the hyperbolic tangent (tanh) and the ReLU (Rectified Linear Unit).

Regularisation and optimisation

To avoidoverfitting and improve model performance, regularisation techniques such as neurondropout and L2 regularisation are used. In addition, optimisation algorithms, such as Adam and RMSprop, adjust the weights efficiently during training.

What you need to be a deep learning professional

Working with deep learning technologies is one of the jobs that a data scientist does. If you want to work in this field, you need a combination of academic background, technical skills and soft skills.

• Academic background: You will need a Bachelor's degree in Computer Science, Computer Engineering or related fields.
• Technical skills: advanced knowledge of linear algebra, calculus, optimisation, and probability theory is a must. You should be proficient in data manipulation tools such as SQL and NoSQL, and frameworks such as TensorFlow and Keras. You should also be proficient in programming languages such as Python, R or Julia.
• Soft skills: communication, critical thinking and problem-solving skills are highly recommended.

Deep learning is transforming the world of technology. It allows machines to learn and perform tasks autonomously and gives access to a new world of possibilities, as yet unexplored, in the field of artificial intelligence. If you want to work in this constantly evolving sector, you will need a solid technical background and a willingness to keep learning.