First of all it's important to underline why this problem is so important today, and therefore why it is very interesting to understand the role and the potential of Deep Learning in this sector. During the last years, Time Series Classification has become one of the most challenging problems in Data Science. This has happened because any classification problem that uses data keeping in consideration some notion of sorting, can be treated as a Time Series Classification problem. Time series are present in many real-world applications ranging from health care, human activity recognition, cyber-security, finance, marketing, automated disease detection, anomaly detection, etc. As the availability of temporal data has increased significantly in the last years, many areas are becoming strongly interested in applications based on time series, and then many new algorithms have been proposed. All these algorithms, apart from those based on deep learning, require some kind of feature engineering as a separate task before the classification is performed, and this can imply the loss of some information and the increase of the development time. On the contrary, deep learning models already incorporate this kind of feature engineering internally, optimizing it and eliminating the need to do it manually.
We have some algorithm that's given a handful of labeled examples, say 10 images of dogs with the label 1 ("Dog") and 10 images of other things with the label 0 ("Not dog")--note that we're mainly sticking to supervised, binary classification for this post. The algorithm "learns" to identify images of dogs and, when fed a new image, hopes to produce the correct label (1 if it's an image of a dog, and 0 otherwise). We have some algorithm that's given a handful of labeled examples, say 10 images of dogs with the label 1 ("Dog") and 10 images of other things with the label 0 ("Not dog")--note that we're mainly sticking to supervised, binary classification for this post. The algorithm "learns" to identify images of dogs and, when fed a new image, hopes to produce the correct label (1 if it's an image of a dog, and 0 otherwise). This setting is incredibly general: your data could be symptoms and your labels illnesses; or your data could be images of handwritten characters and your labels the actual characters they represent.
Convolutional neural networks (CNN) – Might look or appears like magic to many but in reality, it's just simple science and mathematics only. CNN's are a class of neural networks that have proven very effective in areas of image recognition thus in most cases it's applied to image processing. CNNs got huge adoption and success within computer vision applications but mainly it is with supervised learning as compare with unsupervised learning which has got very little attention. This network is a great example of variation for multilayer perceptron for processing and classification. It's a deep learning algorithm in which it takes input as an image and put weights and biases effectively to its objects and finally able to differentiate images from each other. As per Wiki – In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analysing visual imagery. They exist already for several decades but were shown to be very powerful when large labeled datasets are used. This requires fast computers (e.g.
Neural networks are a series of algorithms that identify underlying relationships in a set of data. These algorithms are heavily based on the way a human brain operates. These networks can adapt to changing input and generate the best result without the requirement to redesign the output criteria. In a way, these neural networks are similar to the systems of biological neurons. Deep learning is an important part of machine learning, and the deep learning algorithms are based on neural networks.