If you have come across machines recognising your face and voice among millions of images and wonder how is that possible, it is all to the credit of neural networks and deep learning. Neural networks are the algorithms that leverage the unique character of the human mind which thinks of possibilities. This characteristic is nothing but fuzzy logic, invented by Lotfi Zadeh. It resembles human reasoning and has inspired AI researchers to develop neural network algorithms. While machine learning algorithms take decisions according to the data they are fed, neural networks are designed to follow a path to decide for themselves. Researchers develop hundreds of algorithms in a day with different characteristics and performance capabilities and most of which build on existing models to predict and build real-world models. Neither all models can be applied to one problem, nor all problems can be addressed with one neural network.
It is a type of neural network designed as a feed-forward network. Information passes from one layer to other without revisiting the previous layers. It is designed to identify the pattern in raw data and improve on every new input it gets. The design architecture overlays three layers, where each layer adds weight to the passage of information. They are popularly known as Universal Functional Approximators, as they are capable of learning non-linear functions. Mostly used in predictive processes such as in business intelligence, text prediction, spam email detection, etc, it comes with few drawbacks and advantages over other algorithms.
Even though it is a layered algorithm, the chances of gradual corruption are low. Rather it occurs over a long period so that you have enough time to correct blunders. Unlike other networks, it stores information over the entire network leaving very little scope for disruption of the entire system because of a few missing pieces of information. This very characteristic makes ANN more fault-tolerant than others. They are popular for their multitasking capabilities, for they use a layered system where information is stored at every node, thereby developing an ability to generate outcomes by comparing the event with previous ones. Despite its numerous benefits, it is pretty much difficult to design an ANN for it takes a copious amount of data and a lot more trials to zero in on the right architecture. And also, ANN cannot identify sequential data required for sequential data processing.
Widely used for its computer vision applications, it comes with three layers viz. convolutional layer, pooling layer, and fully-connected layer. Computer vision, which is applied in image identification anchors on CNN networks. The complexity of algorithms increases with each layer. They analyze the input through a series of filters known as kernels. They are like matrices that move over the input data, used to extract features from the images. As the input images are processed, the links between neurons develop as kernels in the layers. For example, to process an image, kernels go through sequential layers and change accordingly in the process of identifying colors, shapes, and eventually, the overall image.
CNN algorithms have shot to fame after visual technology became the main source of information dissemination. The tasks which humans used to do earlier, now are made easy with AI-enabled tools developed for facial recognition, image recognition, handwritten character analysis, X-ray analysis, etc. CNN algorithms are still nascent and they do have issues working with variable data. It has been reported that CNN algorithms are not up to the mark when it comes to processing hidden objects in images, processing titled or rotated images. Training CNN algorithms requires good GPU (Graphical Processing Units), the lack of which might slow down the project.
Voice recognition and natural language processing are the two linchpins of the RNN network. Be it voice search with Apple's Siri, Google Translate, or Picasa's face detection technology, it is all possible because of RNN algorithms. Contrary to feed-forward networks, RNN networks leverage memory. While for traditional neural networks inputs and outputs are assumed to be independent, the RNN network depends on previous outputs within the sequence. RTT Networks use a backpropagation technique that is slightly different from that used by other networks, which is specific to the complete sequence of data.
RNN is known for its double data processing capability, i.e., it processes data belonging to the present and the immediate past, therefore, developing memory and awareness of context through an in-depth understanding of sequences. These algorithms can be designed to process several inputs and outputs simultaneously mapping out one-to-one, one-to-many, many-to-one, and many-to-many datasets. Notwithstanding the benefits RNN has to offer, it comes with significant hurdles in the process of development. They take a lot of time to train RNN algorithms and are not so easy to develop or implement. Because of the way the layers are arranged in the RNN model, the sequence gets rather long resulting in exploding or null weights, leading to a gradient problem. To make an RNN model work, it is necessary to stack up the sequences, and hence it is impossible to pair this model with another one.
No wonder, neural networks are fast becoming indispensable for their versatility in providing solutions for different business problems. McKinsey estimates that deep learning and neural networks have the potential to spin a market of around $3.5 trillion to $5.8 trillion in different domains. The only problem at hand should be to identify the right neural network. Hope this article has thrown some light as to how a specific network works.