Introduction to Neural Networks and Deep Learning

Neural networks form the foundation of  Deep learning which in turn forms the base of Machine learning and Artificial Intelligence. Neural Networks are inspired by the structure and functions of the human brain. They are designed and trained over a period of time to understand patterns, remember their occurrences by receiving inputs and eventually produce appropriate outputs. One of the most common applications of Neural Networks is the Facial recognition feature on a mobile phone. Deep learning refers to the use of algorithms to process information across a set of huge different neural layers within a Neural network. The data that is processed in Deep Neural Networks can be unstructured and unsupervised as well. 

No. Deep learning and Neural Networks belong to the world of Artificial Intelligence. They are two different subfields of AI. Deep learning refers to how a set of algorithms built on complex neural networks, processes the input data across neural layers and provides the appropriate output. A neural network on the other hand refers to the visual representative structure of how these layers are connected. In other words, Neural Networks form the building blocks of Deep Learning.

Neural networks are the building blocks of Deep Learning. Data that is fed to each node in a neural layer is processed and passed on to the next layer on the neural network. The corresponding output is then analyzed for its accuracy. If the output is not satisfactory, it could be fed back to the neural network. This process of ‘training the neural network takes place until the desired output is achieved. Neural networks could be ‘trained’ over a period of time to recognize patterns accurately. In this way, when more neural layers are added to process unlabeled data, it is said to be a Deep Neural Network or DNN.

Neural networks usually are built of multiple neural layers. Each of these layers constitutes nodes that process the input data and feeds the information to the next neural layer until it reaches the output layer. 

Deep neural networks process more complex and unstructured data. This makes the number of layers between the input and output layers huge. Deep neural networks are usually built to process complex data without any supervision. They act based on previously learned or memorized incidents

Deep neural networks thrive on huge amounts of data that can be processed in multiple complex neural layers. In this way, deep neural networks learn incrementally in processing data that is unstructured or data that needs no supervision. This eliminates the need for frequent intervention during data processing between the varied neural layers. One can as well achieve near human output accuracy while using deep neural networks. An example of the implementation of the deep neural network is that of the facial recognition feature in a smartphone. 

It can process huge quantities of data, both structured and unstructured and provide near accurate results. This feature is used widely in the fields of predictive modelling, Medicine, Technology, Robotics, Entertainment etc. For example, YouTube’s personalized video recommendations, which is one of the largest in the entertainment industry. Thanks to deep learning, now YouTube can better understand and learn to suggest videos for every user.

Deep learning uses deep neural networks DNNs that are complex and have huge neural layers. These neural layers learn to predict more accurate results based on the large amounts of unstructured data that is fed into it. This type of learning by the neural network which is more complex in structure and which can deliver the output without any kind of intervention, as time progresses is called deep learning. In other words, the system learns on its own to solve problems and deliver the desired output over a period of time. One of the widely used fields of deep learning is Robotics.

DNNs are built of 3 types of neural layers to process data and provide the desired result. These are the Input layer, the Hidden layers and the Output layer. Each layer consists of several interconnected data neurons. The data from the input layer is processed and passed on to the hidden layer where more data processing takes place. The weight value associated with neurons helps in the derivation of the Activation Function which in turn helps achieve near-perfect output accuracy. This way, the neural network gets trained progressively, eventually to become self-taught about that domain and work independently without any supervision. For example, teaching and training a Humanoid to identify and remember colours, numbers, alphabets etc. During the initial stages, it would need supervision but as time progresses it would learn to do so on its own