Neural Network in Machine learning

Neural Network is also known as Artificial Neural Network or just Neural Net. It is a computational learning system that uses a network of functions to understand and translate input data of a particular type into the desired output, i.e. in another form. The artificial neural network was inspired by human biology and how neurons in the human brain function together to understand inputs like human senses.
Neural networks are one of the many tools and approaches used in machine learning algorithms. The neural network itself could be used as a piece in several machine learning algorithms to process complex data inputs into an input that computers are able to understand. They are a class of models within the general machine learning literature.
Many real-life problems are being solved using a neural network like speech and image recognition, medical diagnosis, finance or spam e-mail filtering, etc.
Reasons why every Data Science aspirant should study Neural Network

• To understand the working of a brain: it is quite large and extremely complicated and made of a material that dies if you poke it, therefore we have to use computer simulations.
• In order to understand a type of parallel computation inspired by neurons and their adaptive connections: it is a very different style from the sequential computation.
• In order to solve practical issues by making use of novel learning algorithms inspired by the human brain: learning algorithms may be extremely helpful even in case they’re not how the brain really works.

Working of a Neural Network                              
Machine learning algorithms that use neural networks, do not need to be programmed with specific rules that define what you should expect from the input. The neural network mastering algorithm rather learns from processing numerous labeled examples (i.e. data with answers) that are provided during training and using this particular answer key to learn what attributes of the input are required to construct the correct output. When a number of examples have been processed, it can easily start processing new, unseen inputs & successfully return results that are accurate. More examples and several inputs the program sees, the more precise the results usually become since the program learns with experience.
For example, imagine the “simple” problem of trying to figure out if an image has a dog. While this’s quite easy for a human to find out, it’s a lot more difficult to teach a computer to determine a dog in a picture using classical methods. Looking at the diverse possibilities of exactly how a dog might look in a photo, writing code to account for each situation is almost impossible. But using machine learning, and a lot more specifically neural networks, the system is able to utilize a generalized method to understanding the content in a picture. Using various levels of functions to decompose the image into data points and information that a laptop or computer can use, the neural network can begin to identify trends that exist across the countless, numerous examples that it processes and classify photos by their similarities.

After processing many training examples of dog images, the algorithm has a model of what components and their respective relationships, in an image are essential to think about when determining whether a dog is present in the image or not. When evaluating a new picture, it compares the data points about the new picture to the model, which is based on all previous evaluations. After that it uses some simple statistics to makes a decision whether the picture has a dog or not based on how closely it matches the model.
With this example, the levels of functions between the output and the input are what form the neural network. In training, the neural network is somewhat much more complex compared to the image above shown. The next picture captures the interaction between layers somewhat more effective but keeps in mind that you can get several variants of the interactions between nodes, and artificial neurons.

Types of Neural Network

• Recurrent Neural Network (RNN)
• Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)
RNN is a multi-layered neural network that could save data in context nodes, enabling it to learn data sequences and output a number or another sequence. In other words, it is an Artificial neural network whose connections involving neurons include things like loops. RNNs are perfectly suited for processing sequences of inputs.  For example:
If the given task is to predict the next word in the sentence “How are you…….”?

1. The RNN neurons will receive a signal pointing to the start of the sentence.
2. The model receives the word “How” as an input and a vector of the number is produced. This vector is sent back to the model to provide a memory to the network. This stage helps the model to remember what it received in the first place i.e. “How”.
3. The model then moves to the next word, it takes the word “are” and the state of the neurons are updated upon receiving each word.
4. The final stage occurs after receiving the last word “you”. The model will provide a probability of all the English words that can complete the sentence. A well-trained RNN provides suggestions like “today”, “doing”, ”dear”, etc.

Uses of RNN 🙂

1. Generate analytics reports for securities traders.
2. Detect fraudulent in credit-card transaction.
3. Power chatbots.
4. The standard uses of RNN are when the practitioners are working on time-series data or sequences. For example : audio recordings or text.

Convolutional Neural Network (CNN)
CNN is a multi-layered neural network with a distinctive architecture designed to extract increasingly advanced features of the data at each level to figure out the output. CNN’s are perfectly designed for perceptual tasks.
CNN is normally used when there is an unstructured data set (e.g., images) and the practitioners have to draw out information from it. For example:
If the task is to predict the caption of an image.

1. The CNN gets an image of a dog, this picture, in computer term, is a set of the pixel. In general, one level for the greyscale image and 3 layers for a color image.
2. During the characteristic learning (hidden layers), the model is going to identify unique features, for example, the tail of this dog, the ear, and so on.
3. When the network completely learned the way to recognize an image, it is able to offer a probability for every image it knows. The label with probably the highest probability is going to become the prediction of the neural network model.

Applications of Neural Network:
Neural networks could be applied to a wide range of problems that enable it to assess many different kinds of input, which include videos, databases, files, images, and more. Additionally, they don’t involve explicit programming to understand the content of these inputs.
Due to the generalized approach to problem-solving that neural networks provide, there is practically no limit to the aspects that this technique could be put on. Some frequent uses of neural networks nowadays, normally include image/pattern recognition, self-driving automobile trajectory prediction, facial recognition, data mining, e-mail spam filtering, healthcare analysis, and cancer research. There are lots more ways in which neural networks are used nowadays, and adoption is increasing rapidly.