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Understand the basics of Neural Networks and how they're used in handwriting and speech recognition, analyzing visual imagery, video processing, anomaly detection, and more.

Understanding the Neural Network

Chander Dhar Sharma

A Perfect Programming Paradigm

Many members within the Artificial Intelligence (AI) community believe that the neural network (NN) is one of the best representations of transferring the learning of humans to machines. If we utilize the accurate knowledge of statistics and mathematical models, we can actually train a machine to learn about the world as we perceive it. While some tend to believe that this is not true, we are constantly finding new ways to test and understand the inner-workings of neural networks. These days, we see neural networks used across an array of fields including medical, media, gaming, machines, driving, and more.

What is a Neural Network?

A neural network is the computational model of the biological neural network. It is a network made by several neurons performing computations based on the data ingested. A neural network consists of several layers – with the number of layers varying based on the project requirements. Some basic neural network models are feed-forward, multilayer-perceptron, back-propagation neural network. This list doesn’t end there – the technology has grown to numerous deep neural networks. Some of these include the Convolutional Neural Network (ConvNet or CNN - most commonly applied to analyzing visual imagery), Recurrent Neural Network (RNN - most commonly used in handwriting or speech recognition), and Bidirectional Long-Short Term Memory (LSTM - most commonly used to process images, video, speech, handwriting and anomaly detection).

The baseline of all neural network models holds the same idea of transferring information from neuron to neuron and performing arithmetical computation on each connection. The patterns neutral networks recognize are numeric and are contained in vectors. Assets such as text, video, images, and time-series need to be converted for neural networks to function. These vectors create mathematical patterns, prediction, clusters, and classifications.

Working With Your Neural Network

Deep neural networks correlate data inputs to outputs. This phenomenon is known as ‘Universal Approximator’ because it learns to approximate an unknown function f(x) = y in which x is an input and y as an output. The neural network will try to understand any correlation between x and y to find the right function or closest representation that links all x respectively to y. For example:

A Neural Network consists of 3 layers:

  1. Input Layer: This layer passes our image, text, video, audio translated data to our NN.
  2. Hidden Layer: This layer is responsible for the learning of data and maps it to a label. For example, in the above figure, the hidden layer is responsible for mapping features of DOG to link its name to DOG. In this step, there are several mathematical operations and matrix transformations working to create the correct function of the input to output
  3. Output Layer: This layer is responsible for giving the predicted output from NN.

Two Most Popular Sections of Machine Learning

Classification:

Every classification model has a dataset in which humans have transferred their knowledge and labeled the data as we understand it. This dataset is used by the model to make correlations between inputs and outputs. This is also known as Supervised Learning. Applications are:

  • Spam / fraudulent detection
  • Object detections
  • Face Detection
  • Speech to text and text to speech recognition
  • Sentiment analysis

Any data we can generate with a label that we care about can be used to train neural networks.

Clustering:

Any data that can be grouped together based on similarity without humans’ input are known as clustering algorithms. Clustering divides the population or data points and creates groups of data that are most similar to each other as compared to other groups of data points. There are two types of clustering:

  1. Hard Clustering: When the data point does belong to the group completely or not.
  2. Soft Clustering: This method of clustering creates a probability of alignment to a certain group of data points.

Applications are:

  • Recommendation systems
  • Social network analysis
  • Search results grouping
  • Anomaly detections
  • Market segmentation

When working with unlabeled information, clustering can help you title your information using the similar features of the data understood by algorithms. These two categories are known to be mostly used in our daily lives. When we see Amazon giving you suggestions of similar products, Gmail marking spam emails, or YouTube having automated subtitles, we see first-hand how utilizing clustering or classification can make our lives easier. Most importantly, both clustering and classification implement neural networks to maximize efficiency and accuracy.

Summary:

Now that we know the fundamentals of neural networks and how they work, remember that your neural network is only as good as the data you provide; the better the data, the better the performance of your model. Remember to spend 80% of your time on creating accurate, valid, and relevant data and then spend the rest of your time on the modelling of your neural network. To explore libraries that consist of ready-to-use, pre-built models, check out these sites: Sklearn, Keras, TensorFlow.

Happy Coding,

- Your Friends at Hotspex Media

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