With the potential to bridge the gap between the capabilities of humans and machines, Convolutional Neural Networks CNN stands superior powering the technology of computer vision. Holding a higher ranking in the field of Machine learning, CNN is based on a mathematical operation of convolution that is applied to a matrix and allows the merger of two sets of information. It uses filters to extract features from images to reduce the processing requirements without losing the features valuable for accurate prediction. CNN comprises an input layer, an output layer, and hidden layers of multiple convolutional layers, pooling layers, and fully connected layers. The core building block of the CNN is the convolutional layer that is responsible for recognizing features in pixels, the pooling layers make these features more abstract and the fully connected layers use the acquired features for prediction. By capturing the spatial features of an image, CNN is dominating computer vision with its applications like facial recognition, Optical character recognition, visual search, image classification, and driverless cars, to name a few.

Propelling the momentum of artificial vision in trendsetting technologies like autonomous vehicles, medical imaging and diagnostics, satellite imagery, robotics, and creativity tools, Image Segmentation plays an important role in Image Recognition systems displaying fine-grain information with its pixel-level understanding. This technique of computer vision converts a digital image into multiple meaningful image segments called image objects at a granular level with the help of image segmentation algorithms that require large-scale data for training. The labels are assigned to pixels and the labels categorize them as per their common features of color, intensity, or texture. With the help of labels, boundaries are specified and the objects of interest in an image are extracted for further processing. A few examples of Image segmentation applications are the separation of foreground and background of objects, locating tumors, video surveillance, and face recognition.

Referred also as text recognition, the technology of OCR uses a scanner to convert the physical documents or images containing printed, typed or handwritten text into digitized text data that can be machine-readable. The OCR software converts the scanned images into a black and white version wherein black color represents the characters and white the background. With the help of pattern recognition to recognize the characters or feature recognition to detect the lines and strokes of the characters, characters are identified and converted into ASCII codes that can be easily handled by computer systems. OCR technology has become a business necessity helping businesses to transition towards digitalization by capturing, evaluating, and maintaining sensitive data and holding its promise of monitoring efficient workflow across various sectors.

This most powerful surveillance tool of biometric security is a software that detects a captured face from video or photo by autofocusing, analyses the geometry of the face that is the distance between the facial features of eyes, nose, forehead, chin and mouth and then converts them into a string of points called faceprint or facial signature. The facial signature is compared with a database of faces to confirm the identity of the person in the photograph or video. Feeding on machine learning algorithms that detect, analyze, store the facial features as data and match them with preexisting databases, the Facial recognition system recognizes the human face through technology. With the potential to hold a strong position in the areas of commercial application from unlocking phones to public security and from improving customer experiences in various industries to marketing and advertising, the technology of Facial recognition is over the length and breadth of the digital province.

Digging into the vast amount of data to reveal the discovery of hidden patterns and extracting knowledge from the huge datasets, Data mining technology plays a crucial role in predicting future trends. Analyzing relationships and patterns in the data through processes involving data collection, cleaning of raw data, finding patterns, creating, testing models, and publishing models through data visualization, data mining helps organizations to identify gaps and errors in the business processes. Using multiple techniques like classification, clustering, regression, association rules, outer detection, sequential patterns, and prediction, data mining allows businesses to open a world of possibilities. Focussed on generating new market opportunities by discovering connections between millions of records, will the accuracy of data mining technology be questioned if incorrect information is applied for decision making?

Unveiling the hidden patterns using Big data and applying machine learning algorithms to interpret complex data, Data Science has paved the way for organizations to make powerful data-driven decisions. Unifying the techniques of statistics, mathematics, and computer science to extract, manage, maintain, store, analyze and visualize the data to create meaningful insights, Data Science stands out from the crowd as a versatile field widely used in various sectors. This study of data that transforms the raw data into valuable information through interpreting, modeling, and deployment enables organizations to visualize the data and make informed decisions around growth, optimization, and performance. With major involvement of data mining, predictive analytics, machine learning, data visualization, and programming, Data Science is evolving at a faster pace adding value to any business big or small. As technology continues to transform the world with advances in the internet, social media, and ever-increasing data, can data science beat human intelligence while curating the input data?

Using the blend of technologies similar to Artificial Intelligence like Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, etc, These decision support systems outshines its ability to analyze patterns, simplify processes by examining large amounts of volumetric data, and spot business opportunities. With the help of computerized models using self-learning technologies like data mining, pattern recognition, and natural language processing, Cognitive computing synthesizes the data fed to machine learning algorithms from different information sources to suggest the best possible answers. Pitching on the grounds of learning, reasoning, and self-correction and assisting humans to make smarter decisions, Cognitive Computing applications include speech recognition, sentiment analysis, face detection, risk assessment, and fraud detection.

Mirroring a structure similar to the human brain and mimicking the exceptional functioning of analyzing and processing the information, Artificial Neural Networks are backing up the machines with extra dollops of imagination and reasoning overshadowing the human efforts. Resembling the behavior of interconnected neurons, these sets of algorithms replicate a similar fashion through artificial neurons called nodes, arranged in layers and connected. Designed by cybernetics to behave in a human-like manner, Artificial Neural Networks have an input layer - that accepts inputs in different formats, the hidden or the processing layers - that reside between the input and the output layers and perform the mathematical computations to transform the input data into output and the output layer - that conveys the results of the given inputs. Performing certain specific tasks of clustering, classification, and pattern recognition, these main components of machine learning are advancing the lifestyle with their sharpened applications in the field of Artificial intelligence, including face recognition, speech recognition, real-time translation, google photos, autonomous cars, and many more. With the hidden layers handling most of the tasks of Artificial Neural Networks, are we likely to see time complexity as a major constraint while solving complex problems?

Being one of the leading high-level neural network APIs, Keras that is defined for human beings and not machines is written in python and supports multiple backends for neural network computation engines like Tensorflow, CNTK, Theano, MXNet, and PlaidML. A very powerful and easy-to-use free open source python library, Keras develops and evaluates deep learning models by wrapping around the functionalities of other ML and DL libraries like Theano and Tensorflow defining and training neural network models in just a few lines of code. Following the best practices of reducing cognitive load, Keras allows the users to productize deep models on smartphones, allows the usage of distributed training of deep learning models on clusters of GPU and TPU, and acts as an interface for the Tensorflow library making it easier to navigate.