Answer - Artificial Intelligence (AI) is everywhere. One of the popular applications of AI is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to human brain). we share few examples of machine learning that we use everyday and perhaps have no idea that they are driven by ML.
Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office day after tomorrow”.
Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.
Virtual Assistants are integrated to a variety of platforms. For example:
Traffic Predictions: We all have been using GPS navigation services. While we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of current traffic. While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are less number of cars that are equipped with GPS. Machine learning in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences.
Online Transportation Networks: When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the detours? The answer is machine learning. Jeff Schneider, the engineering lead at Uber ATC reveals in a an interview that they use ML to define price surge hours by predicting the rider demand. In the entire cycle of the services, ML is playing a major role.
Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. This is why the idea of training computers to do this job makes sense.
The video surveillance system nowadays are powered by AI that makes it possible to detect crime before they happen. They track unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches etc. The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps. And when such activities are reported and counted to be true, they help to improve the surveillance services. This happens with machine learning doing its job at the backend.
From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits. Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are nothing but the applications of ML.
A number of websites nowadays offer the option to chat with customer support representative while they are navigating within the site. However, not every website has a live executive to
answer your queries. In most of the cases, you talk to a chatbot. These bots tend to extract information from the website and present it to the customers. Meanwhile, the chatbots advances with time. They tend to understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms.
Google and other search engines use machine learning to improve the search results for you. Every time you execute a search, the algorithms at the backend keep a watch at how you respond to the results. If you open the top results and stay on the web page for long, the search engine assumes that the the results it displayed were in accordance to the query. Similarly, if you reach the second or third page of the search results but do not open any of the results, the search engine estimates that the results served did not match requirement. This way, the algorithms working at the backend improve the search results.
You shopped for a product online few days back and then you keep receiving emails for shopping suggestions. If not this, then you might have noticed that the shopping website or the app recommends you some items that somehow matches with your taste. Certainly, this refines the shopping experience but did you know that it’s machine learning doing the magic for you? On the basis of your behaviour with the website/app, past purchases, items liked or added to cart, brand preferences etc., the product recommendations are made.
Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples. For example: Paypal is using ML for protection against money laundering. The company uses a set of tools that helps them to compare millions of transactions taking place and distinguish between legitimate or illegitimate transactions taking place between the buyers and sellers.
Answer -
A Convolutional Neural Network (CNN) is a deep learning algorithm that can recognize and classify features in images for computer vision. It is a multi-layer neural network designed to analyze visual inputs and perform tasks such as image classification, segmentation and object detection, which can be useful for autonomous vehicles. CNNs can also be used for deep learning applications in healthcare, such as medical imaging.
There are two main parts to a CNN:
link to deep learning in healthcare article
Basic Convolutional Neural Network Architecture
CNN architecture is inspired by the organization and functionality of the visual cortex and designed to mimic the connectivity pattern of neurons within the human brain.
The neurons within a CNN are split into a three-dimensional structure, with each set of neurons analyzing a small region or feature of the image. In other words, each group of neurons specializes in identifying one part of the image. CNNs use the predictions from the layers to produce a final output that presents a vector of probability scores to represent the likelihood that a specific feature belongs to a certain class.
How a Convolutional Neural Network Works━The CNN layers A CNN is composed of several kinds of layers:
Popular Convolutional Neural Network Architectures
The architecture of a CNN is a key factor in determining its performance and efficiency. The way in which the layers are structured, which elements are used in each layer and how they are designed will often affect the speed and accuracy with which it can perform various tasks.
The ImageNet Challenge
The ImageNet project is a visual database designed for use in the research of visual object recognition software. The ImageNet project has more than 14 million images specifically designed for training CNN in object detection, one million of which also provide bounding boxes for the use of networks such as YOLO.
Since 2010, the project hosts an annual contest called the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). The contenders of the contest build software programs that attempt to correctly detect and classify objects and scenes within the given images. Currently, the challenge uses a cut down list of a thousand separate classes.
When the annual ILSVRC competition began, a good classification rate was 25%, the first major leap in performance was achieved by a network called AlexNet in 2012, which dropped the classification rate by 10%. Over the next years, the error rates dropped to lower percentages and finally exceeded human capabilitie
Question – Explain LSTM (Long Short Term Memory )
Answer - Long Short Term Memory is a kind of recurrent neural network. In RNN output from the last step is fed as input in the current step. LSTM was desgined by Hochreiter&Schmidhuber. It tackled the problem of long-term dependencies of RNN in which the RNN cannot predict the word stored in the long term memory but can give more accurate predictions from the recent information. As the gap length increases RNN does not give efficent performance. LSTM can by default retain the information for long period of time. It is used for processing, predicting and classifying on the basis of time series data.
LSTM has a chain structure that contains four neural networks and different memory blocks called cells.
Information is retained by the cells and the memory manipulations are done by the gates. There are three gates –
Answer - Besides an input layer and an output layer, a neural network has intermediate layers, which might also be called hidden layers. They might also be called encoders.
A shallow network has less number of hidden layers. While there are studies that a shallow network can fit any function, it will need to be really fat. That causes the number of parameters to increase a lot.
There are quite conclusive results that a deep network can fit functions better with less parameters than a shallow network.
In short, "shallow" neural networks is a term used to describe NN that usually have only one hidden layer as opposed to deep NN which have several hidden layers, often of various types.
There are papers that highlight that deep NN with the right architectures achieve better results than shallow ones that have the same computational power (e.g. number of neurons or connections).
The main explanation is that the deep models are able to extract/build better features than shallow models and to achieve this they are using the intermediate hidden layers.
Question – What is deep learning , Explain its uses and application and history.
Answer - Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. ... Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected.
Uses and Application of Deep Learning
Image colorization is the problem of adding color to black and white photographs.
Traditionally this was done by hand with human effort because it is such a difficult task.
Deep learning can be used to use the objects and their context within the photograph to color the image, much like a human operator might approach the problem.
A visual and highly impressive feat.
This capability leverages of the high quality and very large convolutional neural networks trained for ImageNet and co-opted for the problem of image colorization.
Generally the approach involves the use of very large convolutional neural networks and supervised layers that recreate the image with the addition of color.
In this task the system must synthesize sounds to match a silent video.
The system is trained using 1000 examples of video with sound of a drum stick striking different surfaces and creating different sounds. A deep learning model associates the video frames with a database of pre-rerecorded sounds in order to select a sound to play that best matches what is happening in the scene.
The system was then evaluated using a turing-test like setup where humans had to determine which video had the real or the fake (synthesized) sounds.
A very cool application of both convolutional neural networks and LSTM recurrent neural networks.
This is a task where given words, phrase or sentence in one language, automatically translate it into another language.
Automatic machine translation has been around for a long time, but deep learning is achieving top results in two specific areas:
Text translation can be performed without any preprocessing of the sequence, allowing the algorithm to learn the dependencies between words and their mapping to a new language. Stacked networks of large LSTM recurrent neural networks are used to perform this translation.
As you would expect, convolutional neural networks are used to identify images that have letters and where the letters are in the scene. Once identified, they can be turned into text, translated and the image recreated with the translated text. This is often called instant visual translation.
This task requires the classification of objects within a photograph as one of a set of previously known objects.
State-of-the-art results have been achieved on benchmark examples of this problem using very large convolutional neural networks. A breakthrough in this problem by Alex Krizhevsky et al. results on the ImageNet classification problem called AlexNet.
This is a task where given a corpus of handwriting examples, generate new handwriting for a given word or phrase.
The handwriting is provided as a sequence of coordinates used by a pen when the handwriting samples were created. From this corpus the relationship between the pen movement and the letters is learned and new examples can be generated ad hoc.
What is fascinating is that different styles can be learned and then mimicked. I would love to see this work combined with some forensic hand writing analysis expertise.
This is an interesting task, where a corpus of text is learned and from this model new text is generated, word-by-word or character-by-character.
The model is capable of learning how to spell, punctuate, form sentiences and even capture the style of the text in the corpus.
Large recurrent neural networks are used to learn the relationship between items in the sequences of input strings and then generate text. More recently LSTM recurrent neural networks are demonstrating great success on this problem using a character-based model, generating one character at time.
Automatic image captioning is the task where given an image the system must generate a caption that describes the contents of the image.
In 2014, there were an explosion of deep learning algorithms achieving very impressive results on this problem, leveraging the work from top models for object classification and object detection in photographs.
Once you can detect objects in photographs and generate labels for those objects, you can see that the next step is to turn those labels into a coherent sentence description.
This is one of those results that knocked my socks off and still does. Very impressive indeed.
Generally, the systems involve the use of very large convolutional neural networks for the object detection in the photographs and then a recurrent neural network like an LSTM to turn the labels into a coherent sentence.
This is a task where a model learns how to play a computer game based only on the pixels on the screen.
This very difficult task is the domain of deep reinforcement models and is the breakthrough that DeepMind (now part of google) is renown for achieving.
History
The history of Deep Learning can be traced back to 1943, when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the human brain.
They used a combination of algorithms and mathematics they called “threshold logic” to mimic the thought process.
Deep learning is an increasingly popular subset of machine learning. Deep learning models are built using neural networks. A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training. ... Keras is a userfriendly neural network library written in Python.
Answer - Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).
Unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. The acquisition of labeled data for a learning problem often requires a skilled human agent (e.g. to transcribe an audio segment) or a physical experiment (e.g. determining the 3D structure of a protein or determining whether there is oil at a particular location). The cost associated with the labeling process thus may render large, fully labeled training sets infeasible, whereas acquisition of unlabeled data is relatively inexpensive. In such situations, semisupervised learning can be of great practical value. Semi-supervised learning is also of theoretical interest in machine learning and as a model for human learning.
Today’s Machine Learning algorithms can be broadly classified into three categories, Supervised Learning, Unsupervised Learning and Reinforcement Learning. Casting Reinforced Learning aside, the primary two categories of Machine Learning problems are Supervised and Unsupervised Learning. The basic difference between the two is that Supervised Learning datasets have an output label associated with each tuple while Unsupervised Learning datasets do not.
The most basic disadvantage of any Supervised Learning algorithm is that the dataset has to be handlabeled either by a Machine Learning Engineer or a Data Scientist. This is a very costly process, especially when dealing with large volumes of data. The most basic disadvantage of any Unsupervised Learning is that it’s application spectrum is limited.
To counter these disadvantages, the concept of Semi-Supervised Learning was introduced. In this type of learning, the algorithm is trained upon a combination of labeled and unlabeled data. Typically, this combination will contain a very small amount of labeled data and a very large amount of unlabeled data. The basic procedure involved is that first, the programmer will cluster similar data using an unsupervised learning algorithm and then use the existing labeled data to label the rest of the unlabeled data. The typical use cases of such type of algorithm have a common property among them – The acquisition of unlabeled data is relatively cheap while labeling the said data is very expensive.
Intuitively, one may imagine the three types of learning algorithms as Supervised learning where a student is under the supervision of a teacher at both home and school, Unsupervised learning where a
student has to figure out a concept himself and Semi-Supervised learning where a teacher teaches a few concepts in class and gives questions as homework which are based on similar concepts.
A Semi-Supervised algorithm assumes the following about the data –
Google, in 2016 launched a new Semi-Supervised learning tool called Google Expander and you can learn more about it
Answer - The main idea of principal component analysis (PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent. The same is done by transforming the variables to a new set of variables, which are known as the principal components (or simply, the PCs) and are orthogonal, ordered such that the retention of variation present in the original variables decreases as we move down in the order. So, in this way, the 1st principal component retains maximum variation that was present in the original components. The principal components are the eigenvectors of a covariance matrix, and hence they are orthogonal.
Importantly, the dataset on which PCA technique is to be used must be scaled. The results are also sensitive to the relative scaling. As a layman, it is a method of summarizing data. Imagine some wine bottles on a dining table. Each wine is described by its attributes like colour, strength, age, etc. But redundancy will arise because many of them will measure related properties. So what PCA will do in this case is summarize each wine in the stock with less characteristics.
Intuitively, Principal Component Analysis can supply the user with a lower-dimensional picture, a projection or "shadow" of this object when viewed from its most informative viewpoint.
Dimensionality : It is the number of random variables in a dataset or simply the number of features, or rather more simply, the number of columns present in your dataset.
Here, v is the eigenvector and ƛ is the eigenvalue associated with it.
The (i,j)th element is the covariance between i-th and j-th variable.
Next step is to calculate the eigenvalues and eigenvectors for the covariance matrix. The same is possible because it is a square matrix. ƛ is an eigenvalue for a matrix A if it is a solution of the characteristic equation:
Pre-solved code recipes usually help in finishing your projects faster.
A recurrent neural network (RNN) is a type of artificial neural network commonly used in speech recognition and natural language processing (NLP). RNNs are designed to recognize a data's sequential characteristics and use patterns to predict the next likely scenario.
RNNs are used in deep learning and in the development of models that simulate the activity of neurons in the human brain. They are especially powerful in use cases in which context is
critical to predicting an outcome and are distinct from other types of artificial neural networks because they use feedback loops to process a sequence of data that informs the final output, which can also be a sequence of data . These feedback loops allow information to persist; the effect is often described as memory.
RNN use cases tend to be connected to language models in which knowing the next letter in a word or the next word in a sentence is predicated on the data that comes before it. A compelling experiment involves an RNN trained with the works of Shakespeare to produce Shakespeare-like prose -- successfully. Writing by RNNs is a form of computational creativity.
This simulation of human creativity is made possible by the AI’s understanding of grammar and semantics learned from its training set.
Answer - Backpropagation is the central mechanism by which neural networks learn. It is the messenger telling the network whether or not the net made a mistake when it made a prediction. ... Forward propagation is when a data instance sends its signal through a network's parameters toward the prediction at the end.
Back-propagation is the essence of neural net training. It is the method of fine-tuning the weights of a neural net based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and to make the model reliable by increasing its generalization.
Backpropagation is a short form for "backward propagation of errors." It is a standard method of training artificial neural networks. This method helps to calculate the gradient of a loss function with respects to all the weights in the network.
Types of Backpropagation Networks
Two Types of Backpropagation Networks are:
It is one kind of backpropagation network which produces a mapping of a static input for static output. It is useful to solve static classification issues like optical character recognition.
Recurrent Backpropagation:
Recurrent backpropagation is fed forward until a fixed value is achieved. After that, the error is computed and propagated backward.
The main difference between both of these methods is: that the mapping is rapid in static back-propagation while it is nonstatic in recurrent backpropagation.
Algorithm
Consider the following diagram
|
ErrorB= Actual Output – Desired Output |
|
5. Travel back from the output layer to the hidden layer to adjust the weights such that the |
error is decreased.
Why We Need Backpropagation?
Most prominent advantages of Backpropagation are:
Disadvantages of using Backpropagation
Answer -Deep reinforcement learning combines artificial neural networks with a reinforcement learning architecture that enables software-defined agents to learn the best actions possible in virtual environment in order to attain their goals.
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decisionmaking tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.
Answer :-An autoencoder is a neural network architecture capable of discovering structure within data in order to develop a compressed representation of the input. ... Because autoencoders learn how to compress the data based on attributes.Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning. Specifically, we'll design a neural network architecture such that we impose a bottleneck in the network which forces a compressed knowledge representation of the original input. If the input features were each independent of one another, this compression and subsequent reconstruction would be a very difficult task. However, if some sort of structure exists in the data (ie. correlations between input features), this structure can be learned and consequently leveraged when forcing the input through the network's bottleneck.
Answer : - Visual Geometry Group (VGG) has smaller filters than AlexNet, where each filter is of size 3 x 3 but with a lower stride of one, which effectively captures the same receptive field as a 7 x 7 filter with four strides. It has typically 16-19 layers depending on the particular VGG configuration.
VGG means Visual Geometry Group at University of Oxford. The Convolutional neural networks they developed for winning the ImageNet Challenge 2014 in localization and classification tasks are known as VGG nets.
VGG is a convolutional neural network model proposed by K. ...Zisserman from the
University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition” . The model achieves 92.7% top-5 test accuracy in ImageNet , which is a dataset of over 14 million images belonging to 1000 classes.
D : - Face Recognition Application
Answer : - Facial recognition is being used in many businesses
You’re used to unlocking your door with a key, but maybe not with your face. As strange as it sounds, our physical appearances can now verify payments, grant access and improve
existing security systems. Protecting physical and digital possessions is a universal concern which benefits everyone, unless you’re a cybercriminal or a kleptomaniac of course. Facial biometrics are gradually being applied to more industries, disrupting design, manufacturing, construction, law enforcement and healthcare. How is facial recognition software affecting these different sectors, and who are the companies and organisations behind its development?
It doesn’t take a genius to work out why businesses want payments to be easy. Online shopping and contactless cards are just two examples that demonstrate the seamlessness of postmodern purchases. With FaceTech, however, customers wouldn’t even need their cards. In 2016, MasterCard launched a new selfie pay app called MasterCard Identity Check.
Customers open the app to confirm a payment using their camera, and that’s that. Facial recognition is already used in store and at ATMs, but the next step is to do the same for online payments. Chinese ecommerce firm Alibaba and affiliate payment software Alipay are planning to apply the software to purchases made over the Internet.
As well as verifying a payment, facial biometrics can be integrated with physical devices and objects. Instead of using passcodes, mobile phones and other consumer electronics will be accessed via owners’ facial features. Apple, Samsung and Xiaomi Corp.have all installed FaceTech in their phones. This is only a small scale example, though. In future, it looks like consumers will be able to get into their cars, houses, and other secure physical locations simply by looking at them. Jaguar is already working on walking gait ID – a potential parallel to facial recognition technology. Other corporations are likely to take advantage of this, too. Innovative facial security could be especially useful for a company or organisation that handles sensitive data and needs to keep tight controls on who enters their facilities. 3. Criminal identification
If FaceTech can be used to keep unauthorised people out of facilities, surely it can be used to help put them firmly inside them. This is exactly what the US Federal Bureau of Investigation is attempting to do by using a machine learning algorithm to identify suspects from their driver’s licences. The FBI currently have a database which includes half of the national population’s faces. This is as useful as it is creepy, giving law enforcers another way of tracking criminals across the country. AI equipped cameras have also been trialled in the UK to identify those smuggling contraband into prisons.
The ability to collect and collate masses of personal data has given marketers and advertisers the chance to get closer than ever to their target markets. FaceTech could do much the same, by allowing companies to recognise certain demographics – for instance, if the customer is a male between the ages of 12 and 21, the screen might show an ad for the latest FIFA game. Grocery giant Tesco plans to install OptimEyes screens at 450 petrol stations in the UK to deliver targeted ads to customers. According to company CEO Simon Sugar, the cameras could change the face of British retail. Perhaps he’s right – but only if the cameras can correctly identify customers. Being classified as the wrong age or gender is far less amusing than having your name spelt wrong on a Starbucks cup.
Instead of recognising an individual via FaceTech, medical professionals could identify illnesses by looking at a patient’s features. This would alleviate the ongoing strain on medical centres by slashing waiting lists and streamlining the appointment process. The question is, would you really want to find out you had a serious illness from a screen? If it’s a choice between a virtual consultation or a month long wait for an appointment, then maybe so. Another application of facial biometrics within healthcare is to secure patient data by using a unique patient photo instead of passwords and usernames.
With a predicted worth of $15 billion by 2025, biometrics is an industry worth watching. It’s clear that facial biometrics are a helpful tool for finance, law enforcement, advertising and healthcare, as well as a solution to hacking and identity theft. Of course, FaceTech is by no means foolproof. Gaining access to possessions using physical traits could even be counterintuitive for security. A face, as social robots like Nadine have shown us, is easily replicated. And when it comes to public adoption, some people are reluctant to switch to contactless cards, let alone abandon them completely. For the most part, though, facial recognition technology seems to be encouraging a more seamless relationship between people, payments and possessions.