The severe cyclonic storm Nisarga approached the Maharashtra coast around Alibagh in Raigadh with “a sustained wind speed of 100-110 kmph” on June 3, 2020. Then it made landfall at Alibagh at around noontime. Landfall simply means that the storm, after having intensified over the ocean, has moved on to land.
Though the storm mellowed down in intensity as it approached the Maharashtra coast, government bodies took all precautions and evacuation work was done in advance on the basis of forecasts done by meteorologists and scientists.
To save lives and property, it is imperative to predict cyclones and the intensity with which they will strike. Deep Learning, a branch of artificial Intelligence, is helping scientists make breakthroughs in the science of forecasting cyclones.
Image Source: outlookindia.com
Existing Storm Forecast Models
Most conventional dynamical models make accurate short term predictions but they are computationally demanding and “current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing”, says a report.
A tropical cyclone forecast involves the prediction of several interrelated features like track, intensity, rainfall, storm surge etc. The development of current hurricane and cyclone forecasts have advanced over the years but they are largely statistical in nature. The main limitation of this method is the complexity and non-linearity of atmospheric systems.
Deep Learning Models
Recurrent Neural Networks in deep learning models have been, of late, used to study increasingly complicated systems instead of the traditional methods of forecasting because they promise more accuracy. RNNs are a class of artificial neural networks where the modification of weights allows the model to learn intricate dynamic temporal behaviours, says another report.
An RNN with the capability of modelling complex non-linear temporal relationships of a hurricane or a cyclone could increase the accuracy of predicting future cyclonic path forecasts.
Machine Learning
Generally speaking, there are two methods or approaches to detecting extreme weather events like tropical cyclones – the data driven method which includes machine learning and the model driven approach which includes numerical simulation.
“The model-driven approach has the limitation that the prediction error increases with lead time because numerical models are inherently dependent on initial values. On the other hand, machine learning, as a data-driven approach, requires a large amount of high-quality training data,” says areport.
High quality data is easy to procure given the large amounts of data generated from weather stations on a daily basis the world over. So the machine learning method is easier to work and generate results from.
Conclusion
So what was difficult to do, that is find suitable metrics to study and detect the path of tropical cyclones earlier, has now become easier to do and scientists have been able to achieve accuracy in their predictions through the use of neural networks and artificial intelligence in general. For more on the subject, do read our blog here and here. Dexlab Analytics is a premier Deep Learning training institute in Delhi.
Artificial intelligence (AI) and machine learning are today thought to be one of the biggest innovations since the microchip. With the advancement of the science of neural networks, scientists are making extraordinary breakthroughs in machine learning through what is termed as deep learning. These sciences are making life easier and more streamlined for us in more ways than one. Here are a few examples.
1. Smart Gaming
Artificial Intelligence and Machine Learning are used in smart gaming techniques, especially in games that primarily require the use of mental abilities like chess. Google DeepMind’s AlphaGo learnt to play chess, and defeat champions like Lee Sedol (in 2016) by not only studying the moves of masters but by learning how to play the game by practising against itself innumerable times.
2. Automated Transportation
When we fly in an airplane, we experience automated transportation in the sense that a human pilot is only flying the plane for a couple of minutes during take-off and landing. The rest of the flight is maneuvered by a Flight Management System, a synchronization of GPS, motion sensors and computer systems that track flight position. Google Maps has already revolutionized local transport by studying coordinates from smart phones to determine how fast or slow a vehicle is moving and therefore how much traffic there is on a given road at any point of time.
3. Dangerous Jobs
AI technology powered robots are taking over dangerous jobs like bomb disposal and welding. In bomb disposal, today, robots need to be controlled by humans. But scientists believe there will soon come a time when these tasks would be completed by robots themselves. This technology has already saved hundreds of lives. In the field of welding, a hazardous job which entails working in high levels of noise and heat in a toxic environment, robots are helping weld with greater accuracy.
4. Environmental Protection
Machine Learning and artificial intelligence run on big data, large caches of data and mind boggling statistics generated by computer systems. When put to use in the field of environmental protection, these technologies could be used to extract actionable solutions to untenable problems like environmental degradation. For instance, “IBM’s Green Horizon Project studies and analyzes environmental data from thousands of sensors and sources to produce accurate, evolving weather and pollution forecasts.”
5. Robots as Friends
A company in Japan has invented what it calls a robot companion named Pepper who can understand and feel emotions and empathy. Introduced in 2014, Pepper went on sale in 2015 and all the 1000 units were sold off immediately. “The robot was programmed to read human emotions, develop its own, and help its human friends stay happy,” a report says. Robots could also assist the aged in becoming independent and take care of themselves, says a computer scientist at Washington State University.
6. Health Care
Hospitals across the world are mulling over the adoption of AI and ML to treat patients so there are reduced instances of hospital related accidents and spread of diseases like sepsis. AI’s predictive models are helping in the fight against genetic diseases and heart ailments. Also, Deep Learning models which “quickly provide real-time insights and…are helping healthcare professionals diagnose patients faster and more accurately, develop innovative new drugs and treatments, reduce medical and diagnostic errors, predict adverse reactions, and lower the costs of healthcare for providers and patients.”
7. Digital Media
Machine learning has revolutionized the entertainment industry and technology has already found buyers in streaming services such as Netflix, Amazon Prime, Spotify, and Google Play. “ML algorithms are…making use of the almost endless stream of data about consumers’ viewing habits, helping streaming services offer more useful recommendations.”
These technologies will assist with the production of media too. NLP (Natural Language Processing) algorithms help write and compose trending news stories, thus cutting on production time. Moreover, a new MIT-developed AI model named Shelley “helps users write horror stories through deep learning algorithms and a bank of user-generated fiction.”
8. Home Security and Smart Stores
AI-integrated cameras and alarm systems are taking the home security world by storm. The cutting-edge systems “use facial recognition software and machine learning to build a catalog of your home’s frequent visitors, allowing these systems to detect uninvited guests in an instant.” Brick and Mortar stores are likely to adopt facial recognition for payments by shoppers. Biometric capabilities are largely being adopted to enhance the shopping experience.
Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. While classic machine-learning algorithms solved many problems, they are poor at dealing with soft data such as images, video, sound files, and unstructured text.
Deep-learning algorithms solve the same problem using deep neural networks, a type of software architecture inspired by the human brain (though neural networks are different from biological neurons). Neural Networks are inspired by our understanding of the biology of our brains – all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.
The data is inputted into the first layer of the neural network. In the first layer individual neurons pass the data to a second layer. The second layer of neurons does its task, and so on, until the final layer and the final output is produced. Each neuron assigns a weighting to its input — how correct or incorrect it is relative to the task being performed. The final output is then determined by the total of those weightings.
Deep Learning Use Case Examples
Robotics
Many of the recent developments in robotics have been driven by advances in AI and deep learning. Developments in AI mean we can expect the robots of the future to increasingly be used as human assistants. They will not only be used to understand and answer questions, as some are used today. They will also be able to act on voice commands and gestures, even anticipate a worker’s next move. Today, collaborative robots already work alongside humans, with humans and robots each performing separate tasks that are best suited to their strengths.
Agriculture
AI has the potential to revolutionize farming. Today, deep learning enables farmers to deploy equipment that can see and differentiate between crop plants and weeds. This capability allows weeding machines to selectively spray herbicides on weeds and leave other plants untouched. Farming machines that use deep learning–enabled computer vision can even optimize individual plants in a field by selectively spraying herbicides, fertilizers, fungicides and insecticides.
Medical Imaging and Healthcare
Deep learning has been particularly effective in medical imaging, due to the availability of high-quality data and the ability of convolutional neural networks to classify images. Several vendors have already received FDA approval for deep learning algorithms for diagnostic purposes, including image analysis for oncology and retina diseases. Deep learning is also making significant inroads into improving healthcare quality by predicting medical events from electronic health record data. Earlier this year, computer scientists at the Massachusetts Institute of Technology (MIT) used deep learning to create a new computer program for detecting breast cancer.
Here are some basic techniques that allow deep learning to solve a variety of problems.
Fully Connected Neural Networks
Fully Connected Feed forward Neural Networks are the standard network architecture used in most basic neural network applications.
In a fully connected layer each neuron is connected to every neuron in the previous layer, and each connection has its own weight. This is a totally general purpose connection pattern and makes no assumptions about the features in the data. It’s also very expensive in terms of memory (weights) and computation (connections).
Each neuron in a neural network contains an activation function that changes the output of a neuron given its input. These activation functions are:
Linear function: – it is a straight line that essentially multiplies the input by a constant value.
Sigmoid function: – it is an S-shaped curve ranging from 0 to 1.
Hyperbolic tangent (tanH) function: – it is an S-shaped curve ranging from -1 to +1
Rectified linear unit (ReLU) function: – it is a piecewise function that outputs a 0 if the input is less than a certain value or linear multiple if the input is greater than a certain value.
Each type of activation function has pros and cons, so we use them in various layers in a deep neural network based on the problem. Non-linearity is what allows deep neural networks to model complex functions.
Convolutional Neural Networks
Convolutional Neural Networks (CNN) is a type of deep neural network architecture designed for specific tasks like image classification. CNNs were inspired by the organization of neurons in the visual cortex of the animal brain. As a result, they provide some very interesting features that are useful for processing certain types of data like images, audio and video.
Mainly three main types of layers are used to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks). We will stack these layers to form a full ConvNet architecture. A simple ConvNet for CIFAR-10 classification could have the above architecture [INPUT – CONV – RELU – POOL – FC].
INPUT [32x32x3] will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as [32x32x12] if we decided to use 12 filters.
RELU layer will apply an elementwise activation function, such as the max(0,x)max(0,x)thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]).
POOL layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12].
FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1x10], where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
In this way, ConvNets transform the original image layer by layer from the original pixel values to the final class scores. Note that some layers contain parameters and others don’t. In particular, the CONV/FC layers perform transformations that are a function of not only the activations in the input volume, but also of the parameters (the weights and biases of the neurons). On the other hand, the RELU/POOL layers will implement a fixed function. The parameters in the CONV/FC layers will be trained with gradient descent so that the class scores that the ConvNet computes are consistent with the labels in the training set for each image.
Convolution is a technique that allows us to extract visual features from an image in small chunks. Each neuron in a convolution layer is responsible for a small cluster of neurons in the receding layer. CNNs work well for a variety of tasks including image recognition, image processing, image segmentation, video analysis, and natural language processing.
Recurrent Neural Network
The recurrent neural network (RNN), unlike feed forward neural networks, can operate effectively on sequences of data with variable input length.
The idea behind RNNs is to make use of sequential information. In a traditional neural network we assume that all inputs (and outputs) are independent of each other. But for many tasks that is a very bad idea. If you want to predict the next word in a sentence you better know which words came before it. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output being depended on the previous computations. Another way to think about RNNs is that they have a “memory” which captures information about what has been calculated so far. This is essentially like giving a neural network a short-term memory. This feature makes RNNs very effective for working with sequences of data that occur over time, For example, the time-series data, like changes in stock prices, a sequence of characters, like a stream of characters being typed into a mobile phone.
The two variants on the basic RNN architecture that help solve a common problem with training RNNs are Gated RNNs, and Long Short-Term Memory RNNs (LSTMs). Both of these variants use a form of memory to help make predictions in sequences over time. The main difference between a Gated RNN and an LSTM is that the Gated RNN has two gates to control its memory: an Update gate and a Reset gate, while an LSTM has three gates: an Input gate, an Output gate, and a Forget gate.
RNNs work well for applications that involve a sequence of data that change over time. These applications include natural language processing, speech recognition, language translation, image captioning and conversation modeling.
Conclusion
So this article was about various Deep Learning techniques. Each technique is useful in its own way and is put to practical use in various applications daily. Although deep learning is currently the most advanced artificial intelligence technique, it is not the AI industry’s final destination. The evolution of deep learning and neural networks might give us totally new architectures. Which is why more and more institutes are offering courses on AI and Deep Learning across the world and in India as well. One of the best and most competent artificial intelligence certification in Delhi NCR is DexLab Analytics. It offers an array of courses worth exploring.
Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. With computer vision, our computer can extract, analyze and understand useful information from an individual image or a sequence of images. Computer vision is a field of artificial intelligence that works on enabling computers to see, identify and process images in the same way that human vision does, and then provide the appropriate output.
Initially computer vision only worked in limited capacity but due to advance innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.
The Contribution of Deep Learning in Computer Vision
While there are still significant obstacles in the path of human-quality computer vision, Deep Learning systems have made significant progress in dealing with some of the relevant sub-tasks. The reason for this success is partly based on the additional responsibility assigned to deep learning systems.
It is reasonable to say that the biggest difference with deep learning systems is that they no longer need to be programmed to specifically look for features. Rather than searching for specific features by way of a carefully programmed algorithm, the neural networks inside deep learning systems are trained. For example, if cars in an image keep being misclassified as motorcycles then you don’t fine-tune parameters or re-write the algorithm. Instead, you continue training until the system gets it right.
With the increased computational power offered by modern-day deep learning systems, there is steady and noticeable progress towards the point where a computer will be able to recognize and react to everything that it sees.
Application of Computer Vision
The field of Computer Vision is too expansive to cover in depth. The techniques of computer vision can help a computer to extract, analyze, and understand useful information from a single or a sequence of images. There are many advanced techniques like style transfer, colorization, action recognition, 3D objects, human pose estimation, and much more but in this article we will only focus on the commonly used techniques of computer vision. These techniques are: –
Image Classification
Image Classification with Localization
Object Segmentation
Object Detection
So in this article we will go through all the above techniques of computer vision and we will also see how deep learning is used for the various techniques of computer vision in detail. To avoid confusion we will distribute this article in a series of multiple blogs. In first blog we will see the first technique of computer vision which is Image Classification and we will also explore that how deep learning is used in Image Classification.
Image Classification
Image classification is the process of predicting a specific class, or label, for something that is defined by a set of data points. Image classification is a subset of the classification problem, where an entire image is assigned a label. Perhaps a picture will be classified as a daytime or nighttime shot. Or, in a similar way, images of cars and motorcycles will be automatically placed into their own groups.
There are countless categories, or classes, in which a specific image can be classified. Consider a manual process where images are compared and similar ones are grouped according to like-characteristics, but without necessarily knowing in advance what you are looking for. Obviously, this is an onerous task. To make it even more so, assume that the set of images numbers in the hundreds of thousands. It becomes readily apparent that an automatic system is needed in order to do this quickly and efficiently.
There are many image classification tasks that involve photographs of objects. Two popular examples include the CIFAR-10 and CIFAR-100 datasets that have photographs to be classified into 10 and 100 classes respectively.
Deep learning for Image Classification
The deep learning architecture for image classification generally includes convolutional layers, making it a convolutional neural network (CNN). A typical use case for CNNs is where you feed the network images and the network classifies the data. CNNs tend to start with an input “scanner” which isn’t intended to parse all the training data at once. For example, to input an image of 100 x 100 pixels, you wouldn’t want a layer with 10,000 nodes.
Rather, you create a scanning input layer of say 10 x 10 which you feed the first 10 x 10 pixels of the image. Once you passed that input, you feed it the next 10 x 10 pixels by moving the scanner one pixel to the right. This technique is known as sliding windows.
Following Layers are used to build Convolutional Neural Networks:
INPUT [32x32x3] will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.
CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as [32x32x12] if we decided to use 12 filters.
RELU layer will apply an element wise activation function, such as the max(0,x)max(0,x)thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]).
POOL layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12].
FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1x10], where each of the 10 numbers correspond to a class score, such as among the 10 categories of CIFAR-10. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
Output of the Model History
In this way, ConvNets transform the original image layer by layer from the original pixel values to the final class scores. Note that some layers contain parameters and other don’t. In particular, the CONV/FC layers perform transformations that are a function of not only the activations in the input volume, but also of the parameters (the weights and biases of the neurons). On the other hand, the RELU/POOL layers will implement a fixed function. The parameters in the CONV/FC layers will be trained with gradient descent so that the class scores that the ConvNet computes are consistent with the labels in the training set for each image.
Conclusion
The above content focuses on image classification only and the architecture of deep learning used for it. But there is more to computer vision than just classification task. The detection, segmentation and localization of classified objects are equally important. We will see these in next blog.
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Mode is the most typical or prevalent value, and at times, represents the true characteristics of the distribution as a measure of central tendency.
Application:
The numbers of the telephone calls received in 245 successive one minute intervals at an exchange are shown in the following frequency distribution table:
No of Calls
Frequency
0
14
1
21
2
25
3
43
4
51
5
40
6
51
7
51
8
39
9
12
Total
245
[Note: Here we assume total=245 when we calculate Mean from the same data]
Evaluate the Mode from the data.
Calculate Mode in R:
Calculate mode in R from the data, i.e. the most frequent number in the data is 51.
The number 51 repeats itself in 5, 7 and 8 phone calls respectively.
Calculate Median in Python:
First, make a data frame for the data.
Now, calculate the mode from the data frame.
Calculate mode in Python from the data, i.e. the most frequent number in the data is 51.
The number 51 repeats itself in 5, 7 and 8 phone calls respectively.
Mode is used in business, because it is most likely to occur. Meteorological forecasts are, in fact, based on mode calculations.
The modal wage of a group of the workers is the wages which the largest numbers of workers receive, and as such, this wage may be considered as the representative wage of the group.
In this particular data set we use the mode function to know the occurrence of the highest number of phone calls.
It will thus, help the Telephone Exchange to analyze their data flawlessly.
Note – As you have already gone through this post, now, if you are interested to know about the Harmonic Mean, you can check our post on theAPPLICATION OF HARMONIC MEAN USING R AND PYTHON.
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To define Deep Learning, it can be summed up as a machine learning technique used to teach computers all those things which comes to humans quite instinctively. This is a sub-classification of the umbrella term Machine Learning and is based on artificial neural networks.
The technology of driver-less cars, of computers with the knowledge of lampposts and trees as non-living entities and with their discretion of differentiating between a pedestrian and a lamppost, all are being developed from Deep Learning. Besides, the voice assistant you find nowadays, that comes with the smartphones, tablets, TVs and hands-free electronic gadgets, everything is matured by Deep Learning.
Deep Learning is an immensely effective technique with huge prospective. Thus, Deep Learning is a highly regarded technology and more and more people are looking forward to finding their career in it.
Deep Learning: The Path of Success
Among the ever-changing technologies, Deep Learning has its path paved to stand strong in the long run. Now, this is possible primarily because of the high accuracy levels that it has reached.
Pin-pointed Accuracy
With the convincing accuracy levels reached, Deep Learning is believed to be steadfast in situations which involves high risks and which calls for the least margin of errors. For example – driver-less cars.
Extensive Library
If you aim Deep Learning for computer vision with Python, you should be ready with enormous information that it can go through and process quite effortlessly, hence, putting forth an all-inclusive library to be used in real-time. For instance, millions of images, days of video and data should be fed to the system going forward to develop the technology of the driverless car.
Powerful Computing
If we talk about the power that Deep Learning needs, it is astonishingly unreal, the amount of power that this technology expects to perform in its optimum. None other than immensely powerful GPUs are used to get the best results.
As Deep Learning is quite a new thing, unknown in most of its dimensions, here are a few of the fields which have already absorbed or are trying to infuse Deep Learning in constructively.
Automobiles – As we have already mentioned that the automobile industry has already taken Deep Learning quite seriously and is effective moving in the direction, where, soon we would witness cars without any human drivers.
Defence and Aerospace – Deep learning is constantly taken into account when determining the objects that the satellites bring us. Via Deep Learning we can be sure of the areas/objects in the space. Furthermore, whether a particular zone is fit for the soldiers or not, can also be easily determined by Deep Learning.
Pharmacy – Deep Learning is highly significant even in modern medical science. For example, this technology is used to detect cancerous cells.
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Over the last couple of years, no other part of data science has made progress like Deep Learning has. From self-driving cars to scientific research, deep learning has been the game-changer in almost every innovation made. Day by day, its influence on our way of life is getting stronger!
Deep learning is a vast and complex field having numerous sections. It takes several months of consistent effort to master the basics before delving deeper into the subject. And a thorough understanding of fundamental concepts of calculus and algebra is essential for getting started with deep learning education.
This article discusses the basics of deep learning for newbies.
Machine Leaning Basics
One must be thorough with the basics of machine learning, which includes reinforced, supervised and unsupervised learning, before starting off your deep learning education. Statistical techniques, like linear regression and logistic regression, are greatly needed in this field.
Deep Learning Introduction
First of all, you need to know the various deep learning frameworks. Deep Learning algorithms draw inspiration from artificial neural networks. While there are many free online courses, a professional course from a reputed deep learning training institute is the ideal starting point for beginners. Additionally, you can follow relevant eBooks, like the Neural Networks and Deep Learning PDF by Michael Nielson.
Understanding Neural Networks
Neural networks have a layered outlay and their functioning resembles the neurons of human brain. Neural networks are made up of an input layer, an output layer and a hidden layer, and produce output after receiving an input – just like human mind works. You need to be familiar with techniques of handling and pre-processing data, regularization methods, data augmentation, hyperparameter technique, etc. These functions of artificial neural network are widely employed in deep learning, helping tasks like image and speech recognition.
Convolution Neural Network Basics
An important role in deep learning is played by Convolution Neural Network, which is profusely used in object detections, facial recognition, image recognition and classifications, etc. In deep learning, CNN models work by passing the input image through a string of convoluted layers before classifying it with probabilistic values.
Knowing Sequence Models
If you want to go deeper into deep learning, it is crucial to know how to develop models such as Recurrent Neural Networks (RNNs), and make use of alternatives, like Long Short Term Memory (LSTMs) and Gated Recurrent Unit (GRU). Working with audio applications and music synthesis becomes easier when you understand these models.
Unsupervised Deep Learning
A complex topic, but learning it helps crack otherwise unsolvable problems. Problems that remain unclear even after applying supervised learning methods like biasing can be explained with unsupervised deep learning. One popular algorithm of unsupervised deep learning is Autoencoder neural network.
Know Natural Language Processing
NPL deals with understanding human speech and has many benefits. With the help of computational algorithms, NPL analyzes and represents human language. It can also be employed in dialogue generation, machine translation, etc.
Deep Reinforcement Learning
Deep reinforcement learning has immense potential in deep learning. Reinforcement learning algorithms united with deep learning created AlphaGo, which was successful in defeating the strongest Go players!
Theory isn’t enough; you must implement your deep learning knowledge. And to do that properly, you must be able to use Python.
Interestingly, the last decade has witnessed some phenomenal leaps in the technology domain, notably in AI. As compared to the early days of speech recognition, smartphones we use today have transformed themselves entirely; they are more like our virtual assistants: the reason being quantum advancements in Deep Learning and Machine Learning.
The craze surrounding Deep Learning continues to grow. In this blog, we will evaluate whether the trend is going to stay for long and influence the future of AI or is it just a hype which will soon disappear into thin air.
The Hype Cycle
In simple terms, a ‘hype cycle’ refers to a curve that escalates to a peak at the start, then drops sharply and gets into a plateau. Perhaps not surprisingly, Deep Learning has been a part of diverse ‘hype cycles’. Currently, if you follow the tech market statistics, you will find that DL is yet to reach the plateau of productivity, where it would be largely accepted by the public and leveraged for daily work. As of now, DL hasn’t reached that stage, that’s why we can’t confirm whether the technology is going to stay or dwindle away.
From a DL Enthusiast’s Perspective
Following present-day market trends, we can say that virtual reality and augmented reality are close to the plateau of productivity. Years back, when these advanced technologies were launched they exhibited the same hype as Deep Learning. However, with time and development, they are now on the verge of becoming main-stream and we expect the same for our new friend Deep Learning.
In fact, if we see from the perspective of a DL enthusiast, we will discover that DL has been more than just a hype – it has actually done wonders in diverse fields – from playing games to self-driven cars, DL technology is used in almost everything ‘technological’.
In 2016, an AI-driven Go-playing system won over Korean champion Lee Sodol. Not only did it defeated the opponent but also excelled to become the best of Go, acing the strategy game. Tesla too leverages the Deep Learning technology for their self-driving cars. Next, Amazon’s Alexa is heard to use the divine technology of DL to make love-life predictions. It will suggest you what went wrong between you and your consort.
Put simply, Deep Learning is the revolutionary new-age technology. Organizations are investing funds and resources all over the world. Considering the current growth rate, DL technology is soon expected to break into the mainstream industry replacing all conventional modes of technology and communications.
Outlook
With AI being the topic of discussion in almost every industry verticals, DL has been gaining popularity. No wonder, it has proved tremendously beneficial in the past but the future expectations are pretty high as well. In this case, we have to wait and observe how Deep Learning manages to fulfil industry expectations and stay inside the ring!
Delhi is home to a bevy of reputable Deep learning training institutes. Browse over their course details and pan out the best from the lot.
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Known to all, the present-day retail industry is obsessed with all-things-data. With Amazon leading the show, many retailers are found implementing a data-driven mindset throughout the organization. Accurate predictions are significant for retailers, and AI is good in churning out value from retail datasets. Better accuracy in forecasts has resulted in widespread positive impacts.
Below, we’ve chalked down how deep learning, a subset of machine learning addresses retail forecasting issues. It is a prime key to solve most common retail prediction challenges – and here is how:
Deep learning helps in developing advanced, customized forecasting models that are based on unstructured retail data sets. Relying on Graphic Processing Units, it helps process complex tasks – though GPUs area applied only twice during the process; once during training the model and then at the time of inference when the model is applied to new data sets.
Deep learning-inspired solutions help discover complex patterns in data sets. In case of big retailers, the impressive technology of Deep Learning supports multiple SKUs all at the same time, which proves productive on the part of models as they get to learn from the similarities and differences to seek correlations for promotion or competition. For example, winter gloves sell well when puffer jackets are already winning the market, indicating sales. On top of that, deep learning can also ascertain whether an item was not sold or was simply out of stock. It also possesses the ability to determine the larger problem as to why the product was not being sold or marketed.
For a ‘cold start’, historical data is limited but deep learning has the power to leverage other attributes and boost the forecasting. The technology works by picking similar SKUs and implement that information to bootstrap forecasting process.
Nonetheless, there exists an array of challenges associated with Deep Learning technology. The very development of high-end AI applications is at a nascent stage; it is yet to become a fully functional engineering practice.
A larger chunk of successful AI implementation depends on the expertise and experience of the breed of data scientists involved. Handpicking a qualified data scientist in today’s world is the real ordeal. Being fluent in the nuances of deep learning imposes extra challenges. Moreover, apart from being labor intensive in terms of feature engineering and data cleaning, the entire methodology of developing neural network models all manually is difficult and downright challenging. It may even take a substantial amount of time to learn the tricks and scrounge through numerous computational resources and experiments performed by data scientists. All this makes the hunt down for skilled data scientists even more difficult.
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