Big data is all around us, be it generated by our news feed or the photos we upload on social media. Data is the new oil and therefore, today, more than ever before, there is a need to study, organize and extract knowledgeable and actionable insights from it. For this, the role of data scientists has become even more crucial to our world. In this article we discuss the various skills, both technical and non-technical a data scientist needs to master to acquire a standing in a competitive market.
Technical Skills
Python and R
Knowledge of these two is imperative for a data scientist to operate. Though organisations might want knowledge of only one of the two programming languages, it is beneficial to know both. Python is becoming more popular with most organisations. Machine Learning using Python is taking the computing world by storm.
GitHub
Git and GitHub are tools for developers and data scientists which greatly help in managing various versions of the software. “They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.”
Preparing for Production
Historically, the data scientist was supposed to work in the domain of machine learning. But now data science projects are being more often developed for production systems. “At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.”
Cloud
Cloud software rules the roost when it comes to data science and machine learning. Keeping your data on cloud vendors like AWS, Microsoft Azure or Google Cloud makes it easily accessible from remote areas and helps quickly set up a machine learning environment. This is not a mandatory skill to have but it is beneficial to be up to date with this very crucial aspect of computing.
Deep Learning
Deep learning, a branch of machine learning, tailored for specific problem domains like image recognition and NLP, is an added advantage and a big plus point to your resume. Even if the data scientist has a broad knowledge of deep learning, “experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future”. Deep learning training institutes are coming up across the globe, and more so in India.
Math and Statistics
Knowledge of various machine learning techniques, with an emphasis on mathematics and algebra, is integral to being a data scientist. A fundamental grounding in the mathematical foundation for machine learning is critical to a career in data science, especially to avoid “guessing at hyperparameter values when tuning algorithms”. Knowledge of Calculus linear algebra, statistics and probability theory is also imperative.
SQL
Structured Query Language (SQL) is the most widely used database language and a knowledge of the same helps data scientist in acquiring data, especially in cases when a data science project comes in from an enterprise relational database. “In addition, using R packages like sqldf is a great way to query data in a data frame using SQL,” says a report.
AutoML
Data Scientists should have grounding in AutoML tools to give them leverage when it comes to expanding the capabilities of a resource, which could be in short supply. This could deliver positive results for a small team working with limited resources.
Data Visualization
Data visualization is the first step to data storytelling. It helps showcase the brilliance of a data scientist by graphically depicting his or her findings from data sets. This skill is crucial to the success of a data science project. It explains the findings of a project to stakeholders in a visually attractive and non-technical manner.
Non-Technical Skills
Ability to solve business problems
It is of vital importance for a data scientist to have the ability to study business problems in an organization and translate those to actionable data-driven solutions. Knowledge of technical areas like programming and coding is not enough. A data scientist must have a solid foundation in knowledge of organizational problems and workings.
Effective business communication
A data scientist needs to have persuasive and effective communication skills so he or she can face probing stakeholders and meet challenges when it comes to communicating the results of data findings. Soft skills must be developed and inter personal skills must be honed to make you a creatively competent data scientist, something that will set you apart from your peers.
Agility
Data scientist need to be able to work with Agile methodology in that they should be able to work based on the Scrum method. It improves teamwork and helps all members of the team remain in the loop as does the client. Collaboration with team members towards the sustainable growth of an organization is of utmost importance.
Experimentation
The importance of experimentation cannot be stressed enough in the field of data science. A data scientist must have a penchant for seeking out new data sets and practise robustly with previously unknown data sets. Consider this your pet project and practise on what you are passionate about like sports.
This article, the second part of a series, is on the application of artificial intelligence in the field of healthcare. The first part of the series mapped the applications of AI and deep learning in agriculture, with an emphasis on precision farming.
AI has been taking the world by storm and its most crucial application is to the two fields mentioned above. Its application to the field of healthcare is slowly expanding, covering fields of practice such as radiology and oncology.
Stroke Prevention
In a study published in Circulation, a researcher from the British Heart Foundation revealed that his team had trained an artificial intelligence model to read MRI scans and detect compromised blood flow to and from the heart.
And an organisation called the Combio Health Care developed a clinical support system to assist doctors in detecting the risk of strokes in incoming patients.
Brain-Computer Interfaces
Neurological conditions or trauma to the nervous system can adversely affect a patient’s motor sensibilities and his or her ability to meaningfully communicate with his or her environment, including the people around.
AI powered Brain-Computer Interfaces can restore these fundamental experiences. This technology can improve lives drastically for the estimated 5,00,000 people affected by spinal injuries annually the world over and also help out patients affected by ALS, strokes or locked-in syndrome.
Radiology
Radiological imagery obtained from x-rays or CT scanners put radiologists in danger of contracting infection through tissue samples which come in through biopsies. AI is set to assist the next generation of radiologists to completely do away with the need for tissue samples, experts predict.
A report says “(a)rtificial intelligence is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.”
Cancer Treatment
One reason why AI, has made immense advancements in the field of medical oncology is the vast amount of data generated during cancer treatment.
Machine learning algorithms and their ability to study and synthesize highly complex datasets may be able to shed light on new options for targeting therapies to a patient’s unique genetic profile.
Developing countries
Most developing counties suffer from health care systems working on shoe-string budgets with a lack of critical healthcare providers and technicians. AI-powered machines can help plug the deficit of expert professionals.
For example, AI imaging tools can study chest x-rays for signs of diseases like tuberculosis, with an impressive rate of accuracy comparable to human beings. However, algorithm developers must bear in mind the fact that “(t)he course of a disease and population affected by the disease may look very different in India than in the US, for example,” the report says. So an algorithm based on a single ethnic populace might not work for another.
In the first series of this article we have seen what is computer vision and a brief review of its applications. You can read the first part of this article here. We have also seen the contribution of deep learning in computer vision. Especially we focused on Image Classification and deep learning architecture which is used in Image Classification. In this series we will focus on other applications including Image Localization, Object Detection and Image Segmentation. We will also walk through the required deep learning architecture used for above applications.
Image classification with Localization
Similar to classification, localization finds the location of a single object inside the image. Localization can be used for lots of useful real-life problems. For example, smart cropping (knowing where to crop images based on where the object is located), or even regular object extraction for further processing using different techniques. It can be combined with classification for not only locating the object but categorizing it into one of many possible categories.
Iterating over the problem of localization plus classification we end up with the need for detecting and classifying multiple objects at the same time. Object detection is the problem of finding and classifying a variable number of objects on an image. The important difference is the “variable” part. In contrast with problems like classification, the output of object detection is variable in length, since the number of objects detected may change from image to image.
The PASCAL Visual Object Classes datasets, or PASCAL VOC for short (e.g. VOC 2012), is a common dataset for object detection.
Deep learning for Image Localization and Object Detection
There is nothing hardcore about the architectures which we are going to discuss. What we are going to discuss are some clever ideas to make the system intolerant to the number of outputs and to reduce its computation cost. So, we do not know the exact number of objects in our image and we want to classify all of them and draw a bounding box around them. That means that the number of coordinates that the model should output is not constant. If the image has 2 objects, we need 8 coordinates. If it has 4 objects, we want 16. So how we build such a model?
One key idea to traditional computer vision is regions proposal. We generate a set of windows that are likely to contain an object using classic CV algorithms, like edge and shape detection and we apply only these windows (or regions of interests) to the CNN. To learn more about how regions are proposed, we introduce a new architecture called RCNN.
R-CNN
Given an image with multiple objects, we generate some regions of interests using a proposal method (in RCNN’s case this method is called selective search) and wrap the regions into a fixed size. We forward each region to Convolutional Neural Network (such as AlexNet), which will use an SVM to make a classification decision for each one and predicts a regression for each bounding box. This prediction comes as a correction of the region proposed, which may be in the right position but not at the exact size and orientation.
Although the model produces good results, it suffers from a major issue. It is quite slow and computationally expensive. Imagine that in an average case, we produce 2000 regions, which we need to store in disk, and we forward each one of them into the CNN for multiple passes until it is trained. To fix some of these problems, an improvement of the model comes in play called ‘Fast-RCNN’
Fast RCNN
The idea is straightforward. Instead of passing all regions into the convolutional layer one by one, we pass the entire image once and produce a feature map. Then we take the region proposals as before (using some external method) and sort of project them onto the feature map. Now we have the regions in the feature map instead of the original image and we can forward them in some fully connected layers to output the classification decision and the bounding box correction.
Note that the projection of regions proposal is implemented using a special layer (ROI layer), which is essentially a type of max-pooling with a pool size dependent on the input, so that the output always has the same size.
Faster RCNN
And we can take this a step further. Using the produced feature maps from the convolutional layer, we infer regions proposal using a Region Proposal network rather than relying on an external system. Once we have those proposals, the remaining procedure is the same as Fast-RCNN (forward to ROI layer, classify using SVM and predict the bounding box). The tricky part is how to train the whole model as we have multiple tasks that need to be addressed:
The region proposal network should decide for each region if it contains an object or not.
It needs to produce the bounding box coordinates.
The entire model should classify the objects to categories.
And again predict the bounding box offsets.
As the name suggests, Faster RCNN turns out to be much faster than the previous models and is the one preferred in most real-world applications.
Localization and object detection is a super active and interesting area of research due to the high emergency of real world applications that require excellent performance in computer vision tasks (self-driving cars, robotics). Companies and universities come up with new ideas on how to improve the accuracy on regular basis.
There is another class of models for localization and object detection, called single shot detectors, which have become very popular in the last few years because they are even faster and require less computational cost in general. Sure, they are less accurate, but they are ideal for embedded systems and similar power-hungry applications.
Object segmentation
Going one step further from object detection we would want to not only find objects inside an image, but find a pixel by pixel mask of each of the detected objects. We refer to this problem as instance or object segmentation.
Semantic Segmentation is the process of assigning a label to every pixel in the image. This is in stark contrast to classification, where a single label is assigned to the entire picture. Semantic segmentation treats multiple objects of the same class as a single entity. On the other hand, instance segmentation treats multiple objects of the same class as distinct individual objects (or instances). Typically, instance segmentation is harder than semantic segmentation.
In order to perform semantic segmentation, a higher level understanding of the image is required. The algorithm should figure out the objects present and also the pixels which correspond to the object. Semantic segmentation is one of the essential tasks for complete scene understanding. This can be used in analysis of medical images and satellite images. Again, the VOC 2012 and MS COCO datasets can be used for object segmentation.
Deep Learning for Image Segmentation
Modern image segmentation techniques are powered by deep learning technology. Here are several deep learning architectures used for segmentation.
Convolutional Neural Networks (CNNs)
Image segmentation with CNN involves feeding segments of an image as input to a convolutional neural network, which labels the pixels. The CNN cannot process the whole image at once. It scans the image, looking at a small “filter” of several pixels each time until it has mapped the entire image. To learn more see our in-depth guide about Convolutional Neural Networks.
Fully Convolutional Networks (FCNs)
Traditional CNNs have fully-connected layers, which can’t manage different input sizes. FCNs use convolutional layers to process varying input sizes and can work faster. The final output layer has a large receptive field and corresponds to the height and width of the image, while the number of channels corresponds to the number of classes. The convolutional layers classify every pixel to determine the context of the image, including the location of objects.
DeepLab
One main motivation for DeepLab is to perform image segmentation while helping control signal decimation—reducing the number of samples and the amount of data that the network must process. Another motivation is to enable multi-scale contextual feature learning—aggregating features from images at different scales. DeepLab uses an ImageNet pre-trained residual neural network (ResNet) for feature extraction. DeepLab uses atrous (dilated) convolutions instead of regular convolutions. The varying dilation rates of each convolution enable the ResNet block to capture multi-scale contextual information. DeepLab comprises three components:
Atrous convolutions—with a factor that expands or contracts the convolutional filter’s field of view.
ResNet—a deep convolutional network (DCNN) from Microsoft. It provides a framework that enables training thousands of layers while maintaining performance. The powerful representational ability of ResNet boosts computer vision applications like object detection and face recognition.
Atrous spatial pyramid pooling (ASPP)—provides multi-scale information. It uses a set of atrous convolutions with varying dilation rates to capture long-range context. ASPP also uses global average pooling (GAP) to incorporate image-level features and add global context information.
SegNet neural network
An architecture based on deep encoders and decoders is also known as semantic pixel-wise segmentation. It involves encoding the input image into low dimensions and then recovering it with orientation invariance capabilities in the decoder. This generates a segmented image at the decoder end.
Conclusion
In this post we have discussed some applications of computer vision including Image Localization, Object Detection and Image Segmentation. We then discussed required deep learning architectures which are used for the above applications.
Deep Learning has its limitations, scientists argue.
“We have machines that learn in a very narrow way,” Yoshua Bengio, deep learning pioneer, said in his keynote address at NeurIPS in December, 2019. “They need much more data to learn a task than human examples of intelligence, and they still make stupid mistakes.”
Unarguably, deep learning is an imperfect framework of intelligence. It does not think abstractedly, does not comprehend causation and struggles with out-of-distribution generalization. For a deeper understanding of its limitations, this brilliant paper on the science and its shortcomings is available on the internet.
However, despite numerous shortcomings, the commercial uses of deep learning are only just being mined and its capabilities to automate and transform industries still abound. AI and deep learning capabilities, as developed as they are today, are sufficiently mature to spearhead transformation, innovation, and value creation across industries like agriculture, healthcare and construction. “For the most part, these transformative opportunities have not yet been operationalized at scale.”
Radiology
For instance, in the radiology industry, something as extreme and point blank as this was declared in 2016 by AI luminary Geoff Hinton – “It’s quite obvious that we should stop training radiologists now.” Hinton’s comments drew worked up reactions in the medical community but his statement was based on strong data which showed neural networks can identify medical conditions from X-rays with better accuracy than human radiologists can.
Yet, years after Hinton foresaw the removal of the need of human radiologists from the medical science field, no clinic in the world has deployed AI-driven radiology tools at scale. Only a few health organizations have begun using it in limited settings. But more and more organizations are slowly adopting deep learning in radiology.
Off Road Autonomous Vehicles
In another instance, the off-road autonomous vehicle industry is seeing a slow move towards tapping the massive unrealized commercial potential of AI. Construction, agriculture and mining are some of the largest industries in the world. If these industries start deploying deep learning powered automated machines to do work that human hands are trained to do, a massive pool of cost, productivity and safety benefits could be tapped.
Energy
In the field of energy, leading players like BP are using deep learning to innovate and transform work conditions on site. “It uses technology to drive new levels of performance, improve the use of resources and safety and reliability of oil and gas production and refining. From sensors that relay the conditions at each site to using AI technology to improve operations, BP puts data at the fingertips of engineers, scientists and decision-makers to help drive high performance.”
Retail
Burberry, a luxury fashion brand, uses big data and AI to fight counterfeit products. It is also trying to enhance sales and customer relationships by initiating a loyalty program that creates data to help personalize the shopping experience for each customer.
Social Media
Both Twitter and Facebook are tapping into structured and unstructured sets of big data for understanding user behavior and using deep learning to check for communal or racist comments and user preferences.
Deep Learning and Artificial Intelligence is the future and it is here to stay. No wonder then, that more and more professionals are opting to train themselves through deep learning courses. DexLab Analytics is one of the best Deep Learning training institutes in Delhi. Do go through its website for more details.
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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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Before we get started with the process of building a Neural Network, we need to understand first what a Neural Network is.
A neural network is a collection of neurons connected by synapses. This collection is organized into three main layers: the input layer, the hidden layer, and the output layer.
In an artificial neural network, there are several inputs, which are called features, producing a single output, known as a label.
Analogy between Human Mind and Neural Network
Scientists believe that a living creature’s brain processes information through the use of a biological neural network. The human brain has as many as 100 trillion synapses – gaps between neurons – which form specific patterns when activated.
In the field of Deep Learning, a neural network is represented by a series of layers that work much like a living brain’s synapses. It is becoming a popular course now, with an array of career opportunities. Thus, Deep learning Certification in Gurgaon is a must for everyone.
Scientists use neural networks to teach computers how to do things for themselves. The whole concept of Neural network and its varied applications are pretty interesting. Moreover, with the matchless Neural Networks Training in Delhi, you need not look any further.
There are numerous kinds of deep learning and neural networks:
Feedforward Neural Network – Artificial Neuron
Radial basis function Neural Network
Kohonen Self Organizing Neural Network
Recurrent Neural Network (RNN) – Long Short Term Memory
Convolutional Neural Network
Modular Neural Network
Generative adversarial networks (GANs)
Working of a Simple Feedforward Neural Network
It takes inputs as a matrix (2D array of numbers).
Multiplies the input by a set weight (performs a dot product aka matrix multiplication).
Applies an activation function.
Returns an output.
Error is calculated by taking the difference from the desired output from the data and the predicted output. This creates our gradient descent, which we can use to alter the weights.
The weights are then altered slightly according to the error.
To train, this process is repeated 1,000+ times. The more the data is trained upon, the more accurate our outputs will be.
Implementation of a Neural Network with Python and Keras
Keras has two types of models:
Sequential model
The model class used with functional API
Sequential model is probably the most used feature of Keras. Primarily, it represents the array of Keras Layers. It is convenient and builds different types of Neural Networks really quick, just by adding layers to it. Keras also has different types of Layers like Dense Layers, Convolutional Layers, Pooling Layers, Activation Layers, Dropout Layers etc.
The most basic layer is Dense Layer. It has many options for setting the inputs, activation function and so on. So, let’s see how one can build a Neural Network using Sequential and Dense.
First, let’s import the necessary code from Keras:
After this step, the model is ready for compilation. The compilation step asks to define the loss function and the kind of optimizer which should be used. These options depend on the problem one is trying to solve.
Now, the model is ready to get trained. Thus, the parameters get tuned to provide the correct outputs for a given input. This can be done by feeding inputs at the input layer and then, getting an output.
After this one can calculate the loss function using the output and use backpropagation to tune the model parameters. This will fit the model parameters to the data.
Output of the above cell:-
This output shows the loss decrease and the accuracy increase over time. At this point, one can experiment with the hyper-parameters and neural network architecture to get the best accuracy.
After getting the final model architecture, one can now take the model and use feed-forward passes and predict inputs. To start making predictions, one can use the testing dataset in the model that has been created previously. Keras enables one to make predictions by using the .predict() function.
Some points to be remembered while building a strong Neural Network
1. Adding Regularization to Fight Over-Fitting
The predictive models mentioned above are prone to a problem of overfitting. This is a scenario whereby the model memorizes the results in the training set and isn’t able to generalize on data that it hasn’t seen.
In neural networks, regularization is the technique that fights overfitting by adding a layer in the neural network. It can be done in 3 ways:
L1 Regularization
L2 Regularization
Dropout Regularization
Out of these, Dropout is a commonly used regularization technique. In every iteration, it adds a Dropout layer in the neural network and thereby, deactivates some neurons. The process of deactivating neurons is usually random.
2. Hyperparameter Tuning
Grid search is a technique that you can use to experiment with different model parameters to obtain the ones that give you the best accuracy. This is done by trying different parameters and returning those that give the best results. It helps in improving model accuracy.
Conclusion
Neural Network is coping with the fast pace of the technology of the age remarkably well and thereby, inducing the necessity of courses like Neural Network Machine Learning Python, Neural Networks in Python courseand more. Though these advanced technologies are just at their nascent stage, they are promising enough to lead the way to the future.
In this article, Building and Training our Neural Network is shown. This simple Neural Network can be extended to Convolutional Neural Network and Recurrent Neural Network for more advanced applications in Computer Vision and Natural Language Processing respectively.
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At the latest AI summit organized by Intel, Mr. Naveen Rao, Vice President and General Manager of Intel’s AI Products Group, focused on the most vibrant age of computing that is the present age we are living. According to Rao, the widespread and sudden growth of neural networks is putting the capability of the hardware into a real test. Therefore, we now have to reflect deeply on “how processing, network, and memory work together” to figure a pragmatic solution, he said.
The storage of data has seen countless improvements in the last 20 years. We can now boast of our prowess of handling considerably large sets of data, with greater computing capability in a single place. This led to the expansion of the neural network models with an eye on the overall progress in neural Network Machine Learning Pythonand computing in general.
With the onset of exceedingly large data sets to work with, Deep learning for Computer Vision Courseand the other models of Deep Learning to recognize speech, images, and text are extensively feeding on them. The technological giants were undoubtedly the early birds to grab the technical: the hardware and the software configuration to have an edge on the others.
Surely, Deep Learning is on its peak now, where computers can identify the images with incredible vividness. On the other hand, chatbots can carry on with almost natural conversations with us. It is no wonder that the Deep learning Training Institutesall over the world are jumping in the race to bring all of these new technologies efficiently to the general mass.
The Big Problem
We are living in the dynamic age of AI and Machine Learning, with the biggies like Google, Facebook, and its peers, having the technical skills and configuration to take up the challenges. However, the neural networks have fattened up so much lately that it has already started to give the hardware a tough time, getting the better of them all the time.
The number of parameters of the Neural network models is increasing as never before. They are “actually increasing on the order of 10x year on year”, as per Rao. Thus, it is a wall looming in AI. Though Intel is trying its best to tackle this obvious wall, which might otherwise give the industry a severe setback, with extensive research to bring new chip architectures and memory technologies into play, it cannot solve the AI processing problem single-handedly. Rao concluded on a note of requesting the partners in the present competitive scenario.
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It’s hard to ignore the growing prowess of AI and machine learning.
Previously, Gartner predicted that AI will become one of the key priorities for more than 30% C-Suite professionals by 2020. Indeed, it’s true; software vendors across the globe are following this new gold rush. For them, data is like new oil. In this blog, we explore the future of this budding technology and gain some new insights and ideas. Let’s see what the heavyweights from the digital industry have to say:
Hyper-targeting and Personalization
Ben Wald, Co-Founder & VP of Solutions Implementation at Very
Though machine learning is a subset of data analysis, it’s rapidly influencing the IoT industry and its respective devices. In the last couple of years, nearly 90% of data was generated through an array of smartphones, watches and cars. These mountains of data help in forming better customer relationships.
How? Using Machine Learning Using Python of course! With this power tool, the corporate houses are trying to understand their target audience and extract crucial information regarding how well they receive their products and related after-sales services. Fine-tuning personalization on a wider scale is the key. Hopefully, soon, we will be able to achieve this goal. We are still in the nascent stage.
Did you know that AI algorithms have a massive impact on search engine results?
In the next few years, search engines are expected to enhance user and admin experience: courtesy breakthroughs in neural networks and deep learning technologies. These revolutionary technologies, especially deep learning for computer vision with Python will make sure users enjoy a fabulous searching experience and will deliver highly relevant answers. Currently, we are working on delivering results that are based on user’s query and profile. The process requires a lot of manual configurations and a fundamental understanding of how search engines work. Later, the results will be customized based on individuals’ past preferences, interactions and words used. It will be fun to see how machine learning algorithms transform the dynamo of content publishing and search engines.
Real and revolutionary, the concept of quantum computing is wreaking havoc in the domain of science and technology. It is the future of machine learning triggering an array of innovations. Integrating quantum computing with machine learning is expected to transform the field triggering accelerated learning, quicker processing and better capabilities. This means the intricate challenges that we can’t solve now could be done in a fraction of time then.
The potential of quantum computing is huge in the future and is likely affect millions of lives, notably in medicine and healthcare industry.
Currently, there are no commercially-built quantum algorithms or hardware available in the market. However, several research facilities and government agencies have been investing in this new field of science of late.
End Notes
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This is a blog which shall widen your approach on the Statistical Application using R & Python. You perhaps already have been calculatingGeometric Mean using R & Pythonand are already aware of theApplication of Harmonic Mean using R & Python. However, if you are eager to further your knowledge about Skewness & Kurtosis and interested to know of their application using R and Python, then this is the right place.
Skewness:
Skewness is a metric which tells us about the location of my dataset. That is, if you want to know where most of the values are concentrated on an ascending scale.
Skewness is of two kinds: Positive skew and Negative skew. A positively skewed dataset will have most of the values concentrated at the beginning of the scale. Eg: If a woman is asked to rate 100 tinder profiles based on the looks on a scale of 1 – 10, 1 being the ugliest and 10 being the most handsome. Then the resulting ratings will be positively skewed. This is to say that women are harsh critiques of looks.
Now, consider another example: Say if the wealth of the 1% richest people were to be plotted on a scale of say $0 – $200 billion. Then, most of the values will be concentrated at the end of the scale. This will be an example of a negatively skewed dataset.
In essence, skewness is the third central moment about mean and gives us a feel for the location of the data set values. It is recommended to go throughSTATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY to have an understanding of the Central Tendency and its measures. Having no skewness will mean the data set is fairly symmetrical and has a bell shaped curve.
Where n is the sample size, Xi is the ith X value, X is the average and S is the sample standard deviation. Note the exponent in the summation. It is “3”.
Kurtosis:
Kurtosis is a statistical measure that’s used to describe, or Skewness, of observed data around the mean, sometimes referred to as the volatility to volatility. Kurtosis is used generally in the statistical field to describe trends in charts. Kurtosis can be present in a chart with fat tails and a low, even distribution, as well as be present in a chart with skinny tails and a distribution concentrated toward the mean.
Kurtosis for a normal distribution is 3. Most software packages use the formula:
The types of kurtosis are:-
Application:
A person tries to analyze last 12months interest rate of the investment firm to understand the risk factor for the future investment.
Calculating the Skewness & Kurtosis of interest rate in R, we get the positive skewed value, which is near to 0. The skewness of the interest rate is 0.5585253.
The kurtosis of the interest rate is 2.690519
Kurtosis is less than 3, so this is Platykurtic distribution.
Calculate Skewness & Kurtosis in Python:
Calculating the Skewness & Kurtosis of interest rate in Python, we get the positive skewed value and near from 0. The skewness of the interest rate is 0.641697.
The kurtosis of the interest rate is 0.241602.
Kurtosis is less than 3, so this is Platykurtic distribution.
Conclusion:
Firstly, according to the output of the data the value is positively skewed(R & Python), positive skewness indicates a distribution with an asymmetric tail extending toward more positive values.
And the kurtosis is less than 3 (R & Python), it is a platykurtic distribution. Positive kurtosis indicates a relatively peaked distribution. And the distribution is light tails.
Secondly, the value of the skewness and kurtosis are different in R and Python, but the actual effects are more or less the same. The results are different because skewness and kurtosis are calculated with different formulae or method for the measurement like Bowley’s measure, Pearson’s(First, Second) measures, Fisher’s measure & Moment’s measure. And different software (ex. R, Python, SAS, Excel etc) using different processes to calculate skewness & kurtosis brings the same ultimate result. The numerical values change only when the numbers are also changed. So, we sometimes get different results.
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