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Funnel Activation for Visual Recognition: A New Research Breakthrough

Funnel Activation for Visual Recognition: A New Research Breakthrough

The latest research work in the field of image recognition led to the development of a new activation function for visual recognition tasks, namely Funnel activation(FReLU). In this research ReLU and PReLU are extended to a 2D activation by adding a negligible overhead of spatial condition. Experiments on ImageNet, COCO detection, and semantic segmentation tasks are conducted to measure the performance of FReLU.

CNNs have shown advanced performances in many visual recognition tasks, such as image classification, object detection, and semantic segmentation.  In a CNN framework, basically two major kind of layers play crucial roles, the convolution layer and the non-linear activation layer. Both the convolution layers and activation layers perform distinct functions, however, in both layers there are challenges regarding capturing the spatial dependency. However, despite advancements achieved by complex convolutions, improving the performance of visual tasks is still challenging which results in Rectified Linear Unit (ReLU) being the most widely used function till date.

The research focused on two distinct queries

  1. Could regular convolutions achieve similar accuracy, to grasp the challenging complex images?
    2. Could we design an activation specifically for visual tasks?

1. Effectiveness and generalization performance

In a bid to find answers to these questions, researchers identified spatially insensitiveness in activations to be the main impending factor that prevent visual tasks from improving further.

To address this issue they proposed to find a new visual activation task that could be effective in removing this obstacle and be a better alternative to previous activation approaches.

How other activations work

Taking a look at other activations such as Scalar activations, Contextual conditional activations helps in understanding the context better.

Scalar activations basically are concerned with single input and output which could be represented in form of y = f(x). ReLU or, the Rectified Linear Unit is a widely used activation that is used for various tasks and could be represented as y = max(x, 0).

Contextual conditional activations work on the basis of many-to-one function. In this process neurons that are conditioned on contextual information are activated.

Spatial dependency modeling

In order to accumulate the various ranges of spatial dependences, some approaches utilize various shapes of convolution kernels which leads to lesser efficiency. In other methods like STN, spatial transformations are adaptively used for refining short-range dependencies for the dense vision tasks.

FReLU differs from all other methods in the sense that it performs better without involving complex convolutions. FReLU addresses the issues and solves with a higher level of efficiency.

Receptive field: How FReLU differs from other methods regarding the Receptive field

The size as well as the region of the receptive field play a crucial role in vision recognition tasks. The pixel contribution can be unequal. In order to implement the adaptive receptive field and for a better performance, many methods resort to complex convolutions. FReLU differs from such methods in the way that it achieves the same goal with regular convolutions in a more simple yet highly efficient manner.

Funnel Activation: how funnel activation works

FReLU being conceptually simple is designed for visual tasks. The research further delves into reviewing the ReLU activation and PReLU which is an advanced variant of ReLU, moving on to the key elements of FReLU the funnel condition and the pixel-wise modeling capacity, both of which are not found in ReLU or, in any of its variants.

2. Funnel activation

Funnel condition

Here the same max(·) is adopted as the simple non-linear function, when it comes to the condition part it gets extended to be a 2D condition which is dependent on the spatial context for individual pixel.  For the implementation of the spatial condition, Parametric Pooling Window is used for creating dependency.

Pixel-wise modeling capacity

 Due to the funnel condition the network is capable of generating spatial conditions in the non-linear activations for each pixel. This differs from usual methods where spatial dependency is created in the convolution layer and non-linear transformations are conducted separately. This model achieves a pixel-wise modeling capacity thereby extraction of spatial structure of objects could be addressed naturally.

Experiments

Evaluation of the activation is tested via experiments on ImageNet 2012 classification dataset[9,37].The evaluation is done in stages starting with  different sizes of ResNet. Comparisons with scalar activations is done on ResNets with varying depths, followed by Comparison on light-weight CNNs. An object detection experiment is done to evaluate the generalization performance on various tasks on COCO dataset containing 80 object categories. Further comparison is also done on semantic segmentation task in CityScape dataset. Difference of the images could be perceived through the CityScape images.

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4. Visualization of semantic segmentation

Funnel activation: ablation studies

The scope of the visual activation is tested further via ablation studies where each component of the activation namely 1) funnel condition, and 2)max(·) non-linearity are individually examined. The three parts of the investigation are as follows Ablation on the spatial condition, Ablation on the non-linearity, Ablation on the window size

Compatibility with Existing Methods

Before the new activation could be adopted into the convolutional networks, layers and stages need to be decided, the compatibility with other existing approaches such as SENet also was tested. The process took place in stages as follows

Compatibility with different convolution layers

Compatibility with different stages

Compatibility with SENet

Conclusion: Post all the investigations done to test out the compatibility of FReLU on different levels, it could be stated that this funnel activation is simple yet highly effective and specifically developed for visual tasks.  Its pixel-wise modeling capacity is able to grasp even complex layouts easily. But further research work could be done to expand its scope as it definitely has huge potential.

To get in-depth knowledge regarding the various stages of the research work on Funnel Activation for Visual Recognition, check https://arxiv.org/abs/2007.11824.

 


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5 Most Powerful Computer Vision Techniques in use

5 Most Powerful Computer Vision Techniques in use

Computer Vision is one of the most revolutionary and advanced technologies that deep learning has birthed. It is the computer’s ability to classify and recognize objects in pictures and even videos like the human eye does. There are five main techniques of computer vision that we ought to know about for their amazing technological prowess and ability to ‘see’ and perceive surroundings like we do. Let us see what they are.

Image Classification

The main concern around image classification is categorization of images based on viewpoint variation, image deformation and occlusion, illumination and background clutter. Measuring the accuracy of the description of an image becomes a difficult task because of these factors. Researchers have come up with a novel way to solve the problem.

They use a data driven approach to classify the image. Instead of classifying what each image looks like in code, they feed the computer system with many image classes and then develop algorithms that look at these classes and “learn” about the visual appearance of each class. The most popular system used for image classification is Convolutional Neural Networks (CNNs).

Object Detection

Object detection is, simply put, defining objects within images by outputting bounding boxes and labels or tags for individual objects. This differs from image classification in that it is applied to several objects all at once rather than identifying just one dominant object in an image. Now applying CNNs to this technique will be computationally expensive.

So the technique used for object detection is region-based CNNs of R-CNNs. In this technique, first an image is scanned for objects using an algorithm that generates hundreds of region proposals. Then a CNN is run on each region proposal and only then is each object in each region proposal classified. It is like surveying and labelling the items in a warehouse of a store.

Object Tracking

Object tracking refers to the process of tracking or following a specific object like a car or a person in a given scene in videos. This technique is important for autonomous driving systems in self-driving cars. Object detection can be divided into two main categories – generative method and discriminative method.

The first method uses the generative model to describe the evident characteristics of objects. The second method is used to distinguish between object and background and foreground.

Semantic Segmentation

Crucial to computer vision is the process of segmentation wherein whole images are divided or segmented into pixelgroups that are subsequently labeled and classified.

The science tries to understand the role of each pixel in the image. So, for instance, besides recognizing and detecting a tree in an image, its boundaries are depicted as well. CNNs are best used for this technique.

Instance Segmentation

This method builds on semantic segmentation in that instead of classifying just one single dominant object in an image, it labels multiple images with different colours.

When we see complicated images with multiple overlapping objects and different backgrounds, we apply instance segmentation to it. This is done to generate pixel studies of each object, their boundaries and backdrops.

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Conclusion

Besides these techniques to study and analyse and interpret images or a series of images, there are many more complex techniques that we have not delved into in this blog. However, for more on computer vision, you can peruse the DexLab Analytics website. DexLab Analytics is a premiere Deep Learning training institute In Delhi.

 


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Using Deep Learning To Track Tropical Cyclones: A Study

Using Deep Learning To Track Tropical Cyclones: A Study

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 a report.

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.

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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.

 


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Skills Data Scientists Must Master in 2020

Skills Data Scientists Must Master in 2020

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.

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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.


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Applications of Artificial Intelligence: Healthcare

Applications of Artificial Intelligence: Healthcare

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.

Conclusion

It is no surprise then that developing countries like India are even more enthusiastic about adopting deep learning courses in Delhi and machine learning and artificial intelligence sciences in the healthcare sector. Machine Learning courses in India are coming up everywhere and it is important to note that DexLab Analytics is one of the leading artificial intelligence training institute in Gurgaon. Do visit the website today.


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Deep Learning — Applications and Techniques

Deep Learning — Applications and Techniques

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.

Deep Learning — Applications and Techniques

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).

Deep Learning — Applications and Techniques

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.

Deep Learning — Applications and Techniques

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.


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What is a Neural Network?

What is a Neural Network?

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:

  1. Feedforward Neural Network – Artificial Neuron
  2. Radial basis function Neural Network
  3. Kohonen Self Organizing Neural Network
  4. Recurrent Neural Network (RNN) – Long Short Term Memory
  5. Convolutional Neural Network
  6. Modular Neural Network
  7. Generative adversarial networks (GANs)

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Working of a Simple Feedforward Neural Network

  1. It takes inputs as a matrix (2D array of numbers).
  2. Multiplies the input by a set weight (performs a dot product aka matrix multiplication).
  3. Applies an activation function.
  4. Returns an output.
  5. 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.
  6. The weights are then altered slightly according to the error.
  7. 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 PythonNeural Networks in Python course and 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.

Reference Blogs:

https://keras.rstudio.com

https://www.khanacademy.org/math/precalculus/x9e81a4f98389efdf:matrices/x9e81a4f98389efdf:multiplying-matrices-by-matrices/v/matrix-multiplication-intro

 

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Application of Mode using R and Python

Application of Mode using R and Python

Mode, for a given set of observations, is that value of the variable, where the variable occurs with the maximum or the highest frequency.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will elucidate the Mode and its application using Python and R.

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.

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.

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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 the APPLICATION OF HARMONIC MEAN USING R AND PYTHON.

Dexlab Analytics is a formidable institute for Deep learning for computer vision with PythonHere, you would also find more information about courses in Python, Deep LearningMachine Learning, and Neural Networks which will come with proper certification at the end.

We are there in the Social Media where you can follow us both in Facebook and Instagram.

 

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A Deeper Understanding of Deep Learning

A Deeper Understanding of Deep Learning

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.

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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.

Deep Learning and AI using Python

With these being said, Deep Learning is simply superb in how it has performed still and the promise that it is showing to be on par with the age. Therefore, if you are seeking for the Deep learning for computer vision course, you can simply avail of Deep Learning for computer vision Training Center in Delhi NCR.

 

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