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Engineering To Data Science: What’s Causing The Professionals To Consider A Mid-Career Switch?

Engineering To Data Science: What's Causing The Professionals To Consider A Mid-Career Switch?

Among all the decisions we make in our lives, choosing the right career path seems to be the most crucial one. Except for a couple of clueless souls, most students know by the time they clear their boards what they aspire to be. A big chunk of them veer towards engineering, MBA, even pursue masters degree in academics and post completion of their studies they settle for relevant jobs. So far that used to be the happily ever after career story, but, in the last couple of years there seems to be a big paradigm shift and it is causing a stir across industries. Professionals having an engineering background, or, masters degree are opting for a mid-career switch and a majority of them are opting for the data science domain by pursuing a Data Science course. So, what’s pushing them towards DS? Let’s investigate.

What’s causing the career switch?

No matter which field someone has chosen for career, achieving stability is a common goal. However, in many fields be it engineering, or, something else the job opportunities are not unlimited yet the number of job seekers is growing every year. So, thereby one can expect to face a stiff competition grabbing a well-paid job.

There have been many layoffs in recent times especially due to the unprecedented situation the world is going through. Even before that there were reports of job cuts and certain sectors not doing well would directly impact the career of thousands. Even if we do not concentrate on the extremes, the growth prospect in most places could be limited and achieving the desired salary or, promotion oftentimes becomes impossible. This leads to not only frustration but uncertainty as well.

The demand for big data

If you haven’t been living as a hermit, then you are aware of the data explosion that impacted nearly every industry. The moment everyone understood the power of big data they started investing in research and in building a system that can handle, store and process data which is a storehouse of information. Now, who is going to process data to extract the information? And here comes the new breed of data experts, namely the data scientists, who have mastered the technology having undergone Data Science training and are able to develop models and parse through data to deliver the insights companies are looking for to make informed decisions. The data trend is pushing the boundaries and as cutting edge technologies like AI, machine learning are percolating every aspect of the industries, the demand for avant-garde courses like natural language processing course in gurgaon, is skyrocketing.

Lack of trained industry ready data science professionals

Although big data has started trending as businesses started gathering data from multiple sources, there are not many professionals available to handle the data. The trend is only gaining momentum and if you just check the top job portals such as Glassdoor, Indeed and go through the ads seeking data scientists you would immediately know how far the field has traveled. With more and more industries turning to big data, the demand for qualified data scientists is shooting up.

Why data science is being chosen as the best option?

In the 21st century data science is a field which has plethora of opportunities for the right people and this is one field which is not only growing now but is also poised to grow in future as well. The data scientist is one of the most highest paid professional in today’s job market. According to the U.S. Bureau of Labor Statistics report by the year 2026 there is a possibility of creation of 11.5 million jobs in this field.

Now take a look at the Indian context, from agriculture to aviation the demand for data scientists would continue to grow as there is a severe shortage of professionals. As per a report the salary of a data scientist could hover around ₹1,052K per annum and remember the field is growing which means there is not going to be a dearth of job opportunities or, lucrative pay packages.

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The shift

Considering all of these factors there has been a conscious shift in the mindset of the professionals, who are indeed making a beeline for institutes that offer data science certification. By doing so they hope to-

  • Access promising career opportunities
  • Achieve job satisfaction and financial stability
  • Earn more while enjoying job security
  • Work across industries and also be recruited by industry biggies
  • Gain valuable experience to be in demand for the rest of their career
  • Be a part of a domain that promises innovation and evolution instead of stagnation

Keeping in mind the growing demand for professionals and the dearth of trained personnel, premier institutes like DexLab Analytics have designed courses that are aimed to build industry-ready professionals. The best thing about such courses is that you can hail from any academic background, here you will be taught from scratch so that you can grasp the fundamentals before moving on to sophisticated modules.

Along with providing data science certification training, they also offer cutting edge courses  such as, artificial intelligence certification in delhi ncr, Machine Learning training gurgaon. Such courses enable the professionals enhance their skillset to make their mark in a world which is being dominated by big data and AI.  The faculty consists of skilled professionals who are armed with industry knowledge and hence are in a better position to shape students as per industry demands and standards.

The mid-career switch is happening and will continue to happen. There must be professionals who have the expertise to drive an organization towards the future by unlocking their data secrets. However, something must be kept in mind if you are considering a switch, you need to be ready to meet challenges,  along with knowledge of Python for data science training, you need to have a vision, a hunger and a love for data to be a successful data scientist.


A Quick Guide To Natural Language Processing (NLP) And Its Applications

 A Quick Guide To Natural Language Processing (NLP) And Its Applications

When you interact with Alexa, or, conduct a voice search on Google, do you wonder about the technology behind it? What is it that makes it possible to communicate with machines as you would with a human being?

Natural Language Processing (NLP) AKA computational linguistics is a subset of Artificial Intelligence, makes all of it possible by combining artificial intelligence, machine learning, and language to facilitate interaction between computers and humans.

So how does NLP work?

When you put a voice command, it gets recorded and then the audio gets converted into text, and the NLP system starts working on the context and the intention of the command. Basically, the words that we speak are labeled and get converted into a set of numbers. However, Human language is complex and has many nuances and underlying subtexts. The same word under a different context can have different connotations. So, when a simple command is put is gets easier for the machine to follow through as it contains simpler words and clear context, but, the system needs to evolve more to fully process the complex language patterns that evolved through ages. There are courses available such as natural language processing course in gurgaon that can help one acquire specialized knowledge in this field.

NLP and its applications

NLP despite being in a nascent stage is getting recognized for its potential and being applied for executing various tasks.

Sentiment Analysis to assess consumer behavior

 This functionality of NLP is an important part of social media analytics that parses the comments and reactions of users regarding products, brand messages spread over social media platforms to detect the mood of the person. This helps businesses gauge customers’ behavior and to make necessary modifications accordingly. 

Email filtering and weeding out spam

If you are a user of Gmail then you must have noticed the way your emails get segmented once they arrive. Primary, Social, and Promotions are the three broad categories followed by others, there is a segment for spams as well. This smart segmentation is a stark example of NLP at work.

Basically text classification technique is used here to assign a mail to a certain category that is pre-defined. You must have also noticed how well your spam is sorted, this is another result of the application of NLP where certain words trigger the spam alert and the mail gets sent straight to the spam folder. However, this sorting is yet to be perfected via further research.

Automatic summarization to find relevant information

Now thanks to digitization we have to deal with huge amounts of data which has led to information overload. This massive amount of data needs to be processed to find actionable information. Automatic Summarization makes it possible by processing a big document and presenting an accurate and short summary of it.


No discussion on NLP can ever be complete without mentioning the chatbots. The customer service segment is gaining huge benefits from these smart chatbots that can offer virtual assistance to the customers 24×7.  Chatbots can not only enhance customer experience but, are also great for reducing costs for any business. However, modern-day chatbots can handle simple, mundane queries that do not require any special knowledge and skill, in the future we could hope to see the bots handling specialized queries in real-time.

Spell and grammar checker

If you have ever used Grammarly and felt impressed with the result then you must have wondered at some point how does it do it? When you put in a text, it not only looks for punctuations but also points out spelling errors and also shows grammatical errors in places where there is no subject-verb agreement. In fact, you also get alternative suggestions to improve your writing. All of this is possible thanks to transformers used by NLP.

Machine Translation

If you are familiar with Google and its myriad apps then you must be familiar with Google Translate. How quickly it translates your sentences in a preferred language format, machine translation is one application of NLP that is transforming the world. We always talk about big data but making it accessible to people scattered across the globe divided by language barriers could be a big problem. So, the NLP enabled us with machine translation that uses the power of smart algorithms to translate without the need of any human supervision or intervention. However, there is still huge room for improvement as languages are full of nuanced meanings that only a human is capable of understanding.

 What are some examples of NLP at work?

We are not including Siri, or, Alexa  here as you are already familiar with them

  • SignAll is an excellent NLP powered tool that is used for converting sign language into text.
  • Nina is a virtual assistant that deals with banking queries of customers.
  • Translation gets easier with another tool called Lilt that can integrate with other platforms as well.
  • HubSpot integrated the autocorrect feature into its site search function to make searching hassle-free for users.
  • MarketMuse helps writers create content that is high-quality and most importantly relevant.

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Just like AI and its various subsets, NLP is also a field that is still evolving and has a long journey ahead. Language processing is a function that needs more research because simulating human interaction is one thing and processing languages that are so nuanced is not a cakewalk. However, there are plenty of good career opportunities available and undergoing an artificial intelligence course in delhi would be a sound career move.



Top Six Applications of Natural Language Processing (NLP)

Top Six Applications of Natural Language Processing (NLP)

Words are all around us – in the form of spoken language, texts, sound bytes and even videos. The world would have been a chaotic place had it not been for words and languages that help us communicate with each other.

Now, if we were to enhance language with the attributes of artificial intelligence, we would be working with what is known as Natural Language Processing or NLP – the confluence of artificial intelligence and computational linguistics.

In other words, “NLP is the machine’s ability to process what was said to it, structure the information received, determine the necessary response and respond in a language that we understand”.

Here is a list of popular applications of NLP in the modern world.

1. Machine Translation

When a machine translates from one language to another, “we deal with “Machine” Translation. The idea behind MT is simple — to develop computer algorithms to allow automatic translation without any human intervention. The best-known application is probably Google Translate.”

2. Voice and Speech Recognition

Though voice recognition technology has been around for 50 years, it is only in the last few decades, owing to NLP, have scientists achieved significant success in the field. “Now we have a whole variety of speech recognition software programs that allow us to decode the human voice,”be it in mobile telephony, home automation, hands-free computing, virtual assistance and video games.

3. Sentiment Analysis

“Sentiment analysis (also known as opinion mining or emotion AI) is an interesting type of data mining that measures the inclination of people’s opinions. The task of this analysis is to identify subjective information in the text”. Companies use sentiment analysis to keep abreast of their reputation and customer satisfaction.

4. Question Answering

Question-Answering concerns building systems that “automatically answer questions posed by humans in a natural language”. The real examples of Question-Answering applications are: Siri, OK Google, chat boxes and virtual assistants.

5. Automatic Summarization

Automatic Summarization is the process of creating a short, accurate, and fluent summary of a longer text document. The most important advantage of using a summary is it reduces the time taken to read a piece of text. Here are some applications – Aylien Text Analysis, MeaningCloud Summarization, ML Analyzer, Summarize Text and Text Summary.

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6. Chatbots

Chatbots currently operate on several channels like the Internet, web applications and messaging platforms. “Businesses today are interested in developing bots that can not only understand a person but also communicate with him at one level”.

While such applications celebrate the use of NLP in modern computing, there are some glitches that arise in systems that cannot be ignored. “The very nature of human natural language makes some NLP tasks difficult…For example, the task of automatically detecting sarcasm, irony, and implications in texts has not yet been effectively solved. NLP technologies still struggle with the complexities inherent in elements of speech such as similes and metaphors.”

To know more, do take a look at the DexLab Analytics website. DexLab Analytics is a premiere institute that trains professionals in NLP deep learning classification in Delhi.



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


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.


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.


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.


Decoding Advanced Loss Functions in Machine Learning: A Comprehensive Guide

Decoding Advanced Loss Functions in Machine Learning: A Comprehensive Guide

Every Machine Learning algorithm (Model) learns by the process of optimizing the loss functions. The loss function is a method of evaluating how accurate the given prediction is made. If predictions are off, then loss function will output a higher number. If they’re pretty good, it’ll output a lower number. If someone makes changes in the algorithm to improve the model, loss function will show the path in which one should proceed.

Machine Learning is growing as fast as ever in the age we are living, with a host of comprehensive Machine Learning course in India pacing their way to usher the future. Along with this, a wide range of courses like Machine Learning Using Python, Neural Network Machine Learning Python is becoming easily accessible to the masses with the help of Machine Learning institute in Gurgaon and similar institutes.

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We are having different types of loss functions.

  • Regression Loss Functions
  • Binary Classification Loss Functions
  • Multi-class Classification Loss Functions

Regression Loss Functions

  1. Mean Squared Error
  2. Mean Absolute Error
  3. Huber Loss Function

Binary Classification Loss Functions

  1. Binary Cross-Entropy
  2. Hinge Loss

Multi-class Classification Loss Functions

  1. Multi-class Cross Entropy Loss
  2. Kullback Leibler Divergence Loss

Mean Squared Error

Mean squared error is used to measure the average of the squared difference between predictions and actual observations. It considers the average magnitude of error irrespective of their direction.

This expression can be defined as the mean value of the squared deviations of the predicted values from that of true values. Here ‘n’ denotes the total number of samples in the data.

Mean Absolute Error

Absolute Error for each training example is the distance between the predicted and the actual values, irrespective of the sign.

MAE = | y-f(x) |

Absolute Error is also known as the L1 loss. The MAE cost is more robust to outliers as compared to MSE.

Huber Loss

Huber loss is a loss function used in robust regression. This is less sensitive to outliers in data than the squared error loss. The Huber loss function describes the penalty incurred by an estimation procedure f. Huber (1964) defines the loss function piecewise by:

This function is quadratic for small values of a, and linear for large values, with equal values and slopes of the different sections at the two points where |a|= 𝛿. The variable “a” often refers to the residuals, that is to the difference between the observed and predicted values a=y-f(x), so the former can be expanded to: –

Binary Classification Loss Functions

Binary classifications are those predictive modelling problems where examples are assigned one of two labels.

Binary Cross-Entropy

Cross-Entropy is the loss function used for binary classification problems. It is intended for use with binary classification.

Mathematically, it is the preferred loss function under the inference framework of maximum likelihood. Cross-entropy will calculate a score that summarizes the average difference between the actual and predicted probability distributions for predicting class 1. The score is minimized and a perfect cross-entropy value is 0.

Hinge Loss

The hinge loss function is popular with Support Vector Machines (SVMs). These are used for training the classifiers,

l(y) = max(0, 1- t•y)

where ‘t’ is the intended output and ‘y’ is the classifier score.

Hinge loss is convex function but is not differentiable which reduces its options for minimizing with few methods.

Multi-Class Classification Loss Functions

Multi-Class classifications are those predictive modelling problems where examples are assigned one of more than two classes.

Multi-Class Cross-Entropy

Cross-Entropy is the loss function used for multi-class classification problems. It is intended for use with multi-class classification.

Mathematically, it is the preferred loss function under the inference framework of maximum likelihood. Cross-entropy will calculate a score that summarizes the average difference between the actual and predicted probability distributions for all classes. The score is minimized and a perfect cross-entropy value is 0.

Kullback Leibler Divergence Loss

KL divergence is a natural way to measure the difference between two probability distributions.

A KL divergence loss of 0 suggests the distributions are identical. In practice, the behaviour of KL Divergence is very similar to cross-entropy. It calculates how much information is lost (in terms of bits) if the predicted probability distribution is used to approximate the desired target probability distribution.

There are also some advanced loss functions for machine learning models which are used for specific purposes.

  1. Robust Bi-Tempered Logistic Loss based on Bregman Divergences
  2. Minimax loss for GANs
  3. Focal Loss for Dense Object Detection
  4. Intersection over Union (IoU)-balanced Loss Functions for Single-stage Object Detection
  5. Boundary loss for highly unbalanced segmentation
  6. Perceptual Loss Function

Robust Bi-Tempered Logistic Loss based on Bregman Divergences

In this loss function, we introduce a temperature into the exponential function and replace the softmax output layer of the neural networks by a high-temperature generalization. Similarly, the logarithm in the loss we use for training is replaced by a low-temperature logarithm. By tuning the two temperatures, we create loss functions that are non-convex already in the single-layer case. When replacing the last layer of the neural networks by our bi-temperature generalization of the logistic loss, the training becomes more robust to noise. We visualize the effect of tuning the two temperatures in a simple setting and show the efficacy of our method on large datasets. Our methodology is based on Bregman divergences and is superior to a related two-temperature method that uses the Tsallis divergence.

Minimax loss for GANs

Minimax GAN loss refers to the minimax simultaneous optimization of the discriminator and generator models.

Minimax refers to an optimization strategy in two-player turn-based games for minimizing the loss or cost for the worst case of the other player.

For the GAN, the generator and discriminator are the two players and take turns involving updates to their model weights. The min and max refer to the minimization of the generator loss and the maximization of the discriminator’s loss.

Focal Loss for Dense Object Detection

The Focal Loss is designed to address the one-stage object detection scenario in which there is an extreme imbalance between foreground and background classes during training (e.g., 1:1000). Therefore, the classifier gets more negative samples (or more easy training samples to be more specific) compared to positive samples, thereby causing more biased learning.

The large class imbalance encountered during the training of dense detectors overwhelms the cross-entropy loss. Easily classified negatives comprise the majority of the loss and dominate the gradient. While the weighting factor (alpha) balances the importance of positive/negative examples, it does not differentiate between easy/hard examples. Instead, we propose to reshape the loss function to down-weight easy examples and thus, focus training on hard negatives. More formally, we propose to add a modulating factor (1 − pt) γ to the cross-entropy loss, with tunable focusing parameter γ ≥ 0. 

We define the focal loss as

FL(pt) = −(1 − pt) γ log(pt)


Intersection over Union (IoU)-balanced Loss Functions for Single-stage Object Detection

The IoU-balanced classification loss focuses on positive scenarios with high IoU can increase the correlation between classification and the task of localization. The loss aims at decreasing the gradient of the examples with low IoU and increasing the gradient of examples with high IoU. This increases the localization accuracy of models.

Boundary loss for highly unbalanced segmentation

Boundary loss takes the form of a distance metric on the space of contours (or shapes), not regions. This can mitigate the difficulties of regional losses in the context of highly unbalanced segmentation problems because it uses integrals over the boundary (interface) between regions instead of unbalanced integrals over regions. Furthermore, a boundary loss provides information that is complementary to regional losses. Unfortunately, it is not straightforward to represent the boundary points corresponding to the regional softmax outputs of a CNN. Our boundary loss is inspired by discrete (graph-based) optimization techniques for computing gradient flows of curve evolution.

Following an integral approach for computing boundary variations, we express a non-symmetric L2L2 distance on the space of shapes as a regional integral, which avoids completely local differential computations involving contour points. This yields a boundary loss expressed with the regional softmax probability outputs of the network, which can be easily combined with standard regional losses and implemented with any existing deep network architecture for N-D segmentation. We report comprehensive evaluations on two benchmark datasets corresponding to difficult, highly unbalanced problems: the ischemic stroke lesion (ISLES) and white matter hyperintensities (WMH). Used in conjunction with the region-based generalized Dice loss (GDL), our boundary loss improves performance significantly compared to GDL alone, reaching up to 8% improvement in Dice score and 10% improvement in Hausdorff score. It also yielded a more stable learning process.

Perceptual Loss Function

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a \emph{per-pixel} loss between the output and ground-truth images. Parallel work has shown that high-quality images can be generated by defining and optimizing \emph{perceptual} loss functions based on high-level features extracted from pre-trained networks. We combine the benefits of both approaches and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al in real-time. Compared to the optimization-based method, our network gives similar qualitative results but is three orders of magnitude faster. We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results.


Loss function takes the algorithm from theoretical to practical and transforms neural networks from matrix multiplication into deep learning. In this article, initially, we understood how loss functions work and then, we went on to explore a comprehensive list of loss functions also we have seen the very recent — advanced loss functions.

References: –

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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.


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:


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Deep Learning and its Progress as Discussed at Intel’s AI Summit

Deep Learning and its Progress as Discussed at Intel’s AI Summit

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 Python and computing in general.


With the onset of exceedingly large data sets to work with, Deep learning for Computer Vision Course and 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 Institutes all 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.

Deep Learning and AI using Python

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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Straight Out of College? Grasp These Killer Data Science Skills

Straight Out of College? Grasp These Killer Data Science Skills

Data Science is one of the most demanding fields in the present world. Going hand in hand with the Artificial Intelligence, Data Science is showing a colossal growth in the coming years. So, honestly speaking, you should be prepared with all of the cutting-edge tools and up skill yourself accordingly to pace up with the modern world.

According to Derek Steer, CEO of Mode, the world will generate 50 times more data than what we were present in 2011. Moreover, with the data processing power becoming easy and inexpensive for most of the firms, candidates with real skill and a hunger for knowledge would only see their way through till the end, added Steer.

Among various other skills like retail analytics using Python, neural network machine learning Python, which are dominating and/or expected to rule the world of technology in the upcoming years, here we list you some of them:


Data Visualization

This is one of the top notch skills that you can find now. It is process of maintaining data with the help of graphical representations. This further makes the interpretation and thereby, the comprehension of data, much easier.

This is an extremely relevant skill which is not to be found among the high schoolers. This makes the undergraduates or post graduates with the knowhow of data visualisation all the more important everywhere.

Data Modelling

Data Modelling is the second most wanted skill that the entire world is seeking for. In a nutshell, Data Modelling is the process of understanding and using data to seek relationships across varied sets of information.

It is, in fact, a skill which is gaining an immense popularity among the fresh graduates. You can also reach Dexlab Analytics to gain an insight of all the industry relevant courses and enrol yourself asap to speed up your career!

Deep Learning and AI using Python


Python is undoubtedly the most demanding language ever in the history of computer science; hence, it enjoys all the attention that it gets.

With its welcoming nature to every other architecture, which is in sharp contradiction to Java and C++, Python is preferred all the way. Secondly, Python is quite a powerful language and effective too, when it comes to bulk data and a need to process them faster.

It is basically an open source program which is easy accessible and largely customised. This is really a gift for upcoming world of Data Science. Thus, Python for data analysis is an invaluable skill that you can develop to make yourself marketable like never before.

We hope you liked our post! You can Take A Deep Look On How Machine Learning Boosts Business Growth! and more such topics on our website.


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Statistical Application of R & Python: Know Skewness & Kurtosis and Calculate it Effortlessly

Statistical Application of R & Python: Know Skewness & Kurtosis and Calculate it Effortlessly

This is a blog which shall widen your approach on the Statistical Application using R & Python. You perhaps already have been calculating Geometric Mean using R & Python and are already aware of the Application 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 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 through STATISTICAL 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 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:-


A person tries to analyze last 12months interest rate of the investment firm to understand the risk factor for the future investment.

The interest rates are:

12.05%, 13%, 11%, 18%, 10%, 11.5%, 15.08%, 21%, 6%, 8%, 13.2%, 7.5%.

Here is the table:


(One Year)


Rate (%)


Calculate skewness & Kurtosis in R:

Calculate skewness & Kurtosis in R:
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:

Calculate Skewness & Kurtosis in Python:
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.


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.


There are numerous other blogs that you can follow with Dexlab Analytics. Also, if you want to explore computer vision course Python, neural network machine learning Python and more extensive courses on R & Python, then you can also join us and boost both your passion and career.


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