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8 Skills a Python Programmer Should Master

8 Skills a Python Programmer Should Master

Python has become the lingua franca of the computing world. It has come to become the most sought after programming language for deep learning, machine learning and artificial intelligence. It is a favourite with programmers because it is easy to understand and learn and it achieves a lot more in terms of productivity as compared to other languages.

Python is a dynamic, high-level, general-purpose programming language that is useful for developing desktop, web and mobile applications that can also be used for complex scientific and numeric applications, data science, AI etc. Python focuses a lot on code readability.

From web and game development to machine learning, from AI to scientific computing and academic research, Data science and analysis, python is regarded as the real deal. Python is useful in domains like finance, social media, biotech etc. Developing large software applications in Python is also simpler due to its large amount of available libraries.

The Python developer usually deals with backend components, apps connection with third-party web services and giving support to frontend developers in web applications. Of course, one might create applications with use of different languages but pretty often Python is the language chosen for it – and there are several reasons for that.

In this article, we will walk through a structured approach to top 8 skills required to become a Python Developer. These skills are:

  • Core Python
  • Good grasp of Web Frameworks
  • Front-End Technologies
  • Data Science
  • Machine Learning and AI
  • Python Libraries
  • Multi-Process Architecture
  • Communication Skills

Core Python

This is the foundation of any Python developer. If one wants to achieve success in this career, he/she needs to understand the core python concepts. These include the following:

  • Iterators
  • Data Structures
  • Generators
  • OOPs concepts
  • Exception Handling
  • File handling concepts
  • Variables and data types

However, learning the core language (as mentioned above) is only the first step in mastering this language and becoming a successful Python developer.

Good grasp of Web Frameworks

By automating the implementation of redundant tasks, frameworks cut development time and enable developers to focus greatly on application logic rather than routine elements.

Because it is one of the leading programming languages, there is no scarcity of frameworks for Python. Different frameworks have their own set of advantages and issues. Hence, the selection needs to be made on the basis of project requirements and developer preference. There are primarily three types of Python frameworks, namely full-stack, micro-framework, and asynchronous.

A good Python web developer has incredible honing over either of the two web frameworks Django or Flask or both. Django is a high-level Python Web Framework that encourages a good, clean and pragmatic design and Flask is also widely used Python micro web framework.

Front-End Technologies (JavaScript, CSS3, HTML5)

Sometimes, Python developers must work with the frontend team to match together the server-side and the client-side. This means Python developers need a basic understanding of how the frontend works, what’s possible and what’s not, and how the application will appear.

While there is likely a UX team, SCRUM master, and project or product manager to coordinate the workflow, it’s still good to have a basic understanding of front-end tasks.

Data Science

Data science offers a world of new opportunities. Being a Python developer, there are several prerequisites you need to know starting with things you learn in high school mathematics, such as statistics, probability, etc. Some of the other parts of data science you need to understand, and use include SQL knowledge; the use of Python packages, data wrangling and data cleanup, analysis of data, and visualization of data.

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning (as well as Deep Learning) are constantly growing. Python is the perfect programming language which is used in all the frameworks of Machine Learning and Deep Learning. This will be a huge plus for someone if he/she knows about this domain. If someone is into data science, then definitely digging in the Machine Learning topic would be a great idea.

Python Libraries

Python libraries certainly deserve a place in every Python Developer’s toolbox. Python has a massive collection of libraries, both native and third-party libraries. With so many Python libraries out there, though, it’s no surprise that some don’t get all the attention they deserve. Plus, programmers who work exclusively in one domain don’t always know about the goodies available to them for other kinds of work.

Python libraries are extensively used in simplifying everything from file system access, database programming, and working with cloud services to building lightweight web apps, creating GUIs, and working with images, ebooks, and Word files—and much more.

Multiprocessing Architecture

Multiprocessing refers to the ability of a system to support more than one processor at the same time. Applications in a multiprocessing system are broken to smaller routines that run independently. The operating system allocates these threads to the processors improving performance of the system. As a Python-Developer one should definitely know about the MVC (Model View Controller) and MVT (Model View Template) Architecture. Once you understand the Multi-Processing Architecture you can solve issues related to the core framework etc.

Communication Skills

In best software development firms the teams are made out of amazing programmers which work together to achieve the final goal – no matter if it means to finish the project, to create a new app or maybe to help a startup. However, working in a team means that a developer has to communicate well – not only to get the stuff done but also to keep the documentation clear so others can easily read and follow the thinking path to fully understand the idea.

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Conclusion

In this write-up, we have elaborated on the top skills one needs to have to be a successful Python Developer. One must have a working knowledge of Core Python and a good grasp of Web Frameworks, Front-End Technologies, Data Science, Machine Learning and AI, Python Libraries, Multi-Process Architecture and Communication skills. Though there are a few more skills not listed in this blog, one can achieve success in developing large software applications by mastering all the above skills only.

As delineated in the article, Python is the new rage in the computing world. And it is no surprise then that more and more professionals are opting to take up courses teaching Machine learning using Python and python for data analysis.

 

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AI – A Great Opportunity For Cyber Security Solutions

AI - A Great Opportunity For Cyber Security Solutions

AI and machine learning are the new rage in the computing world. And for reasons justified. With advancement in technology, the threat to technological systems and businesses online has also advanced and become more complex.

Cyber criminals are constantly coming up with newer mechanisms to break into cyber systems for theft or disruption. Thus, the cyber security industry is in a fix over what it can do to enhance security features of existing systems. AI and Machine Learning are the answer to its woes.

Artificial Intelligence and Machine Learning work on large sets of data, analyzing them and finding patterns in them. AI helps interpret data and make sense of it to yield solutions and ML learns up intuitively how to spot patterns in the data. The two go hand in hand and complement each other.

Cybersecurity solutions pivot on the science of finding and spotting patterns and planning the right response to these. They have the ability to tap into data and detect a set of code as malicious, even if no one has noticed it or flagged it before. Thus, it becomes complementary to AI in that it involves the cyber security software to be tutored to detect and alert the user about an anomaly or trigger an alarm if a corruption crosses the threshold without being prompted.

Artificial Intelligence and Machine Learning are used in Spam Filter Applications, Network Intrusion Detection and Prevention, Fraud detection, Credit scoring, Botnet Detection, Secure User Authentication, Cyber security Ratings and Hacking Incident Forecasting.

They are much faster than human users deploying software to detect of fight cyber attacks and they do not tire unlike their human counterparts while assessing tons of data and malicious aspects of those data. They are thus not prone to desensitization that a human user would be prone to.

Application of AI in cyber security solutions is akin to taking things up a notch higher up. Without AI, cyber security would lose the option of having the software learn by itself by merely observing sets of data and user patterns.

An AI system would develop a digital fingerprint of the user based on his habits and preferences. This would help in the event of someone other than the user trying to break into his or her system. And AI cyber security systems do this work 24X7, unlike a human user who would spend limited time scanning for malicious codes or components.

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AI and machine learning, since their inception, have transformed the world of cyber security forever. With time, both aspects of the computing world will refine and mature. It is only a matter of time before a user’s cyber security system becomes tailored to her needs.

And it is thus not surprising that more and more professionals are opting for artificial intelligence courses to equip themselves with relevant coursework. The world is moving to reap the benefits of AI intelligence. So, if you are interested in doing the same, opt for an artificial intelligence course in delhi or a Machine Learning course in India by enrolling yourself with DexLab Analytics.

 

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Automation is to Highly Impact the Knowledge Workers

Automation is to Highly Impact the Knowledge Workers

Automation will mainly target the knowledge workers, who are highly paid and educated and involved in thinking and analytical jobs.

The robot revolution is anticipated for quite some time now and with the ongoing advancements in Machine Learning, Artificial Intelligence and Data Science, the future is near. However, it is also one of the most dreaded events for the workers going forward, who would be vulnerable to losing their respective jobs.

Going back to the 2017 McKinsey study, around 50% of the jobs in the manufacturing industries are automatable using the latest technology. However, according to the latest report, the white-collar workers, who are well-read and engaged in thinking and analytical jobs, are more likely to suffer the most.

According to a new study conducted by Michael Webb, Stanford University Economist, the powerful technologies of computer science like Artificial Intelligence and Machine Learning, which can make human-like decisions and grow using real-time data, will eventually target the white-collar workers. Artificial Intelligence has already made marked intrusions in the white-collar jobs, like telemarketing, which are primarily overseen by the bots. However, with the tireless efforts of the Data Scientists, along with the expansion of the Machine Learning course in India, it is believed to oust the majority of the knowledge workers, like chemical engineers, market researchers, market analysts, physicists, librarians and more.

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The new research focuses on the intersecting subject-noun pairs in AI patents and job descriptions to find out the jobs that will be heavily affected by the Ai technology. For example, the job descriptions of market research analysts comprise of “data analysis”, “identifying markets” and “track market trends”, which are in fact, all covered by the AI patents that are existing. This new study looks far more progressive than the previous ones because it analyzes patents for the technology which are yet to develop completely.

With the rising trends of Data Science and Machine Learning, Artificial Intelligence has really come a long way from what an imaginary concept. Thus, courses like Machine Learning Using Python and Python for Data Analysis, are in heavy demands. 

 

This article has been sourced fromwww.vox.com/recode/2019/11/20/20964487/white-collar-automation-risk-stanford-brookings

 

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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.

Conclusion

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: –
 
https://arxiv.org
https://www.wikipedia.org
 

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A Step-by-Step Guide on Python Variables

A Step-by-Step Guide on Python Variables

Variable is the name given to the memory location where data is stored. Once a variable is stored, space is allocated in memory. Variables are named locations that are used to store references to the object stored in memory.

With the rapid rise of the advanced programming techniques, matching with the pacing advancements of Machine Learning and Artificial Intelligence, the need for Python for Data Analysis an Machine Learning Using Python is growing. However, when it comes to trustworthy courses, it is better to go for the best Python Certification Training in Delhi.

Now, coming to this article, here are some of the topics that will be covered in this article:

  • Rules to Define a Variable
  • Assigning Values to a Variable
  • Re-declaring a Variable in Python
  • Variable Scope
  • Deleting a Variable

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Rules to Define a Variable

These are the few rules to define a python variable:

  1. Python variable name can contain small case letters (a-z), upper case letters (A-Z), numbers (0-9), and underscore (_).
  2. A variable name can’t start with a number.
  3. We can’t use reserved keywords as a variable name.
  4. The variable name can be of any length.
  5. Python variable can’t contain only digits.
  6. The variable names are case sensitive.

Assigning Values to a Variable

There is no need for an explicit declaration to reserve memory. The assignment is done using the equal to (=) operator.

Multiple Assignment in Python

Multiple variables can be assigned to the same variable.

Multi-value Assignment in Python

Multiple variables can be assigned to multiple objects.

Re-declaring a Variable in Python

After declaring a variable, one can again declare it and assign a new value to it. Python interpreter discards the old value and only considers the new value. The type of the new value can be different than the type of the old value.

Variable Scope

A variable scope defines the area of accessibility of the variable in the program. A Python variable has two scopes:

  1. Local Scope
  2. Global Scope

Python Local Variable

When a variable is defined inside a function or a class, then it’s accessible only inside it. They are called local variables and their scope is only limited to that function or class boundary.

If we try to access a local variable outside its scope, we get an error that the variable is not defined.

Python Global Variable

When the variable is not inside a function or a class, it’s accessible from anywhere in the program. These variables are called global variables.

Deleting a Variable

One can delete variable using the command “del”.

In the example below, the variable “d” is deleted by using command Del and when it is further proceeded to print, we get an error “variable name is not defined” which means the variable is already deleted.

Conclusion

In this article we have learned the concepts of Python variables which are used in every program. We also learned the rules associated to the naming of a variable, assigning value to a variable, scope of a variable and deleting a variable.

So, if you are also hooked into Python and looking for the best courses, Python course in Gurgaon is certainly a gem of a course!



This technical blog is sourced from: www.askpython.com and intellipaat.com


 

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An In-depth Analysis of Game Theory for AI

An In-depth Analysis of Game Theory for AI

Game Theory is a branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes. With the rapid rise of AI, along with the extensive time and research we are devoting to it, Game Theory is experiencing steady growth. If you are also interested in AI and want to be well-versed with it, then, opt for the Best Artificial Intelligence Training Institute in Gurgaon now!

Games have been one of the main areas of focus in artificial intelligence research. They often have simple rules that are easy to understand and train for. It is clear when one party wins, and frankly, it is fun watching a robot beat a human at chess. This trend of AI research being directed towards games is not at all an accident. Researchers know that the underlying principles of many tasks lie in understanding and mastering game theory. Both AI and game theory seek to find out how participants will react in different situations, figuring out the best response to situations, optimizing auction prices and finding market-clearing prices.

Some Useful Terms in Game Theory

  • Game: Like games in popular understanding, it can be any setting where players take actions and its outcome will depend on them.
  • Player: A strategic decision-maker within a game.
  • Strategy: A complete plan of actions a player will take, given the set of circumstances that might arise within the game.
  • Payoff: The gain a player receives from arriving at a particular outcome of a game.
  • Equilibrium: The point in a game where both players have made their decisions and an outcome is reached.
  • Dominant Strategy: When one strategy is better than another strategy for one player, regardless of the opponent’s play, the better strategy is known as a dominant strategy.
  • Agent: Agent is equivalent to a player.
  • Reward: A payoff of a game can also be termed as a reward.
  • State: All the information necessary to describe the situation an agent is in.
  • Action: Equivalent of a move in a game.
  • Policy: Similar to a strategy. It defines the action an agent will make when in particular states
  • Environment: Everything the agent interacts with during learning.

Different Types of Games in Game Theory

In the game theory, different types of games help in the analysis of different types of problems. The different types of games are formed based on number of players involved in a game, symmetry of the game, and cooperation among players.

Cooperative and Non-Cooperative Games

Cooperative games are the ones in which the players are convinced to adopt a particular strategy through negotiations and agreements between them.

Non-Cooperative games refer to the games in which the players decide on their strategy to maximize their profit. Non-cooperative games provide accurate results. This is because in non-cooperative games, a very deep analysis of a problem takes place.

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Normal Form and Extensive Form Games

Normal form games refer to the description of the game in the form of a matrix. In other words, when the payoff and strategies of a game are represented in a tabular form, it is termed as normal form games.

Extensive form games are the ones in which the description of the game is done in the form of a decision tree. Extensive form games help in the representation of events that can occur by chance.

Simultaneous Move Games and Sequential Move Games

Simultaneous games are the ones in which the move of two players (the strategy adopted by two players) is simultaneous. In a simultaneous move, players do not know the move of other players.

Sequential games are the ones in which the players do not have a deep knowledge about the strategies of other players.

Constant Sum, Zero Sum, and Non-Zero Sum Games

Constant sum games are the ones in which the sum of outcome of all the players remains constant even if the outcomes are different. 

Zero sum games are the ones in which the gain of one player is always equal to the loss of the other player. 

Non-zero sum games can be transformed to zero sum game by adding one dummy player. The losses of the dummy player are overridden by the net earnings of players. Examples of zero sum games are chess and gambling. In these games, the gain of one player results in the loss of the other player.

Symmetric and Asymmetric Games

Symmetric games are the ones where the strategies adopted by all the players are the same. Symmetry can exist in short-term games only because in long-term games the number of options with a player increases. 

Asymmetric games are the ones where the strategies adopted by players are different. In asymmetric games, the strategy that provides benefit to one player may not be equally beneficial for the other player.

Game Theory in Artificial Intelligence

Development of the majority of the popular games which we play in this digital world is with the help of AI and game theory. Game theory is used in AI whenever there is more than one person involved in solving a logical problem. There are various algorithms of Artificial Intelligence which are used in Game Theory. Minimax algorithm in Game Theory is one of the oldest algorithms in AI and is used generally for two players. Also, game theory is not only restricted to games but also relevant to the other large applications of AI like GANs (Generative Adversarial Networks).

GANs (Generative Adversarial Networks)

GAN consists of 2 models, a discriminative model and a generative model. These models are participants on the training phase which looks like a game between them, and each model tries to better than the other.

The target of the generative model is to generate samples that are considered to be fake and are supposed to have the same distribution of the original data samples; on the other hand, the target of discriminative is to enhance itself to be able to recognize the real samples among the fake samples generated by the generative model.

It looks like a game, in which each player (model) tries to be better than the other, the generative model tries to generate samples that deceive and tricks the discriminative model, while the discriminative model tries to get better in recognizing the real data and avoid the fake samples. It is the same idea of the Minimax algorithm, in which each player targets to outclass the other and minimize the supposed loss.

This game continues until a state where each model becomes an expert on what it is doing. The generative model increases its ability to get the actual data distribution and produces data like it, and the discriminative becomes an expert in identifying the real samples, which increases the system’s classification task. In such a case, each model satisfied by its output (strategy), this is called Nash Equilibrium in Game Theory.

Nash Equilibrium

Nash equilibrium, named after Nobel winning economist, John Nash, is a solution to a game involving two or more players who want the best outcome for themselves and must take the actions of others into account. When Nash equilibrium is reached, players cannot improve their payoff by independently changing their strategy. This means that it is the best strategy assuming the other has chosen a strategy and will not change it. For example, in the Prisoner’s Dilemma game, confessing is Nash equilibrium because it is the best outcome, taking into account the likely actions of others.

Conclusion

So in this article, the fundamentals of Game Theory and essential topics are covered in brief. Also, this article gives an idea of the influence of game theory artefacts in the AI space and how Game Theory is being used in the field of Machine Learning and its real-world implementations.

Machine Learning is an ever-expanding application of Artificial Intelligence with numerous applications in the other existing fields. Besides, Machine Learning Using Python is also on the verge of proving itself to be a foolproof technology in the coming years. So, don’t wait and enrol in the world-class Artificial Intelligence Certification in Delhi NCR now and rest assured! 

 

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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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Statistical Application in R & Python: Negative Binomial Distribution

Statistical Application in R & Python: Negative Binomial Distribution

Negative binomial distribution is a special case of Binomial distribution. If you haven’t checked the Exponential Distribution, then read through the Statistical Application in R & Python: EXPONENTIAL DISTRIBUTION.

It is important to know that the Negative Binomial distribution could be of two different types, i.e. – Type 1 and Type 2. In many ways, it could be seen as a generalization of the geometric distribution. The Negative Binomial Distribution essentially operates on the same principals as the binomial distribution but the objective of the former is to model for the success of an event happening in “n” number of trials. Here it is worth observing that the Geometric distribution models for the first success whereas a Negative Binomial distribution models for the Kth 

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This is explained below.

Type 1 Binomial distribution aims to model the trails up to and including the “kth success” in “n number of trials”. To give a simple example, imagine you are asked to predict the probability that the fourth person to hear a gossip will believe that! This kind of prediction could be made using the negative binomial type 1 distribution. 

Conversely, Type 2 Binomial distribution is used to model the number of failures before the “kth success”. To give an example, imagine you are asked about how many penalty kicks it will take before a goal is scored by a particular football player. This could be modeled using a negative binomial type 2 distribution, which might be pretty tricky or almost impossible with any other methods.

The probability distribution function is given below: 

In the next section, we will take you through its practical application in Python and R. 

Application:

Mr. Singh works in an Insurance Company where his target is to sale a minimum of five policies in a day. On a particular day, he had already sold 2 policies after numerous attempts. The probability of sales on each policy is 0.6. Now, if the policies may be considered as independent Bernoulli trials, then:

  1. What is the probability that he has exactly 4 failed attempts before his 3rd successful sales of the day?
  2. What is the probability that he was fewer than 4 failed attempts before his 3rd successful sales of the day?

So, the number of sales = 3.

The probability of failed attempts is 4.

The success of each sale is 0.6.

Calculate Negative Binomial Distribution in R:

In R, we calculate negative binomial distribution to find the probability of insurance sales. Thus, we get,

  1. The probability that he has exactly 4 failed attempts before his 3rd successful sales are 8.29%.
  2. The probability that he has fewer than 4 failed attempts before his 3rd successful sales is 82.08%.

Hence, we can see that chances are quite high that Mr. Singh will succeed in making a sale after 4 failed attempts.

Calculate Negative Binomial Distribution in Python:

In Python, we get the same results as above.

Conclusion:

Negative Binomial distribution is the discrete probability distribution that is actually used for calculating the success and failure of any observation. When applied to real-world problems, the outcomes of the successes and failures may or may not be the outcomes we ordinarily view as good and bad, respectively.

Suppose we used the negative binomial distribution to model the number of days a certain machine works before it breaks down. In this case, “success” would be the days that the machine worked properly, whereas the day when the machine breaks down would be a “failure”. Another example would be, if we used the negative binomial distribution to model the number of attempts an athlete makes on goal before scoring r goals, though, then each unsuccessful attempt would be a “success”, and scoring a goal would be “failure”.

This blog will surely aid in developing a better understanding of how negative binomial distribution works in practice. If you have any comments please leave them below. Besides, if you are interested in catching up with the cutting edge technologies, then reach the premium training institute of Data Science and Machine Learning leading the market with the top-notch Machine Learning course in India.

 

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Artificial Intelligence Jobs: Data Science and Beyond!

Artificial Intelligence Jobs: Data Science and Beyond!

Artificial Intelligence is the latest technology that the industry of computer science has been working on for quite some time now. Though it has not yet been possible to materialize the high-end AIs, weak/narrow Artificial Intelligence which includes, Siri, Cortana, Bixby, Tesla, are the ones that have grown to be simply inseparable in our daily lives. This is simultaneous with the widespread of the Artificial intelligence Course in Delhiwhich is encouraging more and more students to explore new-age technologies. 

With the extensive research and tests carried out on all these new technologies to implement them in the modern industries; AI is yielding more jobs than ever before.

Jobs Springing from the Artificial Intelligence

Artificial intelligence and data always go hand in hand because it is the data that helps us gain insight into the results. Thus, it is not surprising that the professionals utter AI and data at the same instant.

When Amazon mentioned of up-skilling 100,000 employees from the United States to make them ready for the technology of the age, they also claimed that the machines with the ability to deal with data are responsible for most of these jobs.

There have been huge changes in the figures since then, with the data mapping scientists increased to 832%, the total data scientists jumped by 505%, and the total business analysts hiked about 160%. Besides, there is also a marked demand for the other employees, who are from a non-technological background. However, most of these are associated with Artificial Intelligence, like logistics coordinator and executive; process improvement manager; transportation specialist and so on.

Thus, in contradiction to our surmises that AI and its likes will throttle our jobs and crumble every other our opportunities of the same are turning out to be false for good!

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Drawing to a Close

Whether it is Machine Learning, Data Science or Artificial Intelligence, we are noticing a rapid progress and can easily count on a better future rich with technology. However, with the increasing hardware, software and advanced computing, the need to grasp the pacing technology thoroughly is becoming predominant. Thus, Machine Learning Using PythonNeural Network Machine Learning Python and Data Science Courses in Gurgaon are rising in demand to meet the need of the mass. However, you should always go for the best Artificial Intelligence Training Institute in Gurgaon to imbibe a wholesome knowledge of the subject.

 


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