Artificial intelligence (AI) has been evolving unprecedentedly, with advancements in various subfields leading to novel applications.

One such intriguing area is Generative AI, a subset of AI that focuses on creating new content.

This ranges from text to images, to music, and more. At the heart of this fascinating domain lie Generative Adversarial Networks (GANs), which have taken the AI research community by storm.

Understanding Generative Adversarial Networks(GANs)

Generative Adversarial Networks (GANs) is a novel type of AI model, first proposed by Ian Goodfellow and his team in 2014.

These networks consist of two separate models: a Generator and a Discriminator.

The Generator’s goal is to create data that resembles real data, while the Discriminator’s job is to differentiate between real and fake data. These two models compete with each other, hence the name ‘adversarial’.

The Generator starts off with random noise and begins generating data. Meanwhile, armed with real data, the Discriminator starts the classification task.

As the Generator improves its ability to create increasingly convincing fake data, the Discriminator must up its game to detect the fakes.

The process continues till the Discriminator can no longer reliably tell apart the real from the generated data.

The key strength of GANs lies in their ability to generate highly realistic data. Their success is attributed to the unique adversarial learning process, which drives both the Generator and Discriminator to improve constantly.

Different types of GANs

Since their inception, GANs have evolved into various types, each with its own unique capabilities and applications:

Deep Convolutional Generative Adversarial Networks(DCGAN)

These are one of the first and most popular variations of GANs. DCGANs introduced the use of convolutional layers in the Generator and Discriminator, improving the quality of generated images.

The Deep Convolutional Generative Adversarial Network (DCGAN) stands out among other GAN models for its ability to generate high-quality, realistic images.

The distinguishing characteristic of DCGAN is its utilization of Convolutional Neural Networks (CNNs) in both its generator and discriminator models, a feature that lends itself well to image synthesis tasks.

The generator model creates artificial images by upsampling low-dimensional noise vectors and passing them through a series of CNN layers.

Conversely, the discriminator model, tasked with discerning the authenticity of images, accepts both real and synthetic images as inputs.DCGAN has shown promise in generating high-resolution images that closely resemble their real counterparts.

This is primarily attributed to CNN’s ability to learn intricate features and patterns present in images. Moreover, DCGAN can be trained on large datasets without the risk of overfitting due to its regularization techniques, where it uses dropout and weight decay.

Despite the complexity of the images DCGAN can generate, the model has fewer parameters than traditional GANs, making it easier to train and faster to converge.

DCGAN also outperforms other GAN models regarding stability, making it a preferred choice for researchers. Furthermore, employing multiple layers in the generator network can learn more complex features.

Finally, DCGAN models offer a stable training process, a benefit that allows researchers to experiment with different hyperparameters and architectures. Despite advancements in GAN architectures, DCGAN remains a valuable model for image generation tasks.

It has been effectively utilized in various domains, such as computer vision, art generation, and video game design. As research and development continue, the potential for more realistic and diverse visual content using DCGAN is promising.

Cycle Generative Adversarial Network (CycleGAN) 

CycleGANs are a subset of Generative Adversarial Networks (GANs) that excel in image-to-image translation tasks.

They function by coordinating two neural networks—a generator and a discriminator—in an iterative training process.

The generator produces images that mirror a target style or domain, while the discriminator appraises the resemblance between these produced images and the actual samples from the target distribution.

A distinctive feature of CycleGANs is their ability to train on diverse datasets without the necessity of matched training examples.

This proficiency makes them suitable for style transfer tasks, such as imposing the artistic style of one image onto another with different content.

Additionally, CycleGANs have been employed for domain adaptation, which involves modifying models trained on one dataset to perform efficiently on a different yet related dataset.

An instance of this is converting synthetic medical images into realistic patient scans, thereby enhancing diagnostic precision in medical imaging.

A significant innovation of CycleGANs is the incorporation of cycle consistency loss. This function guarantees that the generator’s output image can be mapped back to the original input with minimal deviation, thereby maintaining the consistency and realism of the images generated.

This advancement has mitigated many of the limitations associated with conventional GANs, leading to new possibilities in computer vision and machine learning applications.

CycleGANs have emerged as a powerful tool in the field of image-to-image translation due to their ability to generate realistic and consistent images.

They have demonstrated their effectiveness in various applications, such as style transfer and domain adaptation. As this technology continues to evolve, it promises to significantly transform our digital realm.

Style-based Generative Adversarial Network (StyleGAN)

The Style-based Generative Adversarial Network (StyleGAN), introduced by NVIDIA researchers in 2018, constitutes a significant advancement in the field of generative modeling.

The novelty of StyleGAN lies in its generator architecture, designed to produce high-quality images with realistic nuances.

It surpasses traditional GANs by disentangling image style and content representations via a learned mapping function, thus enabling the generation of images with fine details.

The generator network comprises two parts: a mapping network, which learns a latent code representation for an input image, and a synthesis network, which generates the output image from the said latent code.

This architecture offers considerable advantages, such as precision control over generated images’ styles and scalability to higher resolutions compared to other generative models.

As a result, StyleGAN generates highly realistic faces resembling real-world photographs closely, making it a valuable tool in applications like fashion design and video game graphics.

A key feature of StyleGAN is its ability to manipulate various layers within the generator, offering flexibility in generating diverse images and fine-tuning specifics like facial expressions or clothing styles.

StyleGAN’s ability to generate high-resolution images makes it ideal for digital art, advertising, and video game development. Additionally, its capacity to learn from large datasets allows it to create realistic-looking images with minimal human intervention.

StyleGAN employs a progressive, growing approach to generate large-scale, high-resolution images with impressive fidelity.

Traditional GAN models often struggle with high-resolution image generation due to computational power and memory limitations.

However, StyleGAN’s progressive, growing approach addresses these issues and enhances its image-generation capabilities.

StyleGAN’s proficiency in generating highly-detailed facial features has positioned it as a popular choice among researchers in facial recognition technology.

It can create photorealistic faces with detailed textures and expressions, resembling real human faces closely, which is pivotal in advancing AI systems for security and surveillance purposes.

Advantages and disadvantages of GAN

Generative Adversarial Networks (GANs) have demonstrated the potential to produce diverse and realistic outputs.

They can fabricate unique instances similar to the original data, useful for tasks requiring various outcomes like image synthesis.

Furthermore, GANs can generate realistic derivatives that find applications in the design and entertainment industries and can help protect sensitive information in datasets.

GANs also show a remarkable propensity for creativity, pushing the boundaries of conventional art and music, thereby opening new avenues for human-machine collaboration.

Now, Despite their capabilities, GANs present certain challenges. Training instability arises from the non-linear nature of the loss function used in GANs, leading to difficulties in model convergence.

Mode collapse, where the generator produces a limited variety of outputs, can be mitigated but requires additional computational resources and expertise.

The extensive training duration for GANs, potentially increased by certain modifications, is another significant consideration.

Researchers must judiciously weigh these trade-offs when deciding on using GAN-based methodologies.

Future of GANs and potential impacts

Challenges of GANs include the high computational requirements and the issue of mode collapse.

Mode collapse is a situation where the generator creates images that the discriminator cannot classify, even when those images don’t look like they should be based on the data distribution.

This inhibits the generator’s learning as it is not getting proper feedback from the discriminator​.

Moreover, the training of GANs is computationally intensive, requiring significant memory resources, which poses a considerable challenge.

GANs also need substantial computational power, as they often consist of large and complex models that necessitate a lot of communication and memory.

This strain on system resources is further exacerbated when training on GPU machines due to their limited memory​.

Despite the challenges, GANs have great potential to generate realistic real-time video sequences. For instance, they can be used to generate interactive environments for video games.

However, video synthesis using GANs, which involves generating a series of images, has higher memory and computational requirements​​.

GANs have also been used in drug discovery. Still, such applications necessitate even higher infrastructure requirements due to the need for a reinforcement learning component in addition to the dueling networks​.

For GANs to filter into areas beyond image and video generation and broader use cases in scientific, technical, or enterprise realms, hardware, and software limitations must be resolved.

According to Bryan Catanzaro, VP of Applied Deep Learning at Nvidia, it’s still somewhat early to say that GANs will soon filter into these other domains, despite the interest.

The most success with GANs has been seen in the visual domain, such as in medical imaging​.

While there is hope for GANs to be deployed in more real-world applications beyond image and video in gaming or content generation, some maturity is still needed on both sides of the platform.

Despite these challenges, ongoing research, optimizations, and tweaks could soon bring GANs closer to being a standard technology​.

The advancement of Generative Adversarial Networks (GANs) brings about the potential for sophisticated applications but also ethical considerations.

The capacity of GANs to generate realistic images and manipulate data has profound implications for privacy and security.

It’s critical that developers, researchers, and policymakers address these ethical concerns and ensure responsible usage of these technologies.

As GANs evolve, we must not overlook their potential risks, including misusing generated content for harmful activities like propagating fake news and biases reflected in training data.

Addressing these concerns requires researchers to maintain transparency about their methods and discoveries, reduce bias in their datasets, and consider potential adverse consequences before releasing any new technology.

By prioritizing ethics, we can ensure GANs are utilized for beneficial purposes rather than causing harm.

Conclusion

Generative Adversarial Networks represent an exciting frontier in artificial intelligence, pushing the boundaries of what machines can create.

While they have the potential to revolutionize many areas, it’s important to note that they also come with their own set of challenges.

The computational power and memory required to train these models are currently significant, posing hurdles for their more comprehensive application.

Despite these challenges, the capabilities of GANs are compelling and warrant concerted efforts toward their development and application.

As we look to the future, we should remain mindful of the ethical implications of these advancements.

The ability of GANs to generate realistic content could potentially be misused, necessitating careful consideration and regulation.

But with the right approach, Generative Adversarial Networks could hold the key to many exciting advancements in artificial intelligence.

The post Generative Adversarial Networks appeared first on Anupinder Singh.

It was a long tiring day with a variety of stuff done for streamlining my thoughts into meaningful work.

In the last hour listening to a couple of songs with Lofi version, such soul-touching music that evokes emotions that the words alone cannot express.

As the song progresses, I find myself lost in the music, my mind and body fully immersed in the experience.

The world around me fades away, and all that exists is the music and me.

At this moment, I feel truly alive, connected to something greater than myself.

It’s moments like these that remind me of the power of music and something that I have been neglecting due to demanding work responsibilities.

#Music has the ability to transport us to a place of pure emotion and connection, to touch our souls in a way that nothing else can.

And for that, I am grateful for this moment

The post Music for the Soul appeared first on Anupinder Singh.

The most basic programming languages used are python, java, C++, etc.

And learning programming has become a need of time, yet the most intimidating question is “How to get started with programming.”

So, in this blog, I have tried to share my view on how a beginner-level person can start with programming by answering the most commonly asked questions.

What is Programming?

Programming is a highly valued skill in today’s world, and it is an essential tool for digital innovation.

It is a procedure or method which guides us to write programs using a specific computer programming language that can automate tasks, solve problems, and create digital experiences.

Using the computer programming language, we are writing instructions to perform a specific action. The instructions are executed in a sequence to complete the particular activity.

Programming skill opens up so many possibilities and allows us to manipulate data in creative ways.

It is a great way to get creative with technology and make something truly unique.

What do I Need?

Deliberate practice and determination to persevere even when the going gets tough.

Choosing a Programming Language

Programming is a fascinating and rewarding field to pursue, and the journey begins with choosing a programming language.

With dozens of languages available to learn, it can be daunting for beginners to decide which language to choose.

Before deciding on a language, several factors need to be taken into consideration.

The first factor is what type of project you want to create.

If you’re interested in web development, it’s best to select a web-oriented scripting language such as HTML5 or JavaScript.

For mobile applications, Java or kotline is an excellent choice – it’s widely used within the industry and is one of the top languages for Android development.

On the other hand, if you’re looking for something more general purpose, then Python could be a great option – it’s easy to learn and has vast amounts of libraries available that can make development easier and faster.

Learning Programming Basics

Learning programming basics is essential for anyone who wants to get started with coding.

With a few foundational principles, it’s possible to create robust programs and applications.

Learning the basics of programming can open up opportunities in web design, software development, and other technology fields.

The first step to learning programming is understanding the fundamentals. This includes concepts like variables, data types, control flow, and loops.

Once these concepts are grasped, you can start writing your own code using a variety of languages, such as Python or JavaScript.

Additionally, there are plenty of online resources available that provide helpful tutorials on getting started with coding.

It may take some time before you’re able to write complex programs but mastering the basics will set you up for success in the long run.

Setting Up a Programming Environment

Setting up a programming environment can seem intimidating, but it doesn’t have to be.

Whether you’re just starting with coding, or an experienced programmer looking to switch over to a new language, there are some essential steps you need to take in order to get your development environment up and running.

Setting this foundation is key for successful coding projects, so here’s what you need to know about getting started.

First, identify the type of language that best suits your needs.

Different languages are suited for different applications, so understanding the basics of each language is essential in making sure you pick the right one for your project.

Once you’ve chosen a language, download its compiler and any other necessary tools or software packages from its official website.

Installing this software gives you access to all the necessary tools that will help run and debug code written using that language.

Finding Resources and Support for learning to program

Learning to program can be an intimidating and overwhelming process for someone who has no prior knowledge or experience in coding.

However, with the right resources and support network, anyone can learn how to develop software and create amazing projects.

Fortunately, there are lots of resources available online that make it easy for newcomers to get started with programming.

There are plenty of tutorials, forums, books, and video courses that provide detailed instructions on how to code in different languages.

Additionally, joining online communities like Reddit or Stack Overflow is a great way to connect with experienced coders who are willing to help newbies out.

It’s also important not to forget about reaching out locally; many cities have coding boot camps and meetups where people interested in learning programming can go for extra guidance and peer-to-peer support.

Conclusion: Get Started with programming!

Programming can be an intimidating prospect for beginners, but with the right steps and resources, anyone can become a coder.

Learning to code is essential in today’s world and can provide many career opportunities.

In this article, we have discussed the basics on how to get started with programming.

First, it’s essential to select the language that best suits your needs and interests.

Once you have decided on a language, research helpful resources like tutorials and books that will give you a solid foundation in coding concepts.

Then take action! Start writing code and experiment while implementing various sample or real-life use cases; this is key to learning programming successfully.

Also, Utilize online coding communities or mentorship programs if you feel stuck or need extra help understanding concepts.

Finally, keep practicing!

The post How to get started with programming? appeared first on Anupinder Singh.

Deep learning is a machine learning approach that teaches the machines to learn by examples and experience.

It is a technique where machines acquire skills without human intervention.

What is Deep Learning?

It can be described as a machine learning model which enables the computer to perform classifications based on images, text, sound, etc.

These deep learning models are trained with a large amount of data and neural network architectures which may contain multiple layers.

As a result, the deep learning models are able to achieve a state of the art accuracy, which may exceed human-level performance in some scenarios. 

Machine Learning vs Artificial Intelligence vs Deep Learning: Are all of them are same?

Artificial Intelligence is a generic term that refers to procedures that enable computers to imitate human nature. Machine Learning can be described as a set of algorithms that are trained on data to increase their performance.

Whereas, Deep Learning is a machine learning technique that is inspired by human brain structure. It uses a multiple layered model framework called a neural network.

Deep Learning is a subset of Machine Learning, which is a subset of Artificial Intelligence. This can be understood by the Venn diagram given below:

Why is deep learning so popular?

Deep Learning models provide accurate results which enable consumer electronics to meet user expectations. Also, accuracy is important in the case of safety-critical products like self-driving cars.

Following are the reasons which make the deep learning models more accurate than ever:

• Deep learning models are trained using massive amounts of labelled data like the self-driving car models are trained using zillions of images and videos.
• Deep Learning consumes high computing power like good quality GPUs, powerful clusters and cloud computing. These high quality computing machines help the deep learning models to lower the training time.

Another reason why these models have gained so much popularity is that they do not require the feature extraction step.

The traditional machine learning models like Logistic regression, Decision trees, SVM, etc. cannot be used on raw data directly. They require a separate preprocessing step called feature extraction.

On the other hand, the artificial neural networks used in Deep Learning do not require the feature extraction step.

In other words, the feature extraction step is part of the process which takes place in the artificial neural network.

How does deep learning work?

Deep Learning models implement Artificial Neural Networks which imitate the way the human brain computes information.

The training process involves unknown elements in the input distribution to extract features and discover useful data.

This training process occurs on multiple levels results for accurate computations.

No model is considered perfect, we need to choose the algorithms depending upon the nature of the task to be performed.

Gaining a proper understanding of all the elementary algorithms is required to choose the relevant algorithm.

Deep learning algorithms

It is the fastest-growing tech and In order to implement it, learning about various models is mandatory.

There are two types of models in deep learning: supervised and unsupervised.

Supervised models are trained using examples of a labelled dataset, i.e. the algorithm can use an answer key to evaluate the accuracy of training data.

Whereas in unsupervised models, unlabeled data is used and the algorithms try to gather information by extracting features and patterns on their own.

Supervised Models 

Convolutional Neural Networks (CNNs)

The Convolutional Neural Network or CNN is built to handle a large amount of complexity for pre-processing and computation of data.

It is an advanced and more powerful variation of the classic artificial neural networks. They were developed for image detection and for image classification problems.

When to use the CNNs:

• While using Image Datasets            
• OCR document analysis                  
• When the model requires high complexity in computing the output   
• When the input data is 2-D but can be transformed to 1-D internally for rapid processing.

Classic Neural Networks (Multilayer Perceptrons)

The singular nature of the Classic neural networks helps it to adapt to the elementary binary patterns through a series of input, resembling the learning patterns of a human brain.      

When to use the Classic Neural Networks:

• Classification problems where the set of real values is given as input.
• Tabular dataset, in the form of rows and columns i.e. the CSV files.

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks or RNNs were discovered to be used for predicting sequences. LSTM or Long short-term memory is a renowned RNN algorithm with various possible use cases.

When to use the RNNs:

• One to one mapping: a single input mapped to a single output, example: Image classification.    
• One to many mapping: a single input mapped to a sequence of outputs, example: Image captioning i.e. multiple words from a single image.        
• Many to one mapping: A sequence of inputs produces a single output, example: Sentiment Analysis i.e. binary output from multiple words       
• Many to many mapping: A sequence of inputs produces a sequence of outputs, example: Video classification i.e. splitting the video into multiple frames and labelling each frame separately.

Unsupervised Models

Boltzmann Machines

Boltzmann machines, unlike the above models, do not follow any certain direction. Direction here means input layer→ hidden layer → output.

These machines have nodes connected to each other in a circular fashion of hyperspace like in an image.

When to use the Boltzmann Machines:

• While working with a very specific set of data      
• To build a binary recommendation system             
• Monitoring a system

Self-Organising Maps (SOMs)

Self-Organising Maps or the SOMs use unsupervised data and help with reducing the random variables present in the model (dimensionality reduction).

The output produced is always two dimensional for a self-organising map.

When to use the SOMs:

• When the data does not have an output or Y column          
• Creative projects like music, text, videos, etc. produced by Artificial intelligence Dimensional reduction for feature detection                 
• Exploring the projects to understand the framework behind the dataset

AutoEncoders

The autoencoders work by automatically encoding the data based on the input values, followed by an activation function and then finally decoding the data as output. 

When to use the AutoEncoders:

• Feature or dimensionality detection        
• Building powerful recommendation systems       
• Performing encoding on massive datasets.

Deep learning Applications

Deep Learning, a subset of machine learning, has become a buzzword in the field of artificial intelligence.

It enables the computers to learn from past experiences and examples, helping them to solve complicated problems without human involvement.

What exactly are the problems being tackled by deep learning?

Following are a few major examples where deep learning plays an important role:

Healthcare: Deep learning is a fast-growing trend in the healthcare industry. The sensors and devices that provide real-time data about patients like overall health condition, heartbeat count, blood sugar level, etc. use deep learning. Apart from this, the pharmaceutical companies also implement these algorithms for disease detection, image segmentation, etc. 

Virtual Assistant: Virtual assistants have various applications nowadays. They act like chatbots, online training instructors, etc. The main area of application of virtual assistants is speech recognition, text to speech recognition, and vice versa using natural language processing. All this is possible due to deep learning. Siri, Alexa, Cortana, Google Assistant, etc. are some of the most popular virtual assistants.

Social Media: Deep learning helps Twitter to enhance its performance. These models access and analyse a lot of data in order to learn about user preferences. Not only this, Facebook uses it to improve its user experience by recommending relevant pages, posts, friends etc. In addition to this, Instagram uses its models to prevent cyberbullying and eliminate controversial comments.

Chatbots: Chatbots help in solving customer problems in just a few seconds using Artificial Intelligence to chat via text or text to speech. Chatbots help in consumer interaction, marketing on social media platforms, and instant response to clients. They use machine learning and deep learning models to generate various types of reactions.

Self-driving cars: Self-driving cars operate using machine learning and deep learning algorithms.

“Self-driving cars are the natural extension of active safety and obviously something we should do”. -Elon Musk.

They are able to detect objects near the car, understand the traffic signals, detect the distance between the car and the other vehicles, etc. Tesla is the most renowned self-driving car in the market.

Limitations and Challenges

Although deep learning is an expanding technology in various domains. It comes with a number of limitations and challenges:

• A large amount of data is required to train the models to achieve accurate results.
• Training the deep learning models is a bit expensive since high quality GPUs and hundreds of powerful machines are required.
• There is no predefined framework to help in selecting the relevant deep learning tools. As a result, adopting deep learning skills becomes difficult.
• The data needs to be cleaned before applying any algorithm on it. Irrespective of how efficient the model is, without data cleansing, it will deliver inaccurate results

Conclusion

With the increase in the deployment of big data, deep neural network architecture and computational power, the conventional predictive models have improved in terms of accuracy and efficiency.

The number of organizations adopting big data and advanced technologies like artificial intelligence, machine learning, the Internet of things, etc. have grown and will continue to grow in the near future. 

The post Deep Learning- A brief Introduction appeared first on Anupinder Singh.

Garbage in, garbage out is the motto that needs to be followed to build an accurate machine learning model.

If the data under analysis is not accurate, then it is not useful. Irrespective of how accurate your model is, without data cleaning, it will deliver biased and inaccurate results.

Thus, data cleaning, also called data cleansing or data scrubbing, is one of the most crucial parts of machine learning.

What is data cleaning?

Data cleansing can be understood as a process of making the data ready for analysis.

Eliminating null records and unnecessary columns, fixing the outliers (junk values), restructuring the data to enhance its readability, etc. are some of the components of data cleaning.

Data cleaning also focuses on increasing the accuracy of the dataset by rectifying the existing information, instead of just removing chunks of useless data.

Steps involved in data cleaning

There is no particular procedure for data cleaning, it varies from one dataset to another. However, having a roadmap is essential to keep you on the right track.

Given below are the basic steps which can be followed to create a template for your data cleaning process.

Eliminating duplicates and irrelevant observations

• Duplicate or redundant values affect the efficiency of the model to a large extent.  The data is repeated and may add towards either the correct side or incorrect side, thereby giving biased results. 

• The irrelevant data do not add any value to the dataset, thus should be dropped or removed to save resources like memory and processing time.

Rectifying structural errors

• Structural errors include inconsistencies in naming conventions, typos, and wrong capitalization. These typographical errors result in mislabeled classes or categories. 
• For instance, the model might treat “NA” and “Not Applicable” as two different categories, though they represent the same value. These structural variations make the algorithms very inefficient resulting in unfaithful results.

Filter out the irrelevant outliers

• Outliers are the values that do not fit in the dataset under observation. These values can be understood as the noise in the dataset.
• Outliers arise due to manual errors or data entry mistakes. The Outliers are not always incorrect, so they should not be dropped until we have a valid reason.

Handling missing data

Handling missing values is the trickiest step in the data cleaning process. The missing values can’t be ignored or eliminated since they can represent something crucial. 

Following are a couple of the most common methods to deal with the missing data:

• Removing the observations having missing values, but might result in losing some useful information.
• Imputing the missing values based on the previous observations. Since it is based on assumptions and not actual observations, it does not add any value to the dataset and may result in losing the data integrity.

Some data cleansing tools

Data cleaning is the most important step in machine learning to get accuracy and efficiency.

Performing data cleansing on zillions of data manually is tedious and may result in errors.

This makes the data cleaning tools prominent since they help in keeping a large amount of data clean and consistent.

Openrefine, TIBCO Clarity, Trifacta Wrangler, IBM Infosphere, Cloudingo, Quality Stage, etc. are some of the most popular data cleaning tools.

Conclusion

Working with clean data comes with a lot of advantages like improved efficiency, reduced error margin, accuracy, consistency, better decision making, and many more.

Thus, the data should be cleansed before fitting any model with it.

If you want to invest in Data cleaning then you can learn by implementing it using Python or R.

The post Data Cleaning in a Nutshell appeared first on Anupinder Singh.

One of the greatest realizations that I had experienced during my sessions is that for an effective content delivery it first is required to understand the learners need and learning style.

Generally, the needs can be initially guessed based on the objectives of the course/training, but for learning styles, the guesswork will not contribute, so it is really necessary to do a pre-survey with the intended audience.

This pre-survey always helped me to understand how the group is comprised off based on: Visual, Auditory, Reading/Writing and Kinesthetic.

This pre-survey is a great tool for planning content delivery with required demonstrations, reading materials and practices for participants. And the effectiveness that I received with this approach is far better than the guesswork that I did in my initial years of teaching/training.

Would love to hear your say on this?

The post Learners Style of learning! appeared first on Anupinder Singh.

Why this created the buzz?

Because it reduced the intensive logic implementations for processing the massive quantity of data(generally known as big data), and the results are promising in terms of finding patterns in the data resulting in better business-oriented decisions.

Now, as a beginner, the concept of Machine learning could be overwhelming as there has been plenty of scattered information available across the web, including various theoretical courses and proprietor documentations.

So, here I will try to get you a simple flow on how as a beginner, you can get your self to familiarize yourself with the machine learning domain and where you can start looking at in the first place.

The formal definition could be:

Machine learning(ML) is a field of computer science concerned with programs that learn as well as is concerned with the question of how to construct computer programs that automatically improve with experience.

Now you might also be thinking about how artificial intelligence is different from machine learning, so here is a big picture for you.

Here you can see that machine learning is the subset or, in fact, the more specialized form of artificial intelligence. And, further supports the deep learning domain for more intense & intelligent applications.

Now the next point to understand is why do we want the computer programs to improve with experience. it’s because:

we have huge data and we want to make decisions or predictions from it

AND

we want computers to learn to identify patterns without being explicitly programmed to

And as said, DATA is the new currency for this digital world and is priceless. Therefore, it’s essential to utilize it to achieve the unique potential for your business.

Great, you know why it is essential for computers to improve.

Now, as a programmer, what should you know So that this automation can be achieved.

Types of machine learning

Broadly there are three

Supervised Learning

This is simplest to implement, where primarily the problems related to regression and classification are solved. And the most important is that the Data available for analysis is available in a structured way with minimum anomalies, and even if anomalies are present, they can be rectified by using statistical measures.

General use cases that are implemented under this: Image classifications, Fraud detections, weather/market forecasting, etc. So you can simply infer that where ever the simple predictions are supposed to be done that Supervised learning.

Unsupervised Learning

This is again working on the same objective of prediction, but the complexity is increased. Because the data available for analysis is either minimally structured or totally unstructured. Therefore the added process of Clustering or Dimensionality Reduction is required to be performed before the process of predications can be put in place.

So this requires more insights into the working concepts of statistical procedures and is the next stage of learning in ML. The general use case implementations can be Customer segmentation, recommender system, Feature discoveries, etc.

Reinforcement Learning

This is basically leveraging the power from both the supervised and unsupervised procedures with an addon factor of iterative learning if some error occurred(mispredictions) in the data interpretations.

The procedures(algorithms) implemented in this system are designed in such a way so that it can tune their attributes/parameters(variables) to test it against the variety of values and find the best combinations, for example, neural networks have a variety of parameters like the number of layers, the number of neurons in each layer, connection density between neurons, weights, etc.

The general use cases for such types of implementations are Robot navigation, learning tasks, game AI, self-driving cars, etc.

The interesting point is that corresponding to each type of learning there have been plenty of algorithms published as APIs under the various opensource ML libraries such as skLearn, Keras, Tensorflow, etc. and for data management is working memory(RAM) the primary libraries used are panadas and Numpy.

Here is a webinar discussion on the machine learning types and relevant stuff

So, as a programmer, it has become very easy for you to implement your use cases, provided you know what problem you are trying to solve and what data you will be using along with which algorithm you are going to use and which library supports it.

Machine Learning implementation steps

1. Defining your problem statement
2. Getting data from various sources and pre-processing it for feeding to the selected algorithm(s).
3. Model building by selecting the right ML algorithm and test it with data.
4. Optimize and improve(this requires a repeat of step 2 and step 3 till satisfactory results were produced)
5. Summarize the results/Tell a story by using various Data visualizations.

That would be it if you followed these steps you are through with your ML implementation work.

Now the next point is how do I know which library to look into and which language shall be learned so that the implementation can be hassle-free.

Possible Machine learning track

• Choose a programming language: Python OR R programming. I would prefer to have a python as a beginner as it’s easy to follow, and many libraries are supported by the ML community are programmed using Python. Apart from this should CRUD skills for SQL. Also, it is not like that you required to be an expert in programming skills that you will become as you practice your work.
• Practice your data processing/wrangling using Pandas & NumPy. Also, you should practice with the Matplot library to get yourself familiarised with the data visualizations using various charts.
• Now, as you are through with the first two stages, it is time to open your wings and get your hands dirty with algorithms from sklearn/Keras libraries or any other of your interest as per your problem statement. Take your time to work on various small implementations, start with regression-based algorithms, then classification, clustering, and so on. Spend some good time practising these as this will lay the foundation for your enterprise career.
• So finally, it’s time to move on to the enterprise solutions used by the industry for processing real-time data like presto, HIVE, Hadoop, AWS ML toolkits, SPARK, etc.

Moreover, apart from what all is mentioned above, each specific cloud service provider has its own service stack to support the machine learning environment within its platform. And it is always up to your inclination toward the provider, and you additionally learn their platform-dependent tools over and above what we have discussed.

In case if you have a different say or have something to discuss, feel free to start the discussion thread below. I would love to do so.

Who am I to teach you about machine learning?

Well, I have been working intensively in ML to solve my Ph.D. Research problem and have been through various ML projects to test out multiple hypotheses.

Apart from this, I have been mentoring the budding researchers working on finding solutions to complex problems in the cloud computing domain.

You may read my brief career progress on the About page or check my LinkedIn.

Look forward to having you in the webinar and have a great discussion.

Cheers!
Anupinder

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You will be surprised to know that it was first used in the early 1970s and was introduced by IBM to virtualize its mainframe systems through hardware virtualization, which is still a popular mode of service delivery.

Let’s start with a fundamental question

What is virtualization?

RedHat documentation really defined it well.

Virtualization is a technology that allows you to create multiple simulated environments or dedicated resources from a single, physical hardware system. Software called a hypervisor connects directly to that hardware and allows you to split 1 system into separate, distinct, and secure environments known as virtual machines (VMs). These VMs rely on the hypervisor’s ability to separate the machine’s resources from the hardware and distribute them appropriately.

https://www.redhat.com/en/topics/virtualization/what-is-virtualization

So the virtualization clearly allows the web hosting and data center service providers to make the best use of their hardware investments and maintaining optimal operational cost.

All thanks to this, service providers were able to pass on the benefits to their users/customers with low-cost services and offer a low barrier to entry for new customers.

Virtualization technology leverages unprecedented benefits as follows:

• Increased performance and optimized compute capacity sharing.
• Optimizing under/over utilized hardware and software resources.
• Reduced indirect carbon emissions.

Virtualization technology has a long history of development and has gone through various phases of research, development, and implementation. In fact, there has been a couple of research projects administrated to support the simulation of cloud-based virtualization systems. These systems model the real-world cloud behavior to develop state of the art optimization policies.

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I am using the same to test my first blog and display it to the world and presenting my self to the digital world.

This blog intend to be a digital version of real life Anupinder Singh and will share my learning based on my personal or professional experiences.

With the hope of reaching new heights, here is my first blog test!

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