Show Posts

This section allows you to view all posts made by this member. Note that you can only see posts made in areas you currently have access to.


Messages - Asif Khan Shakir

Pages: [1] 2 3 ... 8
2
You can compare a neural network to a chess game with a computer. It has algorithms, according to which it determines tactics, depending on your moves and actions. The programmer enters data on how each figure moves into the computer’s database, determines the boundaries of the chessboard, introduces a huge number of strategies that chess players play by. At the same time, the computer may, for example, be able to learn from you and other people, and it can become a deep neural network. In a while, playing with different players, it can become invincible.

The neural network is not a creative system, but a deep neural network is much more complicated than the first one. It can recognize voice commands, recognize sound and graphics, do an expert review, and perform a lot of other actions that require prediction, creative thinking, and analytics. Only the human brain has such possibilities. The neural network can get one result (a word, an action, a number, or a solution), while the deep neural network solves the problem more globally and can draw conclusions or predictions depending on the information supplied and the desired result. The neural network requires a specific input of data and algorithms of solutions, and the deep neural network can solve a problem without a significant amount of marked data.

4
Nice

5
Informative

6
Software Engineering / The Data Science Puzzle — 2020 Edition
« on: February 20, 2020, 08:18:04 PM »
With a new year upon us, let's take a fresh look at the current state of the data science puzzle. What are the most important constituent concepts of the data science landscape? How do they fit together? Which of these have been elevated in importance since the previous installment, and which are less important?

As a few years have passed since I last treated this particular topic, it might be worth having a look at this out of interest, and for comparison. We will proceed by first looking at the concept definitions from last time, and then look at how things have changed since then.

We start with the perceived original driver of the data science revolution, big data. What I said in 2017:

Big Data is still important to data science. Take your pick of metaphors, but any way you look at it, Big Data is the raw material that [...] continues to fuel the data science revolution.

As relates to Big Data, I believe that justification of data-acquisition and -retention from a business point of view, expectations that Big Data projects start providing actual financial returns, and the challenges related to data privacy and security will become the big Big Data stories not only of 2017 but moving forward in general. In short, it's time for big returns from, and big protections for, Big Data.

However, as others have opined, Big Data now "just is," and is perhaps no longer an entity deserving of the special attention it has received for the better part of a decade.

While I don't condone the capitlization of most key terms in general, "big data" seemed to previously demand this treatment given its near-fabled status and brand name-like station. Notice this time around I have reneged this status, which goes hand in hand with the idea that big data is no longer top level data science terminology. As alluded to in the final sentence, moving forward big data is simply "data," and we could reword part of that excerpt to read, "data is the raw material that continues to fuel the data science revolution."

Look, at this point we should all be aware of how important data is to the process of data science (it's right there in the name). Whether our data is big or small or lies somewhere else on the data sizing spectrum really doesn't require distinguishing from the outset. We all want to science the data and provide value, whether the data is a lot or a little. "Big data" may provide us with more or unique opportunities for the types of analytics and modeling to employ, but this seems akin to distinguishing the size of our nails from the get-go just so we know what size and type of hammer to bring along for a given job.

Data is everywhere. Much of it is big. It's time we stop emphasizing so, just like it's time we stop saying "smart" phone. The phones are all basically smart now, and making special note of it really says more about you than it does about the phone.

One thing I stand by, however, is that the challenges related to data privacy and security will only grow in importance as the years march on, and we can add ethics into that mix as well, though seriously treating these topics is beyond the scope of this article.

Here's what I said about machine learning as a component of data science last time:

Machine learning is one of the primary technical drivers of data science. The goal of data science is to extract insight from data, and machine learning is the engine which allows this process to be automated. Machine learning algorithms continue to facilitate the automatic improvement of computer programs from experience, and these algorithms are becoming increasingly vital to a variety of diverse fields.

I stand by this, and would only make the argument that machine learning is more than one of the primary technical drivers of data extraction, it is the the primary technical driver.

There are a variety of aspects to data science; we are discussing a number of them in this very article. However, when thinking about extracting insight from data which cannot be seen with the "naked eye" via descriptive statistics or the visualization of these stats or some type of business intelligence reporting — all of which can be very useful and provide invaluable illumination in the proper circumstance — machine learning is the natural path to take, a path which has automation baked in.

Machine learning is not synonymous with data science; however, given the reliance on machine learning to extract insight from data, you can forgive the many who often make this mistake.


9
Faculty Sections / Re: AUPF New - 2019
« on: February 20, 2020, 08:03:29 PM »
Nice

11
MCT / Re: History of Photography-05
« on: February 20, 2020, 08:02:37 PM »
nice

12
Informative

14
Software Engineering / Top 5 must-have Data Science skills for 2020
« on: February 20, 2020, 07:52:08 PM »
Data Science is a competitive field, and people are quickly building more and more skills and experience. This has given rise to the booming job description of Machine Learning Engineer, and therefore, my advice for 2020 is that all Data Scientists need to be developers as well.

To stay competitive, make sure to prepare yourself for new ways of working that come with new tools.

 

1. Agile
Agile is a method of organizing work that is already much used by dev teams. Data Science roles are filled more and more by people who’s original skillset is pure software development, and this gives rise to the role of Machine Learning Engineer.More and more, Data Scientists/Machine Learning Engineers are managed as developers: continuously making improvements to Machine Learning elements in an existing codebase. For this type of role, Data Scientists have to know the Agile way of working based on the Scrum method. It defines several roles for different people, and this role definition makes sure that continuous improvement and be implemented smoothly.

 

2. Github
Git and Github are software for developers that are of great help when managing different versions of software. They track all changes that are made to a code base, and in addition, they add real ease in collaboration when multiple developers make changes to the same project at the same time.With the role of Data Scientist becoming more dev-heavy, it becomes key to be able to handle those dev tools. Git is becoming a serious job requirement, and it takes time to get used to best practices for using Git. It is easy to start working on Git when you’re alone or when your co-works are new, but when you join a team with Git experts and you’re still a newbie, you might struggle more than you think.

3. Industrialization
What is also changing in Data Science is the way we think about our projects. The Data Scientist is still the person who answers business questions with machine learning, as it has always been. But Data Science projects are more and more often developed for production systems, for example, as a micro-service in a larger software. At the same time, advanced types of models are getting more and more CPU and RAM intensive to execute, especially when working with Neural Networks and Deep Learning.

In terms of job descriptions of a Data Scientist, it is becoming more important to not only think about the accuracy of your model but also take into account the time of execution or other industrialization aspects of your project.

4. Cloud and Big Data
While industrialization of Machine Learning is becoming a more serious constraint for Data Scientists, it has also become a serious constraint for Data Engineers and IT in general. Where the Data Scientist can work on reducing the time needed by a model, the IT people can contribute by changing to faster compute services that are generally obtained in one or both of the following:

Cloud: moving compute resources to external vendors like AWS, Microsoft Azure, or Google Cloud makes it very easy to set up a very fast Machine Learning environment that can be accessed from a distance. This asks from Data Scientists to have a basic understanding of Cloud functioning, for example: working with servers at distance instead of your computer, or working on Linux rather than on Windows / Mac.

Big Data: a second aspect of faster IT is using Hadoop and Spark, which are tools that allow for the parallelization of tasks on many computers at the same time (worker nodes). This asks for using a different approach to implementing models as a Data Scientist because your code must allow for parallel execution.
 

5. NLP, Neural Networks, and Deep Learning
Recently, it has still been accepted for a Data Scientist to consider that NLP and image recognition as mere specializations of Data Science that not all have to master. But the use cases for image classification and NLP get more and more frequent even in ‘regular’ business. At current times, it has become unacceptable to not have at least basic knowledge of such models.

Even if you do not have direct applications of such models in your job, a hands-on project is easy to find and will allow you to understand the steps needed in image and text projects.


15
Software Engineering / Top 5 Data Science Trends for 2020
« on: February 20, 2020, 04:18:09 PM »
Technology is evolving continuously, and so are we. In the upcoming years, there will be massive growth in the AI and Machine Learning field. There is already a considerable amount of data to be managed, and with new technological advancements, we can utilize big data in many ways. For that, we have to stay up to date with the latest trends in data science.

Data Science is not a single term; it covers a variety of topics and networks, such as the Internet of Things, Deep Learning, AI, etc. In simple terms, we can count data science as a complete blend of data inference, algorithm computation, analysis, and technology that helps in solving multifaceted business problems.

Moreover, it provides businesses with advanced tools and technologies that allow them to automate complicated business processes linked with extracting, analyzing, and presenting raw data. With so much happening in the technical field, and the data being generated at a rapid speed, it is crucial to know about the latest as well as the upcoming trends in data science.

To keep you up-to-date with the trends in data science, we have created a list of top 5 data science trends that are set to push your business to achieve great success.

1. Access to Artificial Intelligence and intelligent apps

AI has become the mainstream technology for both small and large businesses, and it will bloom in the next few years. At present, we are at the initial stage of using artificial intelligence, but in 2020, we will see more advanced applications of AI in all fields. The reason AI is growing rapidly is that it allows enterprises to improve their overall business processes, and provides a better way of handling both customer and client’s data.

Though utilizing AI will still remain a challenge for many, as exploring the advancement of this technology is not that simple. In 2020, we will find more advanced apps developed with AI, Machine learning, and other technologies that can improve the way we work. Another trend that will take over the market is automated machine learning that will be helpful in transforming data science with better data management. So, you might need specialized training for executing deep learning.

2. Rapid growth in the IoT

According to a report by IDC, it is expected that the investment in IoT technology would reach $1 trillion by the end of 2020, which clearly clarifies the growth of smart and connected devices. Even in 2019, we used apps and devices that allow us to control our home appliances like AC, TV, etc. Most of you might not now that it is only possible via IoT.

If you have ever come across smart devices like Google Assistant or Microsoft Cortana that allow us to automate the regular things, then you will get an idea that the Internet of things is grabbing continuous attention from users. Thus, it will encourage businesses to invest in this technology, especially in smartphone development, that uses IoT the most.

3.Evolution of Big data analytics

When it comes to data science, we simply cannot ignore Big Data analysis, which helps businesses gain a competitive edge over data and achieve their objectives. Nowadays, enterprises use different tools and technologies, especially python, to analyze big data. Also, businesses are focused on identifying the reasons behind certain events that take place at present. And that’s where predictive analytics is used; it helps companies identify what can happen in the future.

For instance, predictive analysis helps you identify the interests of your customers by their purchase or browsing history. Depending on that, you will be able to create smarter strategies to target new customers and retain the current one.

4.Edge Computing will be on the rise

At present, edge computing is propelled by sensors. But with the growth of IoT, Edge computing will take over mainstream cloud systems. Edge computing allows businesses to store streaming data close to the data sources so that they can be analyzed in real-time. Moreover, it offers a great alternative to Big Data analytics that require high-end storage devices and higher network bandwidth.

As the number of devices and sensors for collecting data is increasing rapidly, companies are adopting edge computing, as it is capable of resolving issues related to bandwidth, latency, and connectivity. When combined with cloud technology, edge computing can provide a synchronized structure that will be helpful in minimizing the risks involved in data analysis and management.

5. Demand for Data science Security Professionals

The adoption of artificial intelligence and machine learning will give rise to many new roles in the industry. One role that will be highly in demand is data science, security professionals. As both AI and ML entirely depends on data, and to process this data efficiently, data scientists must have expertise in data science as well as command over computer science.

Though the business market already has access to many experts who are proficient in data science and computer science, there is still a need for more professional data security professionals who can process data to customers securely. For that, data security scientists must be well versed with the latest technologies of data science or big data analysis. For example, python is amongst the most used languages in data science and data analysis, so having a clear understanding of python concepts can help you tackle the problems related to data science security.

 

Conclusion
Data Science has become one of the growing fields in all industries, especially the IT industry. Thus, businesses adopting data science techniques and technologies must stay up-to-date with the latest trends. In this article, we covered five data science trends that will be on the top of the list in 2020. You can take help from these trends to analyze where you need to improve your business processes in order to achieve maximum growth and ROI.

Pages: [1] 2 3 ... 8