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Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

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Know All about Usage-Driven Grouping of Programming Languages Used in Data Science

Programming skills are indispensable for data science professionals. The main job of machine learning engineers and data scientists is drawing insights from data, and their expertise in programming languages enable them to do this crucial task properly. Research has shown that professionals of the data science field typically work with three languages simultaneously. So, which ones are the most popular? Are some languages more likely to be used together?

Recent studies explain that certain programming languages are used jointly besides other programming languages that are used independently. With the survey data collected from Kaggle’s 2018 Machine Learning and Data Science study, usage patterns of over 18,000 data science experts working with 16 programming languages were analyzed. The research revealed that these languages can actually be categorized into smaller sets, resulting in 5 main groupings. The nature of the groupings is indicative of specific roles or applications that individual groups support, like analytics, front-end work and general-purpose tasks.

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Principal Component Analysis for Dimension Reduction

In this article, we will explain how Bob E. Hayes, PhD holder, scientist, blogger and data science writer has used principal component analysis, a type of data reduction method, to categorize 16 different programming languages. Herein, the relationship among various languages is inspected before putting them in particular groups. Basically, principal component analysis looks into statistical associations like covariance within a large collection of variables, and then justifies these correlations with the help of a few variables, called components.

Principal component matrix presents the results of this analysis. The matrix is an nXm table, where:

n= total no. of original variables, which in this case are the number of programming languages

m= number of main components

The strength of relationship between each language and underlying components is represented by the elements of the matrix. Overall, the principal component analysis of programming language usage gives us two important insights:

  • How many underlying components (groupings of programming languages) describe the preliminary set of languages
  • The languages that go best with each programming language grouping

Result of Principal Component Analysis:

The nature of this analysis is exploratory, meaning no pre-defined structure was imposed on the data. The result was primarily driven by the type of relationship shared by the 16 languages. The aim was to explain the relationships with as less components as possible. In addition, few rules of thumb were used to establish the number of components. One was to find the number of eigen values with value greater than 1 – that number determines the number of components. Another method is to identify the breaking point in the scree plot, which is a plot of the 16 eigen values.

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5-factor solution was chosen to describe the relationships. This is owing to two reasons – firstly, 5 eigen values were greater than one and secondly, the scree plot showed a breaking point around 6th eigen value.

Following are two key interpretations from the principal component matrix:

  • Values greater than equal to .45 have been made bold
  • The headings of different components are named on the basis of tools that loaded highly on that component. For example, component 4 has been labeled as Python, Bash, Scala because these languages loaded highest on this component, implying respondents are likely to use Bash and Scala if they work with Python. Other 4 components were labeled in a similar manner.

Groupings of Programming Languages

The given data set is appropriately described by 5 tool grouping. Below are given 5 groupings, including the particular languages that fall within the group, meaning they are likely to be used together.

  1. Java, Javascript/Typescript, C#/.NET, PHP
  2. R, SQL, Visual Basic/VBA, SAS/STATA
  3. C/C++, MATLAB
  4. Python, Bash, Scala
  5. Julia, Go, Ruby

One programming language didn’t properly load into any of the components: SQL. However, SQL is used moderately with three programming languages, namely Java (component 1), R (component 2) and Python (component 4).

It is further understood that the groupings are determined by the functionality of different languages in the group. General-purpose programming languages, Python, Scala and Bash, got grouped under a single component, whereas languages used for analytical studies, like R and the other languages under comp. 2, got grouped together. Web applications and front-end work are supported by Java and other tools under component 1.

Conclusion:

Data science enthusiasts can succeed better in their projects and boost their chances of landing specific jobs by choosing correct languages that are suited for the job role they want. Being skilled in a single programming language doesn’t cut it in today’s competitive industry. Seasoned data professionals use a set of languages for their projects. Hence, the result of the principal component analysis implies that it’s wise for data pros to skill up in a few related programming languages rather than a single language, and focus on a specific part of data science.

For more help with your data science learning, get in touch with DexLab Analytics, a leading data analyst training institute in Delhi. Also check our Machine learning courses in Delhi to be trained in the essential and latest skills in the field.

 
Reference: http://customerthink.com/usage-driven-groupings-of-data-science-and-machine-learning-programming-languages
 

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More than Statistics, Machine Learning Needs Semantics: Explained

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More than Statistics, Machine Learning Needs Semantics: Explained

Of late, machines have achieved somewhat human-like intelligence and accuracy. The deep learning revolution has ushered us into a new era of machine learning tools and systems that perfectly identifies the patterns and predicts future outcomes better than human domain experts. Yet, there exists a critical distinction between man and machines. The difference lies in the way we reason – we, humans like to reason through advanced semantic abstractions, while machines blindly depend on statistics.

The learning process of human beings is intense and in-depth. We prefer to connect the patterns we identify to high order semantic abstractions and our adequate knowledge base helps us evaluate the reason behind such patterns and determine the ones that are most likely to represent our actionable insights.

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On the other hand, machines blindly look for powerful signals in a pool of data. Lacking any background knowledge or real-life experiences, deep learning algorithms fail to distinguish between relevant and specious indicators. In fact, they purely encode the challenges according to statistics, instead of applying semantics.

This is why diverse data training is high on significance. It makes sure the machines witness an array of counterexamples so that the specious patterns get automatically cancelled out. Also, segmenting images into objects and practicing recognition at the object level is the order of the day. But of course, current deep learning systems are too easy to fool and exceedingly brittle, despite being powerful and highly efficient. They are always on a lookout for correlations in data instead of finding meaning.

Are you interested in deep learning? Delhi is home to a good number of decent deep learning training institutes. Just find a suitable and start learning!

How to Fix?

The best way is to design powerful machine learning systems that can tersely describe the patterns they examine so that a human domain expert can later review them and cast their approval for each pattern. This kind of approach would enhance the efficiency of pattern recognition of the machines. The substantial knowledge of humans coupled with the power of machines is a game changer.

Conversely, one of the key reasons that made machine learning so fetching as compared to human intelligence is its quaint ability to identify a range of weird patterns that would look spurious to human beings but which are actually genuine signals worth considering. This holds true especially in theory-driven domains, such as population-scale human behavior where observational data is very less or mostly unavailable. In situations like this, having humans analyze the patterns put together by machines would be of no use.

End Notes

As closing thoughts, we would like to share that machine learning initiated a renaissance in which deep learning technologies have tapped into unconventional tasks like computer vision and leveraged superhuman precision in an increasing number of fields. And surely we are happy about this.

However, on a wider scale, we have to accept the brittleness of the technology in question. The main problem of today’s machine learning algorithms is that they merely learn the statistical patterns within data without putting brains into them. Once, deep learning solutions start stressing on semantics rather than statistics and incorporate external background knowledge to boost decision making – we can finally chop off the failures of the present generation AI.

Artificial Intelligence is the new kid on the block. Get enrolled in an artificial intelligence course in Delhi and kickstart a career of dreams! For help, reach us at DexLab Analytics.

 

The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2019/01/15/why-machine-learning-needs-semantics-not-just-statistics/#789ffe277b5c

 

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5 Great Takeaways from Machine Learning Conference 2019

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5 Great Takeaways from Machine Learning Conference 2019

Machine Learning Developer Summit, one of the leading Machine Learning conferences of India, happening on the 30th and 31st of January 2019 in Bangalore, aims to assemble machine leaning and data science experts and enthusiasts from all over India. Organized by Analytics India Magazine, this high-level meeting will be the hotspot for conversing about the latest developments in machine learning. Attendees can gather immense knowledge from ML experts and innovators from top tech enterprises, and network with individuals belonging to data sciences. Actually, there are tons of rewards for those attending MLDS 2019. Below are some of the best takeaways:

  1. Creation of Useful Data Lake on AWS

In a talk by reputable Raghuraman Balachandran, Solutions Architect for Amazon Web Services, participants will learn how to design clean, dependable data lakes on AWS cloud. He shall also share his experienced outlook on tackling some common challenges of designing an effective data lake. Mr Balachandran will explain the process to store raw data – unstructured, semi-structured or completely structured – and processed data for different analytical uses.

Data lakes are the most used architectures in data-based companies. This talk will allow attendees to develop a thorough understanding of the concept, which is sure to boost their skill set for getting hired.

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  1. Improve Inference Phase for Deep Learning Models

Deep learning models require considerable system resources, including high-end CPUs and GPUs for best possible training. Even after exclusive access to such resources, there may be several challenges in the target deployment phase that were absent in the training environment.

Sunil Kumar Vuppala, Principal Scientist at Philips Research, will discuss methods to boost the performance of DL models during their inference phase. Further, he shall talk about using Intel’s inference engine to improve quality of DL models run in Tensorflow/Caffe/Keras via CPUs.

  1. Being more employable amid the explosive growth in AI and its demand

The demand for AI skills will skyrocket in future – so is the prediction of many analysts considering the extremely disruptive nature of AI. However, growth in AI skills isn’t occurring at the expected rate. Amitabh Mishra, who is the CTO at Emcure Pharmaceuticals, addresses the gap in demand and development of AI skills, and shall share his expert thoughts on the topic. Furthermore, he will expand on the requirements in AI field and provide preparation tips for AI professionals.

  1. Walmart AI mission and how to implement AI in low-infrastructure situations

In the talk by Senior Director of Walmart Lab, Prakhar Mehrotra, audiences get a view of Walmart’s progress in India. Walmart Lab is a subsidiary of the global chain Walmart, which focuses on improving customer experience and designing tech that can be used with Merchants to enhance the company’s range. Mr Mehrotra will give details about Wallmart’s AI journey, focusing on the advancements made so far.

  1. ML’s important role in data cleansing

A good ML model comes from a clean data lake. Generally, a significant amount of time and resources invested in building a robust ML model goes on data cleansing activities. Somu Vadali, Chief of Future Group’s CnD Labs Data and Products section, will talk about how ML can be used to clean data more efficiently. He will speak at length about well-structured processes that allow organizations to shift from raw data to features in a speedy and reliable manner. Businesses may find his talk helpful to reduce their time-to-market for new models and increase efficiency of model development.

Machine learning is the biggest trend of IT and data science industry. In fact, day by day it is gaining more prominence in the tech industry, and is likely to become a necessary skill to get bigger in all fields of employment. So, maneuver your career towards excellence by enrolling for machine learning courses in India. Machine learning course in Gurgaon by DexLab Analytics is tailor-made for your specific needs. Both beginners and professionals find these courses apt for their growth.

 

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Being a Statistician Matters More, Here’s Why

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Being a Statistician Matters More, Here’s Why

Right data for the right analytics is the crux of the matter. Every data analyst looks for the right data set to bring value to his analytics journey. The best way to understand which data to pick is fact-finding and that is possible through data visualization, basic statistics and other techniques related to statistics and machine learning – and this is exactly where the role of statisticians comes into play. The skill and expertise of statisticians are of higher importance.

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Below, we have mentioned the 3R’s that boosts the performance of statisticians:

Recognize – Data classification is performed using inferential statistics, descriptive and diverse other sampling techniques.

Ratify – It’s very important to approve your thought process and steer clear from acting on assumptions. To be a fine statistician, you should always indulge in consultations with business stakeholders and draw insights from them. Incorrect data decisions take its toll.

Reinforce – Remember, whenever you assess your data, there will be plenty of things to learn; at each level, you might discover a new approach to an existing problem. The key is to reinforce: consider learning something new and reinforcing it back to the data processing lifecycle sometime later. This kind of approach ensures transparency, fluency and builds a sustainable end-result.

Now, we will talk about the best statistical techniques that need to be applied for better data acknowledgment. This is to say the key to becoming a data analyst is through excelling the nuances of statistics and that is only possible when you possess the skills and expertise – and for that, we are here with some quick measures:

Distribution provides a quick classification view of values within a respective data set and helps us determine an outlier.

Central tendency is used to identify the correlation of each observation against a proposed central value. Mean, Median and Mode are top 3 means of finding that central value.

Dispersion is mostly measured through standard deviation because it offers the best scaled-down view of all the deviations, thus highly recommended.

Understanding and evaluating the data spread is the only way to determine the correlation and draw a conclusion out of the data. You would find different aspects to it when distributed into three equal sections, namely Quartile 1, Quartile 2 and Quartile 3, respectively. The difference between Q1 and Q3 is termed as the interquartile range.

While drawing a conclusion, we would like to say the nature of data holds crucial significance. It decides the course of your outcome. That’s why we suggest you gather and play with your data as long as you like for its going to influence the entire process of decision-making.

On that note, we hope the article has helped you understand the thumb-rule of becoming a good statistician and how you can improve your way of data selection. After all, data selection is the first stepping stone behind designing all machine learning models and solutions.

Saying that, if you are interested in learning machine learning course in Gurgaon, please check out DexLab Analytics. It is a premier data analyst training institute in the heart of Delhi offering state-of-the-art courses.

 

The blog has been sourced from — www.analyticsindiamag.com/are-you-a-better-statistician-than-a-data-analyst

 

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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

Apache Spark has become an essential part of operations of big technology firms, like Yahoo, Facebook, Amazon and eBay. This is mainly owing to the lightning speed offered by Apache Spark – it is the speediest engine for big data activities. The reason behind this speed: Rather than a disk, it operates on memory (RAM). Hence, data processing in Spark is even faster than in Hadoop.

The main purpose of Apache Spark is offering an integrated platform for big data processes. It also offers robust APIs in Python, Java, R and Scala. Additionally, integration with Hadoop ecosystem is very convenient.

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Why Apache Spark for ML applications?

Many machine learning processes involve heavy computation. Distributing such processes through Apache Spark is the fastest, simplest and most efficient approach. For the needs of industrial applications, a powerful engine capable of processing data in real time, performing in batch mode and in-memory processing is vital. With Apache Spark, real-time streaming, graph processing, interactive processing and batch processing are possible through a speedy and simple interface. This is why Spark is so popular in ML applications.

Apache Spark Use Cases:

Below are some noteworthy applications of Apache Spark engine across different fields:

Entertainment: In the gaming industry, Apache Spark is used to discover patterns from the firehose of real-time gaming information and come up with swift responses in no time. Jobs like targeted advertising, player retention and auto-adjustment of complexity levels can be deployed to Spark engine.

E-commerce: In the ecommerce sector, providing recommendations in tandem with fresh trends and demands is crucial. This can be achieved because real-time data is relayed to streaming clustering algorithms such as k-means, the results from which are further merged with various unstructured data sources, like customer feedback. ML algorithms with the aid of Apache Spark process the immeasurable chunk of interactions happening between users and an e-com platform, which are expressed via complex graphs.

Finance: In finance, Apache Spark is very helpful in detecting fraud or intrusion and for authentication. When used with ML, it can study business expenses of individuals and frame suggestions the bank must give to expose customers to new products and avenues. Moreover, financial problems are indentified fast and accurately.  PayPal incorporates ML techniques like neural networks to spot unethical or fraud transactions.

Healthcare: Apache Spark is used to analyze medical history of patients and determine who is prone to which ailment in future. Moreover, to bring down processing time, Spark is applied in genomic data sequencing too.

Media: Several websites use Apache Spark together with MongoDB for better video recommendations to users, which is generated from their historical data.

ML and Apache Spark:

Many enterprises have been working with Apache Spark and ML algorithms for improved results. Yahoo, for example, uses Apache Spark along with ML algorithms to collect innovative topics than can enhance user interest. If only ML is used for this purpose, over 20, 000 lines of code in C or C++ will be needed, but with Apache Spark, the programming code is snipped at 150 lines! Another example is Netflix where Apache Spark is used for real-time streaming, providing better video recommendations to users. Streaming technology is dependent on event data, and Apache Spark ML facilities greatly improve the efficiency of video recommendations.

Spark has a separate library labelled MLib for machine learning, which includes algorithms for classification, collaborative filtering, clustering, dimensionality reduction, etc. Classification is basically sorting things into relevant categories. For example in mails, classification is done on the basis of inbox, draft, sent and so on. Many websites suggest products to users depending on their past purchases – this is collaborative filtering. Other applications offered by Apache Spark Mlib are sentiment analysis and customer segmentation.

Conclusion:

Apache Spark is a highly powerful API for machine learning applications. Its aim is wide-scale popularity of big data processing and making machine learning practical and approachable. Challenging tasks like processing massive volumes of data, both real-time and archived, are simplified through Apache Spark. Any kind of streaming and predictive analytics solution benefits hugely from its use.

If this article has piqued your interest in Apache Spark, take the next step right away and join Apache Spark training in Delhi. DexLab Analytics offers one the best Apache Spark certification in Gurgaon – experienced industry professionals train you dedicatedly, so you master this leading technology and make remarkable progress in your line of work.

 

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Know the 5 Best AI Trends for 2019

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Know the 5 Best AI Trends for 2019

Artificial Intelligence is perhaps the greatest technological advancement the world has seen in several decades. It has the potential to completely alter the way our society functions and reshape it with new enhancements. From our communication systems to the nature of jobs, AI is likely to restructure everything.

‘Creative destruction’ has been happening since the dawn of human civilization. With any revolutionary technology, the process just speeds up significantly. AI has unleashed a robust cycle of creative destruction across all employment sectors. While this made old skills redundant, the demand and hence acquisition of superior skills have shot up.

The sweeping impact of AI can be felt from the fact that the emerging AI rivalry between USA and China is hailed as ‘The New Space Race’! Among the biggest AI trends of 2018 was China’s AI sector – it came under spotlight for producing more AI-related patents and startups compared to the US. This year, the expectations and uncertainties regarding AI both continue to rise. Below we’ve listed the best AI trends to look out for in 2019:

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AI Chipsets

AI wholly relies on specialized processors working jointly with CPU. But, the downside is that even the most innovative and brilliant CPUs cannot train an AI model. The model requires additional hardware to carry out higher math calculations and sophisticated tasks such as face recognition.

In 2019, foremost chip manufacturers like Intel, ARM and NVidia will produce chips that boost the performance speed of AI-based apps. These chips will be useful in customized applications in language processing and speech recognition. And further research work will surely result in development of applications in fields of automobiles and healthcare.

Union of AI and IoT

This year will see IoT and AI unite at edge computing more than ever. Maximum number of Cloud-trained models shall be placed at the edge layer.

AI’s usefulness in IoT applications for the industrial sector is also anticipated to increase by leaps and bounds. This is because AI can offer revolutionary precision and functionality in areas like predictive maintenance and root cause analysis. Cutting edge ML models based on neural networks will be optimized along with AI.

IoT is emerging as the chief driver of AI for enterprises. Specially structured AI chips shall be embedded on majority of edge devices, which are tools that work as entry points to an entire organization or service provider core networks.

Upsurge of Automated ML

With the entry of AutoML (automated Machine Learning) algorithms, the entire machine learning subject is expected to undergo a drastic change. With the help of AutoML, developers can solve complicated problems without needing to create particular models. The main advantage of automated ML is that analysts and other professionals can concentrate on their specific problem without having to bother with the whole process and workflow.

Cognitive computing APIs as well as custom ML tools perfectly adjust to AutoML. This helps save time and energy by directly tackling the problem instead of dealing with the total workflow. Because of AutoML, users can enjoy flexibility and portability in one package.

AI and Cyber security

The use of AI in cybersecurity is going to increase by a significant measure because of the following reasons: (i) there a big gap between the availability and requirement of cybersecurity professionals, (ii) drawbacks of traditional cybersecurity and (iii) mounting threats of security violations that necessitate innovative approaches. Depending on AI doesn’t mean human experts in the field will no longer be useful. Rather, AI will make the system more advanced and empower experts to handle problems better.

As cybersecurity systems worldwide are expanding, there’s need to cautiously supervise threats. AI will make these essential processes less vulnerable and way more efficient.

Need for AI Skilled Professionals:

In 2018, it was stated that AI jobs would be the highest paying ones and big enterprises were considering AI reskilling. This trend has been carried over to 2019. But companies are facing difficulties trying to bridge the AI skills gap in their employees.

Having said that, artificial intelligence can do wonders for your career if you’re a beginner or advanced employee working with data or technology. In Delhi, you’ll find opportunities to enroll for comprehensive artificial intelligence courses. DexLab Analytics, the premier data science and AI training institute, offers advanced artificial intelligence certification in Delhi NCR. Check out the course details on their website.

 

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Decoding the Equation of AI, Machine Learning and Python

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Decoding the Equation of AI, Machine Learning and Python

AI is an absolute delight. Not only is it considered one of the most advanced fields in the present computer science realm but also AI is a profit-spinning tool leveraged across diverse industry verticals.

In the past few years, Python also seems to be garnering enough fame and popularity. Ideal for web application development, process automation, web scripting, this wonder tool is a very potent programming language in the world. But, what makes it so special?

Owing to ease of scalability, learning and adaptability of Python, this advanced interpreted programming language is the fastest growing global language. Plus, its ever-evolving libraries aid it in becoming a popular choice for projects, like mobile app, data science, web app, IoT, AI and many others.

Python, Machine Learning, AI: Their Equation

Be it startups, MNCs or government organizations, Python seem to be winning every sector. It provides a wide array of benefits without limiting itself to just one activity – its popularity lies in its ability to combine some of the most complex processes, including machine learning, artificial intelligence, data science and natural language processing.

Deep learning can be explained as a subset of a wider arena of machine learning. From the name itself you can fathom that deep learning is an advanced version of machine learning where intelligence is being harnessed by a machine generating an optimal or sub-optimal solution.

Combining Python and AI

Lesser Coding

AI is mostly about algorithms, while Python is perfect for developers who are into testing. In fact, it supports writing and execution of codes. Hence, when you fuse Python and AI, you drastically reduce the amount of coding, which is great in all respects.

Encompassing Libraries

Python is full of libraries, subject to the on-going project. For an instance, you can use Numpy if you are into scientific computation – for advanced computing, you have put your bet on SciPy – whereas, for machine learning, PyBrain is the best answer.

A Host of Resources

Entirely open source powered by a versatile community, Python provides incredible support to developers who want to learn fast and work faster. The huge community of web developers are active worldwide and willing to offer help at any stage of the development cycle.

Better Flexibility

Python is versatile. It can be used for a variety of purposes, right from OOPs approach to scripting. Also, it performs as a quintessential back-end and successfully links different data structures with one another.

Perfect for Today’s Millennial

Thanks to its flexibility and versatility, Python is widely popular amongst the millennials. You might be surprised to hear that it is fairly easier to find out Python developers than finding out Prolog or LISP programmers, especially in some countries. Encompassing libraries and great community support helps Python become the hottest programming language of the 21st century.

Data Science Machine Learning Certification

Some of the most popular Python libraries for AI are:

  • AIMA
  • pyDatalog
  • SimpleAI
  • EasyAI

Want to ace problem-solving skills and accomplish project goals, Machine Learning Using Python is a sure bet. With DexLab Analytics, a recognized Python Training Center in Gurgaon, you can easily learn the fundamentals and advance sections of Python programming language and score goals of success.

 

The blog has been sourced from ― www.information-age.com/ai-machine-learning-python-123477066

 


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A Success Story: Evolution of India’s Startup Ecosystem in 2018

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A Success Story: Evolution of India’s Startup Ecosystem in 2018

India’s startup ecosystem is gaining accolades. Steering away from the conventional, India’s young generation is pursuing the virgin path of entrepreneurship by ditching lucrative job offers from MNCs and government undertakings – the entire industry is witnessing an explosion of cutting-edge startups addressing real problems, framing solutions and satisfying mass level.

Interestingly, 2018 has been the year of success for Indian startups or entrepreneurs venturing into the promising unknown. Why? In total, 8 Indian startups, namely Oyo, Zomato, Paytm Mall, Udaan, Swiggy, Freshworks, Policybazaar and Byju’s crossed the $1 billion net worth mark this year and joined the raft of most-revered 18 Indian unicorns.

Besides attracting investments from domestic venture capitalists, these startups are bathed in global investments – foreign investors pumped in vast amounts on our homegrown startups to capitalize their activities. Thanks to their generosity, India proudly ranks as the 3rd largest startup ecosystem in the world, next to the United Nations and United Kingdom with its 7, 700 tech startups.

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Nevertheless, our phenomenal startup ecosystem has some grey areas too, which are addressed below:

Startup Initiatives

No doubt, the Indian government is taking conscious efforts to support the startup culture in the country, and for that Prime Minister, Narendra Modi has initiated the Startup India Programme. It is a noble step towards ensuring continuous creation and smooth functioning of fresh startups in India with technology in tow.

Thanks to technology, startups growth seemed to be 50% more dynamic this year!

Fund Generation

As compared to struggling years of 2017 and before, 2018 has been the year of driving investments. India experienced a 108% growth in total funding process, a big jump from $2 billion to $4.2 billion. Though investments at later stages skyrocketed, a decline was witnessed in the early stages during funding companies.

“In terms of overall funding, it is a good story. However, we are seeing a continuous decline in seed stage funding of startup companies. If you fall at the seed stage, innovation is hit. It is the area, which needs protection,” shared NASSCOM president Debjani Ghosh, which remains a matter of concern.

Employment Opportunities

Of course, the new startups push job creation numbers. It enhances the employment opportunities. Of late, NASSCOM reported that the epic growth in startup ecosystem resulted in creation of more than 40000 new direct jobs, while indirect jobs soared manifold. Today, the total strength of Indian startup landscape stands at 1.7 Lakh.

In the wake of powerful female voices and gender-neutral campaigns, our domestic startup ecosystem witnessed how women employees called the shots. The numbers of women employees spiked to 14% from 10% and 11% in the last two years, consecutively.

Global Position

Globally, India ranks as the 3rd biggest startup ecosystem in the world, and Bengaluru is the kernel of tech revolution. A report mentioned India’s significance in recording the highest number of startup set ups after Silicon Valley and London across the globe.

Quite interestingly, 40% of startups are launched in Tier 2 and 3 cities, indicating a steady rise of startup culture outside prime cities like Mumbai, Bengaluru and Delhi NCR.

With technology and startup leading the show, it’s high time you expand your in-demand skills of machine learning and data analytics. How? Opt for a good Machine Learning Course in India. It’s a surefire way to learn the basics and hone already learnt skills. For more information on Machine Learning Using Python, drop by DexLab Analytics!

 
The blog has been sourced from ― www.entrepreneur.com/article/322409
 

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Facebook and Google Have Teamed Up to Expand the Horizons of Artificial Intelligence

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Facebook and Google Have Teamed Up to Expand the Horizons of Artificial Intelligence

Tech unicorns, Google and Facebook have joined hands to enhance AI experience, and take it to the next level.

Last week, the two companies revealed that quite a number of engineers are working to sync Facebook’s open source machine learning PyTorch framework with Google’s TPU, or dubbed Tensor Processing Units – the collaboration is one of its kind, and a first time where technology rivals are working on a joint project in technology.

“Today, we’re pleased to announce that engineers on Google’s TPU team are actively collaborating with core PyTorch developers to connect PyTorch to Cloud TPUs,” said Rajen Sheth, Google Cloud director of product management. “The long-term goal is to enable everyone to enjoy the simplicity and flexibility of PyTorch while benefiting from the performance, scalability, and cost-efficiency of Cloud TPUs.”

Joseph Spisak, Facebook product manager for AI added, “Engineers on Google’s Cloud TPU team are in active collaboration with our PyTorch team to enable support for PyTorch 1.0 models on this custom hardware.”

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2016 was the year when Google first introduced its TPU to the world at the Annual Developer Conference – that year itself the search engine giant pitched the technology to different companies and researchers to support their advanced machine-learning software projects. Since then, Google has been selling access to its TPUs through its cloud computing business instead of going the conventional way of selling chips personally to customers, like Nvidia.

Over the years, AI technology, like Deep Learning have been widening its scopes and capabilities in association with tech bigwigs like Facebook and Google that have been using the robust technology to develop software applications that automatically perform intricate tasks, such as recognizing images in photos.

Since more and more companies are exploring the budding ML domain for years now, they are able to build their own AI software frameworks, mostly the coding tools that are intended to develop customized machine-learning powered software easily and effectively. Also, these companies are heard to offer incredible AI frameworks for free in open source models – the reason behind such an initiative is to popularize them amongst the coders.

For the last couple of years, Google has been on a drive to develop its TPUs to get the best with TensorFlow. Moreover, the initiative of Google to work with Facebook’s PyTorch indicates its willingness to support more than just its own AI framework. “Data scientists and machine learning engineers have a wide variety of open source tools to choose from today when it comes to developing intelligent systems,” shared Blair Hanley Frank, Principal Analyst, Information Services Group. “This announcement is a critical step to help ensure more people have access to the best hardware and software capabilities to create AI models.”

Besides Facebook and Google, Amazon and Microsoft are also expanding their AI investment through its PyTorch software.

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The blog has been sourced from — www.dexlabanalytics.com/blog/streaming-huge-amount-of-data-with-the-best-ever-algorithm
 

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