Month: January 2021

Why You Should Use Big Data Analytics in Recruitment

Big data, a term that describes a huge volume of data that inundates businesses on a daily basis, has transformed many processes in numerous different industries. The way big data works in business is that it gets analyzed to reveal patterns that weren’t previously recognized and provide companies with more insight into a certain process, human behavior, or whatever you are searching for.

Today, big data analytics has become a crucial part of business even for small and medium-sized companies. The main reason why this has become so popular is that it gives businesses a bigger chance of success. It lets them know marketing trends before others discover them. It can also show them how they can improve the efficiency of their manufacturing processes. Similarly, big data has been proven to be very effective in the world of recruiting.

Traditional vs. Predictive Hiring?

Traditional hiring is a pretty straightforward process. All you have to do is post an ad somewhere, wait for people to apply for a position, manually screen their resumes, and conduct interviews. Although this type of process isn’t going anywhere soon, in most cases it isn’t the most effective way to find good candidates for a position at your company.

The main downside of the traditional hiring process is that you have to use only the information provided by candidates and your intuition to determine whether they’d be a good addition to your company. Predictive hiring, which is rotted in big data analytics, takes a different approach. It focuses on analyzing historical data to make predictions about future behavior.

How You Can Use Big Data Analytics in Recruiting

What if you could be certain that you found fitting candidates for a position before you even start interviewing? Let’s say that your goal is to turn your company into one of the best essay writing services UK. When you aim to provide such a specific service, you need to have excellent writers in your organization.

If you decided to find candidates through a traditional hiring process, there’s a good chance you’d hire at least one person that isn’t skilled in writing custom essay papers despite having relevant qualifications. That’s because resumes are usually one-dimensional and don’t provide you with a complete picture of candidates.

What makes predictive hiring a better process is that it focuses on identifying the talent level and skills candidates possess instead of their official certifications. It’s worth noting that a concept like big data wouldn’t be possible if it weren’t for the widespread popularity and usage of the internet. Nowadays, it’s normal for a person to have accounts on several different social media platforms.

Recruiters are able to collect data from social media profiles in order to create a better picture of a candidate before the first interview. Some of the best platforms where you can find useful data about candidates include LinkedIn and Quora. If someone is frequently helping college students with academic writing on a website like Quora, it’s a good indication that they’d be a good addition to your essay writing service.

Another thing that big data analytics can do is help you learn more about the applicants’ personalities. You can extract and analyze data from pre-employment assessments to get a better understanding of a candidate’s skills and personality. This can help you determine whether they’d fit with your company’s culture.

If you aim to provide UK best essays to college students, you’ll need to know that they can provide excellent services. In this particular example, college students rely on quality help as anything less than that may cost them a lot. The last thing you want to do is hire a bunch of dissertation writers that can’t even craft an essay paper. Big data analytics can help you avoid situations like this by providing you with a complete picture of an applicant’s previous performance reviews.

If a candidate has any type of online presence, regardless of how small it is, big data analytics will find and analyze it to provide you with a better understanding of potential candidates. In our example of starting an essay writing service, it’s imperative to look for people with university diplomas. However, if you were to look for a programmer for an IT firm, you could use big data recruitment to find experts in this field who might not have a diploma.

Benefits of Big Data Recruitment

Recruitment can be quite a costly process, especially if you have the task of filling several different positions at your company. Expenses can be very high if you need to repeat the recruitment process due to the bad quality of new hires. One of the main benefits of big data recruitment is that it increases the quality of new hires and minimizes hiring mistakes. Recruiters are able to take a more strategic approach if they use big data analytics to fill a position.

Another benefit of predictive hiring is that it helps you to create a more consistent workforce. Knowing the personality traits of a candidate before they first step foot in your building can make it easier for you to decide whether to hire them. If you and your recruitment team are on a tight deadline, you can also use big data to analyze and predict the speed of hire. Finally, you can rely on big data analytics to monitor and embed diversity into the hiring process.

Factors Affecting the Market Growth of AI in Agriculture Market

The artificial intelligence in agriculture market have impacted positively due to the pandemic. AI is applied largely in the agriculture sector in various countries across the globe is predicted to boost the overall market in the forecast period. Due to shut down across the globe the market has not impacted adversely. Moreover, increasing implementation of artificial intelligence with the help of various sensor in the agricultural field is predicted to be the major driving factor for the market in the forecast period. Cost involved for artificial intelligence in the agricultural land is too high which is predicted to hamper the market growth over the coming years.

AI is only used in large field lands, implementation of AI in the smaller land with lesser investment is predicted to create more growth opportunity in the forecast period. For instance, India joins GPAI as founding member to support human-centric development with the help of AI in various field including agriculture, education, finance and telecommunication. The initiative will be helpful for diversity, innovation, and economic growth of the country. During this unpredicted situation, we are helping our clients in understanding the impact of COVID19 on the global artificial intelligence in agriculture market. Our report includes:

  • Technological Impact
  • Social Impact
  • Investment Opportunity Analysis
  • Pre- & Post-COVID Market Scenario
  • Infrastructure Analysis
  • Supply Side & Demand Side Impact

Check out How COVID-19 impact on the Artificial Intelligence in Agriculture Market @ https://www.researchdive.com/connect-to-analyst/224

The global market is classified on the basis of application and deployment. The report offers the complete information about drivers, opportunities, restraints, segmental analysis and major players of the global market.

Factors Affecting the Market Growth

As per our analyst, increasing adoption of AI in agriculture field through sensor is predicted to be the major driving factor for the market in the forecast period. On the other hand, Lack of awareness among the farmer and the cost involved in implementing of AI in agriculture is very high which is predicted to hamper the market in the forecast period.

Drone Analytics Segment is Predicted to be the Most Profitable Segment

On the basis of application, the global artificial intelligence in agriculture market is segmented into weather tracking, precision farming, and drone analytics. Drone analytics is predicted to have the maximum market share in the forecast period. With the help of drone one can easily monitor the agricultural operation, increase crop production and optimize the agricultural activities due to which it is predicted to boost the segment market in the forecast period.

Download Sample Report of the Artificial Intelligence in Agriculture Market @ https://www.researchdive.com/request-toc-and-sample/224


Cloud Segment is Predicted to Grow Enormously

On the basis of deployment, the global artificial intelligence in agriculture market is segmented cloud, on premise, and hybrid. Cloud segment is predicted to have the highest market share in the forecast period. Cloud gives the option to the farmer to choose the right crop, cultivating process and operational activities that are associated with the respective farm which is predicted to drive the market in the forecast period.

Europe Region Market is Predicted to be the Most Profitable Region

On the basis of region, the global artificial intelligence in agriculture market is segmented North America, Asia Pacific, LAMEA, and Europe. Europe is predicted to have the highest market share in the forecast period. Increasing demand towards AI in farming and implementation of various AI techniques in farming is predicted to be the major driving factor for the global artificial intelligence in agriculture market in the forecast period.

Top Player in the Global Artificial Intelligence Market

The key players operating in the global artificial intelligence market include

  • GAMAYA, Inc,
  • Aerial Systems Inc.,
  • aWhere Inc.
  • INTERNATIONAL BUSINESS MACHINES CORPORATION,
  • Farmers Edge Inc.,
  • Descartes Labs, Inc.,
  • Microsoft,
  • Deere & Company,
  • Granular, Inc.,
  • The Climate Corporation

About Us:
Research Dive is a market research firm based in Pune, India. Maintaining the integrity and authenticity of the services, the firm provides the services that are solely based on its exclusive data model, compelled by the 360-degree research methodology, which guarantees comprehensive and accurate analysis. With unprecedented access to several paid data resources, team of expert researchers, and strict work ethic, the firm offers insights that are extremely precise and reliable. Scrutinizing relevant news releases, government publications, decades of trade data, and technical & white papers, Research dive deliver the required services to its clients well within the required timeframe. Its expertise is focused on examining niche markets, targeting its major driving factors, and spotting threatening hindrances. Complementarily, it also has a seamless collaboration with the major industry aficionado that further offers its research an edge.

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What is Apache DolphinScheduler?

Apache DolphinScheduler(incubator) – a distributed and extensible workflow scheduler platform with powerful DAG visual interfaces, dedicated to solving complex job dependencies in the data pipeline and providing various types of jobs available out of the box.

Features:

  • Associate the tasks according to the dependencies of the tasks in a DAG graph, which can visualize the running state of the task in real-time.
  • Support various task types: Shell, MR, Spark, SQL (MySQL, PostgreSQL, hive, spark SQL), Python, Sub_Process, Procedure, etc.
  • Support scheduling of workflows and dependencies, manual scheduling to pause/stop/recover task, support failure task retry/alarm, recover specified nodes from failure, kill task, etc.
  • Support the priority of workflows & tasks, task failover, and task timeout alarm or failure.
  • Support workflow global parameters and node customized parameter settings.
  • Support online upload/download/management of resource files, etc. Support online file creation and editing.
  • Support task log online viewing and scrolling and downloading, etc.
  • Have implemented cluster HA, decentralize Master cluster and Worker cluster through Zookeeper.
  • Support the viewing of Master/Worker CPU load, memory, and CPU usage metrics.
  • Support displaying workflow history in tree/Gantt chart, as well as statistical analysis on the task status & process status in each workflow.
  • Support back-filling data.
  • Support multi-tenant.
  • Support internationalization.
  • More features waiting for you to explore…

What’s in Dolphin Scheduler

Stability Easy to use Features Scalability
Decentralized multi-master and multi-worker Visualization of workflow key information, such as task status, task type, retry times, task operation machine information, visual variables, and so on at a glance. Support pause, recover operation Support customized task types
support HA Visualization of all workflow operations, dragging tasks to draw DAGs, configuring data sources and resources. At the same time, for third-party systems, provide API mode operations. Users on DolphinScheduler can achieve many-to-one or one-to-one mapping relationship through tenants and Hadoop users, which is very important for scheduling large data jobs. The scheduler supports distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. Master and Worker support dynamic adjustment.
Overload processing: By using the task queue mechanism, the number of schedulable tasks on a single machine can be flexibly configured. Machine jam can be avoided with high tolerance to numbers of tasks cached in task queue. One-click deployment Support traditional shell tasks, and big data platform task scheduling: MR, Spark, SQL (MySQL, PostgreSQL, hive, spark SQL), Python, Procedure, Sub_Process

User Interface

Get Help

  1. Submit an [issue]
  2. Subscribe to the mail list: https://dolphinscheduler.apache.org/en-us/docs/development/subscribe.html, then email dev@dolphinscheduler.apache.org
  3. Get help through Slack: [Slack channel]

Dolphin Scheduler Official Website

dolphinscheduler.apache.org

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Can a Diploma from a Lower Ranking University Hurt your Data Science Career Prospects?

Here I specifically discuss the case of a PhD degree from a third-tier university, though to some extent, it also applies to master degrees. Many professionals joining companies such as Facebook, Microsoft, or Google in a role other than a programmer, typically have a PhD degree, although there are many exceptions. It is still possible to learn data science on the job, especially if you have a quantitative background (say in physics or engineering) and have experience working with serious data: see here. After all, learning Python is not that hard and can be done via data camps. What is more difficult to acquire is the analytical maturity. 

University of Namur

In my cased, I did my PhD at the University of Namur, a place that nobody has heard of. The topic of my research was computational statistics and image analysis. These were hot topics back then, and I was also lucky to work part-time in the corporate world for a state-of-the-art GIS (Geographic Information System) company, working with engineers on digital satellite images, as part of my PhD program, thanks to my mentor. Much of what I worked on is still very active these days, on a much bigger scale. It was the precursor of automated driving systems, and the math department in my alma mater was young and still very creative back then. This brings me to my first advice when choosing a PhD program.

Advice #1

  • If you come from a poor background, your options might be more limited (this was my case), and you need to leverage everything you can. My parents did not have the money to send me to expensive schools, and I ended up attending the closest one to avoid spending a lot of money on rent. On the plus side, I did not accumulate student loans.
  • Before deciding on a PhD program, carefully choose your mentor. Mine was not known for his research, but he was well connected to the industry, managed to get money to fund his projects, and was working on exciting, applied projects. 

A side effect on my last piece of advice is that if your goal is to stay in Academia, you may have to rely on yourself to make your research worthy of publications and susceptible to land you a tenured position. The way I did it is summarized in my next advice. You want ideally to leave all doors open, both Academia and other options.

Advice #2

  • Be proactive about reaching out to well respected professors in your field. Attend conferences and meet peers from around the world. Accept jobs such as reviewers. Start publishing in third-tier journals, move to second-tier, and then get a few ones in first-tier journals before completing your PhD. The one I published in Journal of Statistical Society, Series B, is what resulted in me being accepted as a postdoc at Cambridge University. Initially when it was accepted, it only had my name on it. 
  • It helps to be passionate about what you do. My very first paper was in Journal of Number Theory, during my first year as a PhD student. It happened because I had a passion for number theory that I developed during my middle-school and high-school years. I hated high-school math (repetitive, boring mechanical exercises) but loved the math that I discovered and self-learned myself during these years, mostly through reading. I was the only student to participate (and be a finalist) at the national Math Olympiads, in my school. When you are young, it’s something good to have on your resume. 

So to answer the original question – does it hurt coming from a low ranking school – at this point you know that you can still succeed despite the odds. But it requires patience, perseverance, and you must be very good at what you do. Perhaps the biggest drawback is the lack of great connections that top schools offer. You have to make up for that. Also great schools have state-of-the-art equipment and labs (so you can learn the most modern stuff), but somehow my little math department didn’t lack these, so I was not penalized for that. I also cultivated great relationships with the computer science department. At the end, my research was at the intersection of math, statistics and computer science.

My last piece of advice is about what happens next after completing your PhD. In my case, I started a postdoc at Cambridge then moved to the corporate world (after failing a job interview for a tenured position) and eventually became entrepreneur, VC-funded executive, and sold my last venture recently to a publicly traded company. I still do independent math research, even more so and of higher caliber than during my PhD years. 

Advice #3

  • Contact other successful professionals who came from a third-tier university to ask for their advice. In my math department, two other PhD students in my cohort ended up having a stellar career: Michel Bierlaire (postdoc MIT after Namur) is now full professor at EPFL; Didier Burton (also postdoc MIT after Namur) ended up as an executive at Yahoo. 
  • If you can, leverage the fact that you are very applied, don’t have student loans, and thus you can ask a lower salary, be more competitive, gain various horizontal experience in many places while developing world-class expertise in a few areas.  I eventually realized that working for myself (not as consultant, but entrepreneur) was what I liked best.

You may argue that you don’t need any diploma to create your own self-funded company, not even elementary school, but in the end I believe I got the best I could out of my PhD. In my case, it also implied relocating several times, from Belgium (due to lack of jobs) to UK to United States, and from the East Coast to the Bay Area and finally Seattle. I’ve been through various bubbles and market crashes; you may use your analytical skills to navigate them the best you can, selling and buying at the right time, understanding the markets, and emerge stronger each time. 

About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent also founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). He is also the founder and investor in Paris Restaurant in Anacortes, WA. You can access Vincent’s articles and books, here.

NLP with Bangla: word2vec, generating Bangla text & sentiment analysis (LSTM), ChatBot (RASA NLU)

In this blog, we shall discuss on a few NLP techniques with Bangla language. We shall start with a demonstration on how to train a word2vec model with Bangla wiki corpus with tensorflow and how to visualize the semantic similarity between words using t-SNE. Next, we shall demonstrate how to train a character / word LSTM on selected Tagore’s songs to generate songs like Tagore with keras.

Once that’s accomplished, we shall create sentiment analysis dataset by crawling the daily astrological prediction pages of a leading Bangla newspaper and manually labeling the sentiment of each of the predictions corresponding to each moon-sign. We shall train an LSTM sentiment a analysis model to predict the sentiment of a moon-sign prediction. Finally, we shall use RASA NLU (natural language understanding) to build a very simple chatbot in Bangla.

Word2vec model with Bangla wiki corpus with tensorflow

  • Let’s start by importing the required libraries
import collections import numpy as np import tensorflow as tf from matplotlib import pylab 
  • Download the Bangla wikipedia corpus from Kaggle. The first few lines from the corpus are shown below:

id,text,title,url

1528,

“রবীন্দ্রনাথ ঠাকুর”

রবীন্দ্রনাথ ঠাকুর (৭ই মে, ১৮৬১ – ৭ই আগস্ট, ১৯৪১) (২৫ বৈশাখ, ১২৬৮ – ২২ শ্রাবণ, ১৩৪৮ বঙ্গাব্দ) ছিলেন অগ্রণী বাঙালি কবি, ঔপন্যাসিক, সংগীতস্রষ্টা, নাট্যকার, চিত্রকর, ছোটগল্পকার, প্রাবন্ধিক, অভিনেতা, কণ্ঠশিল্পী ও দার্শনিক। তাঁকে বাংলা ভাষার সর্বশ্রেষ্ঠ সাহিত্যিক মনে করা হয়। রবীন্দ্রনাথকে গুরুদেব, কবিগুরু ও বিশ্বকবি অভিধায় ভূষিত করা হয়। রবীন্দ্রনাথের ৫২টি কাব্যগ্রন্থ, ৩৮টি নাটক, ১৩টি উপন্যাস ও ৩৬টি প্রবন্ধ ও অন্যান্য গদ্যসংকলন তাঁর জীবদ্দশায় বা মৃত্যুর অব্যবহিত পরে প্রকাশিত হয়। তাঁর সর্বমোট ৯৫টি ছোটগল্প ও ১৯১৫টি গান যথাক্রমে “”গল্পগুচ্ছ”” ও “”গীতবিতান”” সংকলনের অন্তর্ভুক্ত হয়েছে। রবীন্দ্রনাথের যাবতীয় প্রকাশিত ও গ্রন্থাকারে অপ্রকাশিত রচনা ৩২ খণ্ডে “”রবীন্দ্র রচনাবলী”” নামে প্রকাশিত হয়েছে। রবীন্দ্রনাথের যাবতীয় পত্রসাহিত্য উনিশ খণ্ডে “”চিঠিপত্র”” ও চারটি পৃথক গ্রন্থে প্রকাশিত। এছাড়া তিনি প্রায় দুই হাজার ছবি এঁকেছিলেন। রবীন্দ্রনাথের রচনা বিশ্বের বিভিন্ন ভাষায় অনূদিত হয়েছে। ১৯১৩ সালে “”গীতাঞ্জলি”” কাব্যগ্রন্থের ইংরেজি অনুবাদের জন্য তিনি সাহিত্যে নোবেল পুরস্কার লাভ করেন।রবীন্দ্রনাথ ঠাকুর কলকাতার এক ধনাঢ্য ও সংস্কৃতিবান ব্রাহ্ম পিরালী ব্রাহ্মণ পরিবারে জন্মগ্রহণ করেন। বাল্যকালে প্রথাগত বিদ্যালয়-শিক্ষা তিনি গ্রহণ করেননি; গৃহশিক্ষক রেখে বাড়িতেই তাঁর শিক্ষার ব্যবস্থা করা হয়েছিল। আট বছর বয়সে তিনি কবিতা লেখা শুরু করেন। ১৮৭৪ সালে “”তত্ত্ববোধিনী পত্রিকা””-এ তাঁর “””” কবিতাটি প্রকাশিত হয়। এটিই ছিল তাঁর প্রথম প্রকাশিত রচনা। ১৮৭৮ সালে মাত্র সতেরো বছর বয়সে রবীন্দ্রনাথ প্রথমবার ইংল্যান্ডে যান। ১৮৮৩ সালে মৃণালিনী দেবীর সঙ্গে তাঁর বিবাহ হয়। ১৮৯০ সাল থেকে রবীন্দ্রনাথ পূর্ববঙ্গের শিলাইদহের জমিদারি এস্টেটে বসবাস শুরু করেন। ১৯০১ সালে তিনি পশ্চিমবঙ্গের শান্তিনিকেতনে ব্রহ্মচর্যাশ্রম প্রতিষ্ঠা করেন এবং সেখানেই পাকাপাকিভাবে বসবাস শুরু করেন। ১৯০২ সালে তাঁর পত্নীবিয়োগ হয়। ১৯০৫ সালে তিনি বঙ্গভঙ্গ-বিরোধী আন্দোলনে জড়িয়ে পড়েন। ১৯১৫ সালে ব্রিটিশ সরকার তাঁকে নাইট উপাধিতে ভূষিত করেন। কিন্তু ১৯১৯ সালে জালিয়ানওয়ালাবাগ হত্যাকাণ্ডের প্রতিবাদে তিনি সেই উপাধি ত্যাগ করেন। ১৯২১ সালে গ্রামোন্নয়নের জন্য তিনি শ্রীনিকেতন নামে একটি সংস্থা প্রতিষ্ঠা করেন। ১৯২৩ সালে আনুষ্ঠানিকভাবে বিশ্বভারতী প্রতিষ্ঠিত হয়। দীর্ঘজীবনে তিনি বহুবার বিদেশ ভ্রমণ করেন এবং সমগ্র বিশ্বে বিশ্বভ্রাতৃত্বের বাণী প্রচার করেন। ১৯৪১ সালে দীর্ঘ রোগভোগের পর কলকাতার পৈত্রিক বাসভবনেই তাঁর মৃত্যু হয়।রবীন্দ্রনাথের কাব্যসাহিত্যের বৈশিষ্ট্য ভাবগভীরতা, গীতিধর্মিতা চিত্ররূপময়তা, অধ্যাত্মচেতনা, ঐতিহ্যপ্রীতি, প্রকৃতিপ্রেম, মানবপ্রেম, স্বদেশপ্রেম, বিশ্বপ্রেম, রোম্যান্টিক সৌন্দর্যচেতনা, ভাব, ভাষা, ছন্দ ও আঙ্গিকের বৈচিত্র্য, বাস্তবচেতনা ও প্রগতিচেতনা। রবীন্দ্রনাথের গদ্যভাষাও কাব্যিক। ভারতের ধ্রুপদি ও লৌকিক সংস্কৃতি এবং পাশ্চাত্য বিজ্ঞানচেতনা ও শিল্পদর্শন তাঁর রচনায় গভীর প্রভাব বিস্তার করেছিল। কথাসাহিত্য ও প্রবন্ধের মাধ্যমে তিনি সমাজ, রাজনীতি ও রাষ্ট্রনীতি সম্পর্কে নিজ মতামত প্রকাশ করেছিলেন। সমাজকল্যাণের উপায় হিসেবে তিনি গ্রামোন্নয়ন ও গ্রামের দরিদ্র মানুষ কে শিক্ষিত করে তোলার পক্ষে মতপ্রকাশ করেন। এর পাশাপাশি সামাজিক ভেদাভেদ, অস্পৃশ্যতা, ধর্মীয় গোঁড়ামি ও ধর্মান্ধতার বিরুদ্ধেও তিনি তীব্র প্রতিবাদ জানিয়েছিলেন। রবীন্দ্রনাথের দর্শনচেতনায় ঈশ্বরের মূল হিসেবে মানব সংসারকেই নির্দিষ্ট করা হয়েছে; রবীন্দ্রনাথ দেববিগ্রহের পরিবর্তে কর্মী অর্থাৎ মানুষ ঈশ্বরের পূজার কথা বলেছিলেন। সংগীত ও নৃত্যকে তিনি শিক্ষার অপরিহার্য অঙ্গ মনে করতেন। রবীন্দ্রনাথের গান তাঁর অন্যতম শ্রেষ্ঠ কীর্তি। তাঁর রচিত “”আমার সোনার বাংলা”” ও “”জনগণমন-অধিনায়ক জয় হে”” গানদুটি যথাক্রমে গণপ্রজাতন্ত্রী বাংলাদেশ ও ভারতীয় প্রজাতন্ত্রের জাতীয় সংগীত।

জীবন.

প্রথম জীবন (১৮৬১–১৯০১).

শৈশব ও কৈশোর (১৮৬১ – ১৮৭৮).
রবীন্দ্রনাথ ঠাকুর কলকাতার জোড়াসাঁকো ঠাকুরবাড়িতে জন্মগ্রহণ করেছিলেন। তাঁর পিতা ছিলেন ব্রাহ্ম ধর্মগুরু দেবেন্দ্রনাথ ঠাকুর (১৮১৭–১৯০৫) এবং মাতা ছিলেন সারদাসুন্দরী দেবী (১৮২৬–১৮৭৫)। রবীন্দ্রনাথ ছিলেন পিতামাতার চতুর্দশ সন্তান। জোড়াসাঁকোর ঠাকুর পরিবার ছিল ব্রাহ্ম আদিধর্ম মতবাদের প্রবক্তা। রবীন্দ্রনাথের পূর্ব পুরুষেরা খুলনা জেলার রূপসা উপজেলা পিঠাভোগে বাস করতেন। ১৮৭৫ সালে মাত্র চোদ্দ বছর বয়সে রবীন্দ্রনাথের মাতৃবিয়োগ ঘটে। পিতা দেবেন্দ্রনাথ দেশভ্রমণের নেশায় বছরের অধিকাংশ সময় কলকাতার বাইরে অতিবাহিত করতেন। তাই ধনাঢ্য পরিবারের সন্তান হয়েও রবীন্দ্রনাথের ছেলেবেলা কেটেছিল ভৃত্যদের অনুশাসনে। শৈশবে রবীন্দ্রনাথ কলকাতার ওরিয়েন্টাল সেমিনারি, নর্ম্যাল স্কুল, বেঙ্গল অ্যাকাডেমি এবং সেন্ট জেভিয়ার্স কলেজিয়েট স্কুলে কিছুদিন করে পড়াশোনা করেছিলেন। কিন্তু বিদ্যালয়-শিক্ষায় অনাগ্রহী হওয়ায় বাড়িতেই গৃহশিক্ষক রেখে তাঁর শিক্ষার ব্যবস্থা করা হয়েছিল। ছেলেবেলায় জোড়াসাঁকোর বাড়িতে অথবা বোলপুর ও পানিহাটির বাগানবাড়িতে প্রাকৃতিক পরিবেশের মধ্যে ঘুরে বেড়াতে বেশি স্বচ্ছন্দবোধ করতেন রবীন্দ্রনাথ।১৮৭৩ সালে এগারো বছর বয়সে রবীন্দ্রনাথের উপনয়ন অনুষ্ঠিত হয়েছিল। এরপর তিনি কয়েক মাসের জন্য পিতার সঙ্গে দেশভ্রমণে বের হন। প্রথমে তাঁরা আসেন শান্তিনিকেতনে। এরপর পাঞ্জাবের অমৃতসরে কিছুকাল কাটিয়ে শিখদের উপাসনা পদ্ধতি পরিদর্শন করেন। শেষে পুত্রকে নিয়ে দেবেন্দ্রনাথ যান পাঞ্জাবেরই (অধুনা ভারতের হিমাচল প্রদেশ রাজ্যে অবস্থিত) ডালহৌসি শৈলশহরের নিকট বক্রোটায়। এখানকার বক্রোটা বাংলোয় বসে রবীন্দ্রনাথ পিতার কাছ থেকে সংস্কৃত ব্যাকরণ, ইংরেজি, জ্যোতির্বিজ্ঞান, সাধারণ বিজ্ঞান ও ইতিহাসের নিয়মিত পাঠ নিতে শুরু করেন। দেবেন্দ্রনাথ তাঁকে বিশিষ্ট ব্যক্তিবর্গের জীবনী, কালিদাস রচিত ধ্রুপদি সংস্কৃত কাব্য ও নাটক এবং উপনিষদ্‌ পাঠেও উৎসাহিত করতেন। ১৮৭৭ সালে “”ভারতী”” পত্রিকায় তরুণ রবীন্দ্রনাথের কয়েকটি গুরুত্বপূর্ণ রচনা প্রকাশিত হয়। এগুলি হল মাইকেল মধুসূদনের “”””, “”ভানুসিংহ ঠাকুরের পদাবলী”” এবং “””” ও “””” নামে দুটি গল্প। এর মধ্যে “”ভানুসিংহ ঠাকুরের পদাবলী”” বিশেষভাবে উল্লেখযোগ্য। এই কবিতাগুলি রাধা-কৃষ্ণ বিষয়ক পদাবলির অনুকরণে “”ভানুসিংহ”” ভণিতায় রচিত। রবীন্দ্রনাথের “”ভিখারিণী”” গল্পটি (১৮৭৭) বাংলা সাহিত্যের প্রথম ছোটগল্প। ১৮৭৮ সালে প্রকাশিত হয় রবীন্দ্রনাথের প্রথম কাব্যগ্রন্থ তথা প্রথম মুদ্রিত গ্রন্থ “”কবিকাহিনী””। এছাড়া এই পর্বে তিনি রচনা করেছিলেন “””” (১৮৮২) কাব্যগ্রন্থটি। রবীন্দ্রনাথের বিখ্যাত কবিতা “””” এই কাব্যগ্রন্থের অন্তর্গত।

যৌবন (১৮৭৮-১৯০১).
১৮৭৮ সালে ব্যারিস্টারি পড়ার উদ্দেশ্যে ইংল্যান্ডে যান রবীন্দ্রনাথ। প্রথমে তিনি ব্রাইটনের একটি পাবলিক স্কুলে ভর্তি হয়েছিলেন। ১৮৭৯ সালে ইউনিভার্সিটি কলেজ লন্ডনে আইনবিদ্যা নিয়ে পড়াশোনা শুরু করেন। কিন্তু সাহিত্যচর্চার আকর্ষণে সেই পড়াশোনা তিনি সমাপ্ত করতে পারেননি। ইংল্যান্ডে থাকাকালীন শেকসপিয়র ও অন্যান্য ইংরেজ সাহিত্যিকদের রচনার সঙ্গে রবীন্দ্রনাথের পরিচয় ঘটে। এই সময় তিনি বিশেষ মনোযোগ সহকারে পাঠ করেন “”রিলিজিও মেদিচি””, “”কোরিওলেনাস”” এবং “”অ্যান্টনি অ্যান্ড ক্লিওপেট্রা””। এই সময় তাঁর ইংল্যান্ডবাসের অভিজ্ঞতার কথা “”ভারতী”” পত্রিকায় পত্রাকারে পাঠাতেন রবীন্দ্রনাথ। উক্ত পত্রিকায় এই লেখাগুলি জ্যেষ্ঠভ্রাতা দ্বিজেন্দ্রনাথ ঠাকুরের সমালোচনাসহ প্রকাশিত হত “””” নামে। ১৮৮১ সালে সেই পত্রাবলি “””” নামে গ্রন্থাকারে ছাপা হয়। এটিই ছিল রবীন্দ্রনাথের প্রথম গদ্যগ্রন্থ তথা প্রথম চলিত ভাষায় লেখা গ্রন্থ। অবশেষে ১৮৮০ সালে প্রায় দেড় বছর ইংল্যান্ডে কাটিয়ে কোনো ডিগ্রি না নিয়ে এবং ব্যারিস্টারি পড়া শুরু না করেই তিনি দেশে ফিরে আসেন।১৮৮৩ সালের ৯ ডিসেম্বর (২৪ অগ্রহায়ণ, ১২৯০ বঙ্গাব্দ) ঠাকুরবাড়ির অধস্তন কর্মচারী বেণীমাধব রায়চৌধুরীর কন্যা ভবতারিণীর সঙ্গে রবীন্দ্রনাথের বিবাহ সম্পন্ন হয়। বিবাহিত জীবনে ভবতারিণীর নামকরণ হয়েছিল মৃণালিনী দেবী (১৮৭৩–১৯০২ )। রবীন্দ্রনাথ ও মৃণালিনীর সন্তান ছিলেন পাঁচ জন: মাধুরীলতা (১৮৮৬–১৯১৮), রথীন্দ্রনাথ (১৮৮৮–১৯৬১), রেণুকা (১৮৯১–১৯০৩), মীরা (১৮৯৪–১৯৬৯) এবং শমীন্দ্রনাথ (১৮৯৬–১৯০৭)। এঁদের মধ্যে অতি অল্প বয়সেই রেণুকা ও শমীন্দ্রনাথের মৃত্যু ঘটে।১৮৯১ সাল থেকে পিতার আদেশে নদিয়া (নদিয়ার উক্ত অংশটি অধুনা বাংলাদেশের কুষ্টিয়া জেলা), পাবনা ও রাজশাহী জেলা এবং উড়িষ্যার জমিদারিগুলির তদারকি শুরু করেন রবীন্দ্রনাথ। কুষ্টিয়ার শিলাইদহের কুঠিবাড়িতে রবীন্দ্রনাথ দীর্ঘ সময় অতিবাহিত করেছিলেন। জমিদার রবীন্দ্রনাথ শিলাইদহে “”পদ্মা”” নামে একটি বিলাসবহুল পারিবারিক বজরায় চড়ে প্রজাবর্গের কাছে খাজনা আদায় ও আশীর্বাদ প্রার্থনা করতে যেতেন। গ্রামবাসীরাও তাঁর সম্মানে ভোজসভার আয়োজন করত।১৮৯০ সালে রবীন্দ্রনাথের অপর বিখ্যাত কাব্যগ্রন্থ “””” প্রকাশিত হয়। কুড়ি থেকে ত্রিশ বছর বয়সের মধ্যে তাঁর আরও কয়েকটি উল্লেখযোগ্য কাব্যগ্রন্থ ও গীতিসংকলন প্রকাশিত হয়েছিল। এগুলি হলো “”””, “”””, “”রবিচ্ছায়া””, “””” ইত্যাদি। ১৮৯১ থেকে ১৮৯৫ সাল পর্যন্ত নিজের সম্পাদিত “”সাধনা”” পত্রিকায় রবীন্দ্রনাথের বেশ কিছু উৎকৃষ্ট রচনা প্রকাশিত হয়। তাঁর সাহিত্যজীবনের এই পর্যায়টি তাই “”সাধনা পর্যায়”” নামে পরিচিত। রবীন্দ্রনাথের “”গল্পগুচ্ছ”” গ্রন্থের প্রথম চুরাশিটি গল্পের অর্ধেকই এই পর্যায়ের রচনা। এই ছোটগল্পগুলিতে তিনি বাংলার গ্রামীণ জনজীবনের এক আবেগময় ও শ্লেষাত্মক চিত্র এঁকেছিলেন।

  • Preprocess the csv files with the following code using regular expressions (to get rid of punctuations). Remember we need to decode to utf-8 first, since we have unicode input files.
  from glob import glob import re words = [] for f in glob('bangla/wiki/*.csv'):     words += re.sub('[rn—?,;।!‘"’.:()[]…0-9]', ' ', open(f, 'rb').read().decode('utf8').strip()).split(' ') words = list(filter(lambda x: not x in ['', '-'], words)) print(len(words)) # 13964346 words[:25] #['রবীন্দ্রনাথ', # 'ঠাকুর', # 'রবীন্দ্রনাথ', # 'ঠাকুর', # '৭ই', # 'মে', # '১৮৬১', # '৭ই', # 'আগস্ট', # '১৯৪১', # '২৫', # 'বৈশাখ', # '১২৬৮', # '২২', # 'শ্রাবণ', # '১৩৪৮', # 'বঙ্গাব্দ', # 'ছিলেন', # 'অগ্রণী', # 'বাঙালি', # 'কবি', # 'ঔপন্যাসিক', # 'সংগীতস্রষ্টা', # 'নাট্যকার', # 'চিত্রকর']  
  • Create indices for unique words in the dataset.
vocabulary_size = 25000 def build_dataset(words):   count = [['UNK', -1]]   count.extend(collections.Counter(words).most_common(vocabulary_size - 1))   dictionary = dict()   for word, _ in count:     dictionary[word] = len(dictionary)   data = list()   unk_count = 0   for word in words:     if word in dictionary:       index = dictionary[word]     else:       index = 0  # dictionary['UNK']       unk_count = unk_count + 1     data.append(index)   count[0][1] = unk_count   reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))    return data, count, dictionary, reverse_dictionary  data, count, dictionary, reverse_dictionary = build_dataset(words) print('Most common words (+UNK)', count[:5]) # Most common words (+UNK) [['UNK', 1961151], ('এবং', 196916), ('ও', 180042), ('হয়', 160533), ('করে', 131206)] print('Sample data', data[:10]) #Sample data [1733, 1868, 1733, 1868, 5769, 287, 6855, 5769, 400, 2570] del words  # Hint to reduce memory. 
  • Generate batches to be trained with the word2vec skip-gram model.
  • The target label should be at the center of the buffer each time. That is, given a focus word, our goal will be to learn the most probable context words.
  • The input and the target vector will depend on num_skips and skip_window.
  data_index = 0 def generate_batch(batch_size, num_skips, skip_window):   global data_index   assert batch_size % num_skips == 0   assert num_skips <= 2 * skip_window   batch = np.ndarray(shape=(batch_size), dtype=np.int32)   labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)   span = 2 * skip_window + 1 # [ skip_window target skip_window ]   buffer = collections.deque(maxlen=span)   for _ in range(span):     buffer.append(data[data_index])     data_index = (data_index + 1) % len(data)   for i in range(batch_size // num_skips):     target = skip_window  #      targets_to_avoid = [ skip_window ]     for j in range(num_skips):       while target in targets_to_avoid:         target = random.randint(0, span - 1)       targets_to_avoid.append(target)       batch[i * num_skips + j] = buffer[skip_window]       labels[i * num_skips + j, 0] = buffer[target]     buffer.append(data[data_index])     data_index = (data_index + 1) % len(data)   return batch, labels  print('data:', [reverse_dictionary[di] for di in data[:8]]) # data: ['রবীন্দ্রনাথ', 'ঠাকুর', 'রবীন্দ্রনাথ', 'ঠাকুর', '৭ই', 'মে', '১৮৬১', '৭ই'] for num_skips, skip_window in [(2, 1), (4, 2)]:     data_index = 0     batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)     print('nwith num_skips = %d and skip_window = %d:' %            (num_skips, skip_window))     print('    batch:', [reverse_dictionary[bi] for bi in batch])     print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])     # data: ['রবীন্দ্রনাথ', 'ঠাকুর', 'রবীন্দ্রনাথ', 'ঠাকুর', '৭ই', 'মে',  '১৮৬১', '৭ই']     # with num_skips = 2 and skip_window = 1:     # batch: ['ঠাকুর', 'ঠাকুর', 'রবীন্দ্রনাথ', 'রবীন্দ্রনাথ', 'ঠাকুর', 'ঠাকুর',  '৭ই', '৭ই']     # labels: ['রবীন্দ্রনাথ', 'রবীন্দ্রনাথ', 'ঠাকুর', 'ঠাকুর', '৭ই', 'রবীন্দ্রনাথ', 'ঠাকুর', 'মে']     # with num_skips = 4 and skip_window = 2:     # batch: ['রবীন্দ্রনাথ', 'রবীন্দ্রনাথ', 'রবীন্দ্রনাথ', 'রবীন্দ্রনাথ', 'ঠাকুর', 'ঠাকুর', 'ঠাকুর', 'ঠাকুর']     # labels: ['রবীন্দ্রনাথ', '৭ই', 'ঠাকুর', 'ঠাকুর', 'মে', 'ঠাকুর', 'রবীন্দ্রনাথ', '৭ই'] 
  • Pick a random validation set to sample nearest neighbors.
  • Limit the validation samples to the words that have a low numeric ID, which by construction are also the most frequent.
  • Look up embeddings for inputs and compute the softmax loss, using a sample of the negative labels each time (this is known as negative sampling, which is used to make the computation efficient, since the number of labels are often too high).
  • The optimizer will optimize the softmax_weights and the embeddings.
    This is because the embeddings are defined as a variable quantity and the optimizer’s `minimize` method will by default modify all variable quantities that contribute to the tensor it is passed.
  • Compute the similarity between minibatch examples and all embeddings.
   batch_size = 128  embedding_size = 128 # Dimension of the embedding vector.  skip_window = 1 # How many words to consider left and right.  num_skips = 2 # #times to reuse an input to generate a label.  valid_size = 16 # Random set of words to evaluate similarity on.  valid_window = 100 # Only pick dev samples in the head of the                        # distribution.  valid_examples = np.array(random.sample(range(valid_window),                                           valid_size))  num_sampled = 64 # Number of negative examples to sample.  graph = tf.Graph()  with graph.as_default(), tf.device('/cpu:0'):    # Input data.    train_dataset = tf.placeholder(tf.int32, shape=[batch_size])    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)  # Variables.    embeddings = tf.Variable(       tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))    softmax_weights = tf.Variable(       tf.truncated_normal([vocabulary_size, embedding_size],                            stddev=1.0 / math.sqrt(embedding_size)))    softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))  # Model.    embed = tf.nn.embedding_lookup(embeddings, train_dataset)    loss = tf.reduce_mean(                            tf.nn.sampled_softmax_loss(weights=softmax_weights,                               biases=softmax_biases, inputs=embed, labels=train_labels,                             num_sampled=num_sampled, num_classes=vocabulary_size))  # Optimizer.  optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)    # use the cosine distance:  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))    normalized_embeddings = embeddings / norm    valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)    similarity = tf.matmul(valid_embeddings,  tf.transpose(normalized_embeddings))  
  • Train the word2vec model with the batches constructed, for 100k steps.
  num_steps = 100001with tf.Session(graph=graph) as session:   tf.global_variables_initializer().run()   print('Initialized')   average_loss = 0   for step in range(num_steps):     batch_data, batch_labels = generate_batch(       batch_size, num_skips, skip_window)     feed_dict = {train_dataset : batch_data,                   train_labels : batch_labels}     _, l = session.run([optimizer, loss], feed_dict=feed_dict)     average_loss += l     if step % 2000 == 0:       if step > 0:         average_loss = average_loss / 2000       # The average loss is an estimate of the loss over the last        # 2000 batches.       print('Average loss at step %d: %f' % (step, average_loss))       average_loss = 0       # note that this is expensive (~20% slowdown if computed every        # 500 steps)     if step % 10000 == 0:       sim = similarity.eval()       for i in range(valid_size):         valid_word = reverse_dictionary[valid_examples[i]]         top_k = 8 # number of nearest neighbors         nearest = (-sim[i, :]).argsort()[1:top_k+1]         log = 'Nearest to %s:' % valid_word         for k in range(top_k):           close_word = reverse_dictionary[nearest[k]]           log = '%s %s,' % (log, close_word)         print(log)   final_embeddings = normalized_embeddings.eval() 
  • The following shows how the loss function decreases with the increase in training steps.
  • During the training process, the words that become semantically near come closer in the embedding space.

Image for post

  • Use t-SNE plot to map the following words from 128-dimensional embedding space to 2 dimensional manifold and visualize.
  words = ['রাজা', 'রাণী', 'ভারত','বাংলাদেশ','দিল্লী','কলকাতা','ঢাকা',          'পুরুষ','নারী','দুঃখ','লেখক','কবি','কবিতা','দেশ',          'বিদেশ','লাভ','মানুষ', 'এবং', 'ও', 'গান', 'সঙ্গীত', 'বাংলা',           'ইংরেজি', 'ভাষা', 'কাজ', 'অনেক', 'জেলার', 'বাংলাদেশের',           'এক', 'দুই', 'তিন', 'চার', 'পাঁচ', 'দশ', '১', '৫', '২০',           'নবম', 'ভাষার', '১২', 'হিসাবে', 'যদি', 'পান', 'শহরের', 'দল',           'যদিও', 'বলেন', 'রান', 'করেছে', 'করে', 'এই', 'করেন', 'তিনি',           'একটি', 'থেকে', 'করা', 'সালে', 'এর', 'যেমন', 'সব',  'তার',           'খেলা',  'অংশ', 'উপর', 'পরে', 'ফলে',  'ভূমিকা', 'গঠন',            'তা', 'দেন', 'জীবন', 'যেখানে', 'খান', 'এতে',  'ঘটে', 'আগে',           'ধরনের', 'নেন', 'করতেন', 'তাকে', 'আর', 'যার', 'দেখা',           'বছরের', 'উপজেলা', 'থাকেন', 'রাজনৈতিক', 'মূলত', 'এমন',           'কিলোমিটার', 'পরিচালনা', '২০১১', 'তারা', 'তিনি', 'যিনি', 'আমি',            'তুমি', 'আপনি', 'লেখিকা', 'সুখ', 'বেদনা', 'মাস', 'নীল', 'লাল',           'সবুজ', 'সাদা', 'আছে', 'নেই', 'ছুটি', 'ঠাকুর',          'দান', 'মণি', 'করুণা', 'মাইল', 'হিন্দু', 'মুসলমান','কথা', 'বলা',               'সেখানে', 'তখন', 'বাইরে', 'ভিতরে', 'ভগবান' ] indices = [] for word in words:     #print(word, dictionary[word])     indices.append(dictionary[word]) two_d_embeddings = tsne.fit_transform(final_embeddings[indices, :]) plot(two_d_embeddings, words) 
  • The following figure shows how the words similar in meaning are mapped to embedding vectors that are close to each other.
  • Also, note that arithmetic property of the word embeddings: e.g., the words ‘রাজা’ and ‘রাণী’ are approximately along the same distance and direction as the words ‘লেখক’ and ‘লেখিকা’, reflecting the fact that the nature of the semantic relatedness in terms of gender is same.

  • The following animation shows how the embedding is learnt to preserve the semantic similarity in the 2D-manifold more and more as training proceeds.

Generating song-like texts with LSTM from Tagore’s Bangla songs

Text generation with Character LSTM

  • Let’s import the required libraries first.
from tensorflow.keras.callbacks import LambdaCallback from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.layers import LSTM from tensorflow.keras.optimizers import RMSprop, Adam import io, re 
  • Read the input file, containing few selected songs of Tagore in Bangla.
raw_text = open('rabindrasangeet.txt','rb').read().decode('utf8') print(raw_text[0:1000])  পূজা  অগ্নিবীণা বাজাও তুমি  অগ্নিবীণা বাজাও তুমি কেমন ক’রে ! আকাশ কাঁপে তারার আলোর গানের ঘোরে ।। তেমনি ক’রে আপন হাতে ছুঁলে আমার বেদনাতে, নূতন সৃষ্টি জাগল বুঝি জীবন-‘পরে ।। বাজে ব’লেই বাজাও তুমি সেই গরবে, ওগো প্রভু, আমার প্রাণে সকল সবে । বিষম তোমার বহ্নিঘাতে বারে বারে আমার রাতে জ্বালিয়ে দিলে নূতন তারা ব্যথায় ভ’রে ।।  অচেনাকে ভয় কী অচেনাকে ভয় কী আমার ওরে? অচেনাকেই চিনে চিনে উঠবে জীবন ভরে ।। জানি জানি আমার চেনা কোনো কালেই ফুরাবে না, চিহ্নহারা পথে আমায় টানবে অচিন ডোরে ।। ছিল আমার মা অচেনা, নিল আমায় কোলে । সকল প্রেমই অচেনা গো, তাই তো হৃদয় দোলে । অচেনা এই ভুবন-মাঝে কত সুরেই হৃদয় বাজে- অচেনা এই জীবন আমার, বেড়াই তারি ঘোরে ।।অন্তর মম অন্তর মম বিকশিত করো অন্তরতর হে- নির্মল করো, উজ্জ্বল করো, সুন্দর করো হে ।। জাগ্রত করো, উদ্যত করো, নির্ভয় করো হে ।। মঙ্গল করো, নিরলস নিঃসংশয় করো হে ।। যুক্ত করো হে সবার সঙ্গে, মুক্ত করো হে বন্ধ । সঞ্চার করো সকল কর্মে শান্ত তোমার ছন্দ । চরণপদ্মে মম চিত নিস্পন্দিত করো হে । নন্দিত করো, নন্দিত করো, নন্দিত করো হে ।।  অন্তরে জাগিছ অন্তর্যামী অন্তরে জাগিছ অন্তর্যামী । 
  • Here we shall be using a many-to-many RNN as shown in the next figure.

  • Pre-process the text and create character indices to be used as the input in the model.
  processed_text = raw_text.lower() print('corpus length:', len(processed_text)) # corpus length: 207117 chars = sorted(list(set(processed_text))) print('total chars:', len(chars)) # total chars: 89 char_indices = dict((c, i) for i, c in enumerate(chars)) indices_char = dict((i, c) for i, c in enumerate(chars)) 
  • Cut the text in semi-redundant sequences of maxlen characters.
  def is_conjunction(c):   h = ord(c) # print(hex(ord(c)))   return (h >= 0x980 and h = 0x9bc and h = 0x9f2)                 maxlen = 40 step = 2 sentences = [] next_chars = [] i = 0 while i < len(processed_text) - maxlen:   if is_conjunction(processed_text[i]):     i += 1     continue   sentences.append(processed_text[i: i + maxlen])   next_chars.append(processed_text[i + maxlen])   i += step   print('nb sequences:', len(sentences))   # nb sequences: 89334 
  • Create one-hot-encodings.
  x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool) y = np.zeros((len(sentences), len(chars)), dtype=np.bool) for i, sentence in enumerate(sentences):   for t, char in enumerate(sentence):     x[i, t, char_indices[char]] = 1     y[i, char_indices[next_chars[i]]] = 1 
  • Build a model, a single LSTM.
  model = Sequential() model.add(LSTM(256, input_shape=(maxlen, len(chars)))) model.add(Dense(128, activation='relu')) model.add(Dense(len(chars), activation='softmax')) optimizer = Adam(lr=0.01) #RMSprop(lr=0.01) model.compile(loss='categorical_crossentropy', optimizer=optimizer) 
  • The following figure how the model architecture looks like:

Image for post

  • Print the model summary.
  model.summary()  Model: "sequential"  _________________________________________________________________  Layer (type)                 Output Shape              Param #     =================================================================  lstm (LSTM)                  (None, 256)               354304      _________________________________________________________________  dense (Dense)                (None, 128)               32896       _________________________________________________________________  dense_1 (Dense)              (None, 89)                11481       =================================================================  Total params: 398,681 Trainable params: 398,681 Non-trainable params: 0  _________________________________________________________________ 
  • Use the following helper function to sample an index from a probability array.
  def sample(preds, temperature=1.0):   preds = np.asarray(preds).astype('float64')   preds = np.log(preds) / temperature   exp_preds = np.exp(preds)   preds = exp_preds / np.sum(exp_preds)   probas = np.random.multinomial(1, preds, 1)   return np.argmax(probas) 
  • Fit the model and register a callback to print the text generated by the model at the end of each epoch.
  print_callback = LambdaCallback(on_epoch_end=on_epoch_end) model.fit(x, y, batch_size=128, epochs=60, callbacks=[print_callback]) 
  • The following animation shows how the model generates song-like texts with given seed texts, for different values of the temperature parameter.

Text Generation with Word LSTM

  • Pre-process the input text, split by punctuation characters and create word indices to be used as the input in the model.
processed_text = raw_text.lower() from string import punctuation r = re.compile(r'[s{}]+'.format(re.escape(punctuation))) words = r.split(processed_text) print(len(words)) words[:16] 39481 # ['পূজা', # 'অগ্নিবীণা', # 'বাজাও', # 'তুমি', # 'অগ্নিবীণা', # 'বাজাও', # 'তুমি', # 'কেমন', # 'ক’রে', # 'আকাশ', # 'কাঁপে', # 'তারার', # 'আলোর', # 'গানের', # 'ঘোরে', # '।।']  unique_words = np.unique(words) unique_word_index = dict((c, i) for i, c in enumerate(unique_words)) index_unique_word = dict((i, c) for i, c in enumerate(unique_words)) 
  • Create a word-window of length 5 to predict the next word.
WORD_LENGTH = 5 prev_words = [] next_words = [] for i in range(len(words) - WORD_LENGTH):     prev_words.append(words[i:i + WORD_LENGTH])     next_words.append(words[i + WORD_LENGTH]) print(prev_words[1]) # ['অগ্নিবীণা', 'বাজাও', 'তুমি', 'অগ্নিবীণা', 'বাজাও'] print(next_words[1]) # তুমি print(len(unique_words)) # 7847 
  • Create OHE for input and output words as done for character-RNN. Fit the model on the pre-rpocessed data.
print_callback = LambdaCallback(on_epoch_end=on_epoch_end) model.fit(X, Y,           batch_size=128,           epochs=60,           callbacks=[print_callback]) 
  • The following animation shows the song -like text generated by the word-LSTM at the end of an epoc.

Bangla Sentiment Analysis using LSTM with Daily Astrological Prediction Dataset

  • Let’s first create sentiment analysis dataset by crawling the daily astrological predictions (রাশিফল) page of the online edition of আনন্দবাজার পত্রিকা (e.g., for the year 2013), a leading Bangla newspaper and then manually labeling the sentiment of each of the predictions corresponding to each moon-sign.
  • Read the csv dataset, the first few lines look like the following.
  df = pd.read_csv('horo_2013_labeled.csv') pd.set_option('display.max_colwidth', 135)  df.head(20) 

  • Transform each text in texts in a sequence of integers.
  tokenizer = Tokenizer(num_words=2000, split=' ') tokenizer.fit_on_texts(df['আপনার আজকের দিনটি'].values) X = tokenizer.texts_to_sequences(df['আপনার আজকের দিনটি'].values) X = pad_sequences(X) X #array([[   0,    0,    0, ...,   26,  375,    3],         #       [   0,    0,    0, ...,   54,    8,    1],         #       [   0,    0,    0, ...,  108,   42,   43],         #       ...,         #       [   0,    0,    0, ..., 1336,  302,   82],         #       [   0,    0,    0, ..., 1337,  489,  218],         #       [   0,    0,    0, ...,    2,  316,   87]]) 
  • Here we shall use a many-to-one RNN for sentiment analysis as shown below.

  • Build an LSTM model that takes a sentence as input and outputs the sentiment label.
model = Sequential() model.add(Embedding(2000, 128,input_length = X.shape[1])) model.add(SpatialDropout1D(0.3)) model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(2,activation='softmax')) model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy']) print(model.summary())  _________________________________________________________________ Layer (type)                 Output Shape              Param #    ================================================================= embedding_10 (Embedding)     (None, 12, 128)           256000     _________________________________________________________________ spatial_dropout1d_10 (Spatia (None, 12, 128)           0          _________________________________________________________________ lstm_10 (LSTM)               (None, 128)               131584     _________________________________________________________________ dense_10 (Dense)             (None, 2)                 258        ================================================================= Total params: 387,842 Trainable params: 387,842 Non-trainable params: 0 _________________________________________________________________ None 
  • Divide the dataset into train and validation (test) dataset and train the LSTM model on the dataset.
  Y = pd.get_dummies(df['sentiment']).values X_train, X_test, Y_train, Y_test, _, indices = train_test_split(X,Y, np.arange(len(X)), test_size = 0.33, random_state = 5) model.fit(X_train, Y_train, epochs = 5, batch_size=32, verbose = 2)  #Epoch 1/5  - 3s - loss: 0.6748 - acc: 0.5522  #Epoch 2/5  - 1s - loss: 0.5358 - acc: 0.7925  #Epoch 3/5  - 1s - loss: 0.2368 - acc: 0.9418  #Epoch 4/5  - 1s - loss: 0.1011 - acc: 0.9761  #Epoch 5/5  - 1s - loss: 0.0578 - acc: 0.9836  
  • Predict the sentiment labels of the (held out) test dataset.
  result = model.predict(X[indices],batch_size=1,verbose = 2) df1 = df.iloc[indices] df1['neg_prob'] = result[:,0] df1['pos_prob'] = result[:,1] df1['pred'] = np.array(['negative', 'positive'])[np.argmax(result, axis=1)] df1.head() 

  • Finally, compute the accuracy of the model for the positive and negative ground-truth sentiment corresponding to daily astrological predictions.
  df2 = df1[df1.sentiment == 'positive'] print('positive accuracy:' + str(np.mean(df2.sentiment == df2.pred))) #positive accuracy:0.9177215189873418 df2 = df1[df1.sentiment == 'negative'] print('negative accuracy:' + str(np.mean(df2.sentiment == df2.pred))) #negative accuracy:0.9352941176470588 

Building a very simple Bangla Chatbot with RASA NLU

  • The following figure shows how to design a very simple Bangla chatbot to order food from restaurants using RASA NLU.
  • We need to design the intents, entities and slots to extract the entities properly and then design stories to define how the chatbot will respond to user inputs (core / dialog).
  • The following figure shows how the nlu, domain and stories files are written for the simple chatbot.

  • A sequence-to-sequence deep learning model is trained under the hood for intent classification. The next code block shows how the model can be trained.
import rasa model_path = rasa.train('domain.yml', 'config.yml', ['data/'], 'models/') 
  • The following gif demonstrates how the chatbot responds to user inputs.

References

How AI Is Upending the B2B Sales Experience

Artificial intelligence (AI) is the horsepower of the future, and its importance going forward was even more emphasized due to the Covid 19 pandemic. Being valued at $27.23 billion as of 2019, the artificial intelligence market is expected to reach $267 billion globally by 2027, a 1000% increase in the space of eight years.

AI is set to have a huge impact on the sales process over the coming years with 91.5% of top businesses already having an ongoing investment in artificial intelligence. Buyer analytics provided by AI will give key insight to help get deals across the line.

 How exactly is AI upending the B2B sales experience? Three major aspects are discussed below:

 

Optimizing processes

57% of sales reps are expected to miss quota this year. This would be largely due to inefficient and unorganized sales processes.

 However, artificial intelligence is expected to facilitate great strides in the direction of sales process optimization. This would begin with sales onboarding. A study of some companies showed that onboarding of sales reps takes about 4.5 months. Some companies may even take over 7 months for new reps to get fully productive.

Using its analytical capabilities, AI will serve as a guide to new sales reps, giving them insight on how often they should reach out to a possible buyer and how to close deals efficiently. This will reduce the time it takes to onboard new reps because they are guided to taking the most effective courses of action.

 AI will also play a major role in facilitating processes such as the preparation of content. This was a job normally left to the employee who could write the best essay, if there was no such person, the company made do with the best essay writing services it could find. Writing experts at Dissertation Today say that receiving such offers from companies is quite common.

Features like automation and Natural Language Processing (NLP) would make these processes much more seamless. Lesser tasks can now be automated, giving sales reps more time to focus on more productive issues. AI will allow data to be collected in one place, making it more accessible for meetings with potential buyers.

 This takes us to our next point.

 

Automation of mundane sales tasks

Scheduling meetings are a painstaking process. More was time is spent on scheduling meetings than on the actual meetings themselves. Sales reps could spend up to 4.75 hours arranging meetings; from sending out reminders to communicating new information.

 However, AI has given us automation. There are several new software that use AI-powered automation to make these processes so much easier.

 AI can also help automate the tracking and acquisition of buyer data. So, the sales reps pay proper attention to the needs of the buyer. 

 

Personalization

77% of buyers prefer a brand that offers a personalized experience. Sadly, a study by Forrester shows that nearly 8 in 10 buyers say sales reps meet them with irrelevant content.

AI brings an entirely new dimension to personalization in the B2B sales experience. This will largely affect lead scoring. As of now, lead scoring has to do with ranking leads to find out their sales-readiness. Lead scoring is done based on the level of interest showed in your business, how they factor into your business strategy, and their current position in the buying cycle. With AI managing all the data concerning marketing, buyers, and sales, things would become easier for sales reps. Sales reps will be handed information that is tailored specifically to the needs of each lead.

The amount of accurate content available to salespeople will also be dramatically affected by artificial intelligence. The Internet of Things (IoT) poses quite some interesting possibilities when you integrate the data coming from it. The main purpose of such data would be to monitor products. But it’s likely that a situation in which, for instance, Airbus, after monitoring their own products digitally, comes up with a personalized product list for Virgin America at the exact time they may be considering replacing a particular machine part on some planes in their fleet.

Wrapping it all up

The application of artificial intelligence to the B2B sales process will have a huge effect on the way sales reps work. One great benefit of using AI will be feedback generation. Data from when customers first see a product to monitoring those they later acquire can be collected and used to improve customer experience. AI will change the way sales and marketing work, bringing more efficiency and intelligence.

 Artificial intelligence is still surrounded by certain myths. This may leave companies feeling skeptical about adopting this technology. However, AI is the sales rep’s best friend. It facilitates healthier and better relationships between reps and customers, thus increasing company success.

 

Author Bio:

Ashley Simmons is a professional journalist and editor. She has been working in a newspaper in Salt Lake City for 4 years. She is also a content writing expert on topics such as psychology, modern education, business, and marketing innovations. She is a master in her craft.

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