Key Topics for Random Processes & Statistics and Probability

Comprehensive List of Topics for Random Processes:

1. Stochastic Processes

2. Markov Chains

3. Continuous-Time Markov Chains

4. Markov Decision Processes (MDPs)

5. Random Walks

6. Poisson Processes

7. Renewal Processes

8. Stationary Processes

9. Weak and Strong Stationarity

10. Autocorrelation Function

11. Autoregressive Processes (AR)

12. Moving Average Processes (MA)

13. ARMA and ARIMA Models

14. ARCH and GARCH Models

15. Ergodicity

16. Brownian Motion (Wiener Process)

17. Fractional Brownian Motion

18. Gaussian Processes

19. Lévy Processes

20. Martingales

21. Submartingales and Supermartingales

22. Random Fields

23. Spectral Analysis of Time Series

24. Power Spectral Density

25. Cross-Correlation and Cross-Spectrum

26. Queuing Theory

27. Random Walk Hypothesis

28. Mean Reversion

29. Wiener-Khinchin Theorem

30. Entropy and Information Theory

31. Fokker-Planck Equation

32. Kolmogorov Equations

33. Jump Processes

34. Semi-Markov Processes

35. Diffusion Processes

36. Stochastic Differential Equations (SDEs)

37. Ito’s Lemma

38. Langevin Equation

39. Filtering Theory (e.g., Kalman Filter)

40. Random Measures

41. Cox Processes

42. Birth-Death Processes

43. Time Series Analysis

44. Hidden Markov Models (HMM)

45. Self-Similar Processes

46. Long-Range Dependence

47. Hawkes Processes

48. Empirical Processes

49. Random Matrices

50. Random Graphs

Comprehensive List of Topics for Probability and Statistics:

1. Basic Probability Theory

2. Axioms of Probability

3. Random Variables

4. Probability Mass Function (PMF)

5. Probability Density Function (PDF)

6. Cumulative Distribution Function (CDF)

7. Joint, Marginal, and Conditional Distributions

8. Expected Value (Mean)

9. Variance and Standard Deviation

10. Covariance and Correlation

11. Skewness and Kurtosis

12. Moments and Moment Generating Functions

13. Chebyshev’s Inequality

14. Probability Generating Functions

15. Characteristic Functions

16. Law of Large Numbers

17. Central Limit Theorem

18. Convergence in Probability and Distribution

19. Bayes’ Theorem

20. Bayesian Inference

21. Prior and Posterior Distributions

22. Hypothesis Testing

23. p-Values

24. Type I and Type II Errors

25. Confidence Intervals

26. Sampling Distributions

27. Point Estimation

28. Maximum Likelihood Estimation (MLE)

29. Method of Moments

30. Bayesian Estimation

31. Interval Estimation

32. Sampling Theory

33. Markov Property

34. Monte Carlo Methods

35. Bootstrap and Resampling Methods

36. Permutation Tests

37. Experimental Design

38. Analysis of Variance (ANOVA)

39. Factorial Designs

40. Regression Analysis

41. Linear Regression

42. Multiple Linear Regression

43. Logistic Regression

44. Polynomial Regression

45. Generalized Linear Models (GLM)

46. Mixed-Effects Models

47. Time Series Analysis

48. Non-parametric Statistics

49. Parametric vs. Non-Parametric Tests

50. Goodness-of-Fit Tests (e.g., Chi-Square Test)

51. Multivariate Statistics

52. Principal Component Analysis (PCA)

53. Factor Analysis

54. Discriminant Analysis

55. Canonical Correlation Analysis

56. Clustering (K-means, Hierarchical)

57. Classification Techniques

58. Decision Trees

59. Random Forests

60. Support Vector Machines (SVM)

61. Naive Bayes Classifier

62. Bayesian Networks

63. Hidden Markov Models (HMMs)

64. Time Series Forecasting

65. AR, MA, and ARIMA Models

66. Seasonal Decomposition

67. Exponential Smoothing

68. Cointegration and Error Correction Models

69. Time-Varying Volatility Models (ARCH/GARCH)

70. Survival Analysis

71. Reliability Theory

72. Extreme Value Theory

73. Risk Analysis

74. Quality Control and SPC

75. Experimental Design and RCTs

76. Empirical Bayes Methods

77. Robust Statistics

78. Statistical Learning Theory

79. Bootstrap Confidence Intervals

80. Empirical Likelihood

81. Kernel Density Estimation

82. Probability Inequalities (e.g., Jensen’s Inequality)

83. Asymptotic Theory

84. Sequential Analysis

85. Influence Functions

86. U-statistics

87. Sufficient Statistics

88. Exponential Families

89. Decision Theory

90. Game Theory

91. Utility Theory

92. Meta-Analysis

93. Statistical Computing

94. Missing Data Techniques

95. Spatial Statistics

96. Functional Data Analysis

97. Multilevel Models

98. Time-Varying Coefficient Models

99. Causal Inference

100. Propensity Score Matching

Notes on Entrepreneurship theories as per ChatGPT – Part 1

Entrepreneurship is a multifaceted field that intersects with economics, sociology, psychology, and strategic management. Over the years, researchers have developed a wide range of theories to explain various aspects of entrepreneurship, such as opportunity recognition, innovation, firm creation, strategic behavior, and more. Below is an exhaustive list of entrepreneurship theories along with brief descriptions of each:

### 1. Opportunity-Based Theories

1. Opportunity Recognition Theory:

Key Concept: Entrepreneurs identify and act on profitable opportunities that others do not see.

Major Contributor: Israel Kirzner (1973).

Core Idea: The theory distinguishes between alertness (recognizing opportunities) and discovery.

2. Entrepreneurial Opportunity Theory:

Key Concept: The process of identifying, evaluating, and exploiting business opportunities.

Major Contributors: Shane & Venkataraman (2000).

Core Idea: Entrepreneurship is defined as the nexus of two phenomena: the presence of lucrative opportunities and the presence of enterprising individuals.

3. Resource-Based Theory (RBT):

Key Concept: Entrepreneurial success is determined by the resources controlled by the entrepreneur.

Major Contributor: Jay Barney (1991).

Core Idea: Resources must be valuable, rare, inimitable, and non-substitutable (VRIN) to provide a sustainable competitive advantage.

4. Discovery Theory vs. Creation Theory:

Key Concept: Entrepreneurs either discover pre-existing opportunities (discovery theory) or create new opportunities through innovation and interactions (creation theory).

Major Contributors: Sarasvathy (2001) and Alvarez & Barney (2007).

Core Idea: Discovery theory assumes an objective reality, whereas creation theory views opportunities as emerging from human action.

### 2. Economic Theories of Entrepreneurship

1. Schumpeterian Theory (Creative Destruction):

Key Concept: Entrepreneurs are innovators who create economic growth through creative destruction, disrupting existing markets.

Major Contributor: Joseph Schumpeter (1934).

Core Idea: Innovation is the source of economic development, and entrepreneurs introduce new products, processes, and business models.

2. Knightian Uncertainty Theory:

Key Concept: Entrepreneurs make decisions under uncertainty and bear the associated risks.

Major Contributor: Frank Knight (1921).

Core Idea: Differentiates between risk (measurable probability) and uncertainty (unknown probability), with the entrepreneur rewarded for dealing with uncertainty.

3. Cantillon’s Theory of Entrepreneurship:

Key Concept: Entrepreneurs are risk-takers who buy at certain prices and sell at uncertain prices.

Major Contributor: Richard Cantillon (18th century).

Core Idea: The entrepreneur acts as a middleman and absorbs market risk.

4. Marshallian Demand-Supply Theory:

Key Concept: Entrepreneurship arises from economic forces where demand and supply create incentives for entrepreneurial activity.

Major Contributor: Alfred Marshall (1890).

Core Idea: Entrepreneurs allocate resources efficiently to balance demand and supply in markets.

5. Baumol’s Theory of Productive, Unproductive, and Destructive Entrepreneurship:

Key Concept: Entrepreneurs engage in activities that can be productive (value-creating), unproductive (rent-seeking), or destructive (illegal).

Major Contributor: William Baumol (1990).

Core Idea: The allocation of entrepreneurship depends on the institutional environment and rewards structure.

### 3. Behavioral Theories of Entrepreneurship

1. Psychological Traits Theory:

Key Concept: Focuses on the personality traits of entrepreneurs (e.g., risk-taking, need for achievement).

Major Contributor: David McClelland (1961).

Core Idea: High “need for achievement” drives individuals to become entrepreneurs.

2. Locus of Control Theory:

Key Concept: Entrepreneurs believe that they can control their own destiny (internal locus of control) rather than being influenced by external forces.

Major Contributor: Julian Rotter (1966).

Core Idea: Entrepreneurs with an internal locus of control are more likely to take initiative and innovate.

3. Self-Efficacy Theory:

Key Concept: A person’s belief in their own ability to execute tasks and achieve goals.

Major Contributor: Albert Bandura (1977).

Core Idea: High entrepreneurial self-efficacy correlates with greater likelihood of pursuing entrepreneurial opportunities.

### 4. Sociological Theories of Entrepreneurship

1. Network Theory:

Key Concept: Entrepreneurial success is influenced by social networks and the quality of personal and professional relationships.

Major Contributor: Mark Granovetter (1973).

Core Idea: Weak ties are crucial for accessing diverse information and resources.

2. Social Capital Theory:

Key Concept: Social capital, such as trust, norms, and networks, is crucial for entrepreneurial success.

Major Contributor: Pierre Bourdieu (1986).

Core Idea: Entrepreneurs leverage social capital to access resources and opportunities.

3. Ecological Theory of Entrepreneurship:

Key Concept: Entrepreneurship is influenced by social, cultural, and economic environments.

Major Contributor: Howard Aldrich (1979).

Core Idea: Explains entrepreneurship as an adaptive response to environmental conditions.

### 5. Strategic and Management Theories of Entrepreneurship

1. Strategic Entrepreneurship Theory:

Key Concept: Combines opportunity-seeking and advantage-seeking behaviors to create wealth.

Major Contributors: Hitt, Ireland, Camp, & Sexton (2001).

Core Idea: Entrepreneurs balance exploration and exploitation to achieve long-term success.

2. Dynamic Capabilities Theory:

Key Concept: Focuses on how firms adapt to changing environments through dynamic capabilities.

Major Contributor: Teece, Pisano, & Shuen (1997).

Core Idea: Entrepreneurs build dynamic capabilities to integrate, build, and reconfigure resources for competitive advantage.

3. Effectuation Theory:

Key Concept: Entrepreneurs start with available resources and focus on controlling the future rather than predicting it.

Major Contributor: Saras Sarasvathy (2001).

Core Idea: Entrepreneurs focus on what they can control rather than trying to predict uncertain outcomes.

4. Corporate Entrepreneurship/Intrapreneurship Theory:

Key Concept: Focuses on entrepreneurial behavior within large organizations.

Major Contributor: Gifford Pinchot (1985).

Core Idea: Intrapreneurs drive innovation and renewal within established firms.

### 6. Innovation Theories in Entrepreneurship

1. Disruptive Innovation Theory:

Key Concept: Disruptive innovations displace established technologies or products.

Major Contributor: Clayton Christensen (1997).

Core Idea: Focuses on how simpler, cheaper innovations disrupt markets.

2. Innovative Entrepreneurship Theory:

Key Concept: Emphasizes the role of innovation in entrepreneurship.

Major Contributor: Schumpeter (1934).

Core Idea: Entrepreneurs introduce new products, processes, or services that drive economic change.

### 7. Entrepreneurial Ecosystem Theories

1. Entrepreneurial Ecosystem Theory:

Key Concept: Entrepreneurship is influenced by a supportive ecosystem that includes actors, institutions, and policies.

Major Contributors: Isenberg (2010) and Stam (2015).

Core Idea: A robust ecosystem promotes entrepreneurial success through access to capital, talent, and networks.

2. Institutional Theory:

Key Concept: Institutions shape the entrepreneurial environment through formal and informal rules.

Major Contributor: Scott (1995).

Core Idea: Institutional support and barriers significantly influence entrepreneurial behavior.

### 8. Behavioral Decision-Making Theories

1. Prospect Theory:

Key Concept: Entrepreneurs make decisions based on potential gains and losses, which are weighed differently.

Major Contributors: Kahneman & Tversky (1979).

Core Idea: Entrepreneurs are more likely to take risks when faced with potential losses.

2. Bricolage Theory:

Key Concept: Entrepreneurs create solutions using limited resources at hand.

Major Contributor: Baker & Nelson (2005).

Core Idea: Entrepreneurs use “making do” strategies to address resource constraints creatively.

This comprehensive list covers the major entrepreneurship theories, providing you with a strong theoretical foundation for understanding the diverse aspects of entrepreneurial behavior, strategy.

Resources on Advanced Statistics & Probability as per ChatGPT

Here’s a list of some excellent resources across various formats—books, YouTube channels, and blogs—that cover advanced statistics and probability theories:

Books:

1. “Introduction to Probability” by Dimitri P. Bertsekas and John N. Tsitsiklis

– A comprehensive introduction to probability, available for free in PDF form on MIT’s OpenCourseWare.

– [Link to PDF](https://athenasc.com/probbook.html)

2. “Think Stats” by Allen B. Downey

– This book focuses on applying statistics to real-world data, with practical examples using Python.

– [Link to PDF](https://greenteapress.com/wp/think-stats-2e/)

3. “The Elements of Statistical Learning” by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

– A highly regarded book on statistical learning, offering a deep dive into many advanced topics.

– [Link to PDF](https://web.stanford.edu/~hastie/ElemStatLearn/)

4. “All of Statistics: A Concise Course in Statistical Inference” by Larry Wasserman

– A great resource that covers both basic and advanced topics in statistics.

– [Link to PDF](https://www.stat.cmu.edu/~larry/all-of-statistics/)

5. “Introduction to Statistical Thought” by Michael Lavine

– This book covers the foundations of statistical inference and is available for free online.

– [Link to PDF](https://www.math.umass.edu/~lavine/Book/book.html)

YouTube Channels:

1. MIT OpenCourseWare – Probability and Statistics

– Features lectures from MIT’s undergraduate and graduate courses, including advanced topics in probability and statistics.

– [MIT OCW YouTube Channel](https://www.youtube.com/user/MIT)

2. Khan Academy

– Although mostly known for basic statistics, Khan Academy also offers more advanced courses in probability and statistical inference.

– [Khan Academy – Probability & Statistics](https://www.youtube.com/user/khanacademy)

3. StatQuest with Josh Starmer

– Excellent channel that breaks down complex statistical concepts into easily understandable segments, including advanced topics.

– [StatQuest YouTube Channel](https://www.youtube.com/user/joshstarmer)

4. Brilliant.org

– While Brilliant offers paid content, their YouTube channel provides free videos covering advanced mathematical concepts, including probability and statistics.

– [Brilliant.org YouTube Channel](https://www.youtube.com/c/Brilliantorg)

5. Harvard University – STAT110 (Probability)

– Lectures from Harvard’s popular STAT110 course, taught by Professor Joe Blitzstein, covering probability theory in depth.

– [Harvard University – STAT110 YouTube Channel](https://www.youtube.com/playlist?list=PL2SOU6wwxB0v1kQTpqpuuGIjJRWJaFfeH)

Blogs and Online Courses:

1. Cross Validated (Stack Exchange)

– A Q&A site specifically for statistics, probability, and data science. It’s a great place to see advanced problems discussed in depth.

– [Cross Validated](https://stats.stackexchange.com/)

2. OpenIntro

– Offers free textbooks, labs, and resources on statistics, including advanced topics.

– [OpenIntro](https://www.openintro.org/)

3. Towards Data Science (Medium)

– A popular blog on Medium with numerous articles on advanced statistics, probability, and their applications in data science.

– [Towards Data Science](https://towardsdatascience.com/)

4. DataCamp Community

– While DataCamp offers paid courses, their blog has free articles and tutorials on advanced statistical methods.

– [DataCamp Community](https://www.datacamp.com/community)

5. Probability and Statistics EBook

– An online resource that provides detailed explanations of advanced topics in probability and statistics.

– [Probability and Statistics EBook](http://www.probabilitycourse.com/)

MOOCs and Online Lectures:

1. Coursera – Statistical Learning by Stanford University

– A free course that covers statistical learning, based on the book “The Elements of Statistical Learning.”

– [Coursera – Statistical Learning](https://www.coursera.org/learn/statistical-learning)

2. edX – Probability: The Science of Uncertainty and Data by MIT

– A free course that dives deep into probability theory and applications.

– [edX – MIT Probability Course](https://www.edx.org/course/probability-the-science-of-uncertainty-and-data)

3. Harvard Online Learning – Data Science: Probability

– Part of Harvard’s Data Science Professional Certificate, this course is available for free auditing.

– [Harvard – Data Science: Probability](https://online-learning.harvard.edu/course/data-science-probability)

These resources cover a broad range of advanced topics in probability and statistics, and they offer various levels of depth, from introductory overviews to rigorous academic treatments.

How to tame the SEO beast with Liferay? Part 1.

Here are some keywords and concepts to explore:

1. Performance tuning – https://www.linkedin.com/pulse/performance-tuning-liferay-part-3-neil-harwani-nsoof/

2. Performance options for pages & search in built in Liferay – -> https://learn.liferay.com/w/dxp/using-search/search-pages-and-widgets/search-insights

–> https://learn.liferay.com/w/dxp/content-authoring-and-management/page-performance-and-accessibility/analyze-seo-and-accessibility-on-pages

–> https://learn.liferay.com/w/dxp/content-authoring-and-management/page-performance-and-accessibility/about-the-page-audit-tool

3. SEO features in Liferay

–> https://learn.liferay.com/w/dxp/site-building/optimizing-sites

–> https://learn.liferay.com/w/dxp/site-building/displaying-content/using-display-page-templates/configuring-seo-and-open-graph

4. Set up your own monitoring (simple JMeter is a good start) and focus on page load times in conjunction with other things rather than only scores. Use various tools – an example: –> https://bloggerspassion.com/website-performance-speed-test-tools/

–> https://developer.chrome.com/docs/lighthouse/overview/

5. Liferay headless – https://www.liferay.com/solutions/headless-apis

6. Lazy loading, innovative solutions like lighter pages with type ahead and so on

7. Understand various tools for website performance, formula for page speed insights & SEO but focus on your metrics like page load speed, image quality. Don’t blindly pick a tool and follow it. However, suggestions / recommendations / insights of various tools should be explored and worked upon as needed

8. Note: Mobile score is throttled down by page speed insights to slow 3G or so

Other hints:

1. Robots.txt

2. Core web vitals

3. Sitemap

4. https

5. Broken links, friendly URLs, mobile friendliness

6. Content quality

7. Tags

8. Images

9. Caching

Liferay with right configuration, customization & tuning is capable of some magical things 🙂

Keywords from my FPM journey – Part 2

Some of the keywords from my FPM journey – Part 2:

Learnings from eMasters Data Science for Decision Making – IIT Gandhinagar – Part 1

Below are the learnings from eMasters Data Science for Decision Making – IIT Gandhinagar – Part 1. This needs to be an article in itself. You can find more details here about eDSDM here: e-Masters | IITGNX

  • There is Mathematics for your AIML problem in feature engineering, preprocessing, evaluation metrics, models, errors, etc. and so on or you can build some relationship between Mathematics and your problem either by breaking the problem into pieces or transforming data and so on.
  • Human intuition is still invaluable like in situations of imbalanced datasets, regression and more.
  • Visualization and EDA almost always help for your problems. For higher dimension problems you can do PCA, T-SNE, shadowing on lower dimensions, etc. more approaches to bring it to lower dimensions with or without transformation like basis transformation. In this situation, you should also feed data after this transformation for testing / using the model.
  • There are problems beyond model selection and preprocessing, feature engineering like data quality, overfitting, evaluation, errors, hyper parameter tuning and generalization which we need to think about.
  • Situations like classification of medical diagnosis issues have very high value repercussions for wrong classification whereas some models with overfitting won’t generalize and cause problems like in normal situations with regression.
  • Probability concepts like equally likely for randomness, distributions, fairness, probability tree, joint probability, permutations and combinations, etc. have lot of value and are worth learning.
  • There is no learning like learning by experience, concepts and application.
  • Your assumptions maybe wrong as well so it’s good to verify.
  • Accuracy alone may not be a good measure, please add recall & precision as well in your analysis especially in imbalanced datasets.
  • Domain knowledge matters. Don’t ignore this.
  • Email me: Neil@HarwaniSytems.in
  • Website: www.HarwaniSystems.in
  • Blog: www.TechAndTrain.com/blog
  • LinkedIn: Neil Harwani | LinkedIn

Returning to school / academics from industry

Below is an article summarizing some points that I have experienced when transitioning back to academics / school from industry. I have done academics (learning) part time which is study part time since 2011 onwards to achieve various goals in academics along with work in industry. Here is the summary of what it takes and some tips to excel:

  • You need to accept that getting a degree or a good certification takes time and effort.
  • You need dedicated time over the nights or mornings on weekdays and especially half of the weekends sacrificing time with family and friends.
  • Calendaring or scheduling time using calendars is your best friend.
  • Finding out the best resources from the internet and Wikipedia or similar portals is very helpful.
  • You can do anything but not everything. This is actually true. You need to drop / deprioritize what you cannot do due to lack of time.
  • Your industry & family environment needs to be supportive of your goals and efforts, only then you will be able to manage both industry and academics.
  • Writing / journaling also definitely helps, something like a blog as well can help.
  • Pick growth mindset, have an open mind and learn continuously. This needs to become a habit.
  • Use your industry knowledge to have discussions with batchmates/peers and professors. This helps to learn quickly and have engaging discussions.
  • Integrate industry practices into your academic work.
  • Network with industry, professors, batchmates to learn more effectively and stay on top of trends.
  • Take advantage of academic assets like libraries and online databases for research.
  • Use online platforms for research, collaboration, and project management.
  • Understand mixing theory and practice. It helps.
  • Aim to bridge the gap between academia and industry in your work.
  • Plan your savings and finances to manage academic expenses properly.
  • Maintain a healthy balance between work, life and study time.
  • Manage stress through exercise, proper nutrition, and mindfulness practices.
  • Set clear, achievable, planned goals and not ad-hoc random expectations. Adjust as necessary.
  • Be flexible to new academic environments.
  • Try for innovation using your industry and academic knowledge.
  • Returning to good academic degrees / diplomas / certifications / workshops will most likely improve your knowledge and skills significantly.
  • Email me: Neil@HarwaniSytems.in
  • Website: www.HarwaniSystems.in
  • Blog: www.TechAndTrain.com/blog
  • LinkedIn: Neil Harwani | LinkedIn

Mastering Data Series – Enterprise Content Management – Part 1

Here are the main points for Enterprise Content Management in Mastering Data series – Part 1 from architecture & technology perspective:

  1. Hierarchical object model to store content & documents with it’s attributes (extended and / or default) in the core engine
  2. Publishing module for content release to audience
  3. Portal for accessing these content
  4. Use cases: Workflow over content, Content publishing, Digital Asset Management, Digital Rights Management, Scanning Solutions, Search, Content Sharing, Content Automation, Knowledge Management, Knowledge Discovery, Insights/Analytics over documents and so on
  5. Scanning solutions: OCR, ICR, OMR, HCR, Barcode, Watermarking and so on
  6. Compliance / Retention / Governance
  7. Lifecycle of ECM: Capture, Manage, Store, Preserve & Deliver
  8. Integrations: SoA, Pub-Sub, ESB, Asynchronous integration and so on
  9. Transformation services between various formats
  10. Distribution over email, paper, internet, etc.
  11. What all comes in the deployment typically: Search, File Store, Core Engine, Workflow Engine, Integration module, Portal, Scanning Solution, Publishing Module, Social Media, AI/ML Insights, Data Science over documents module, etc.
Enterprise content management – Wikipedia

Pointers to work with product support at Liferay – Part 1

Below pointers may help when working with Liferay support to cut response times:

1. How is the vanilla product behaving for your problem area?

2. What are the relevant database tables, source code and configurations saying? Discuss about this early on the ticket

3. Attach any video of the problem that you might have?

4. Have you checked whether it’s truly a Liferay issue or an issue with the environment and ecosystem around it like web server, CDN, WAF and so on. These details should also go on the ticket.

5. Are your logs verbose enough? If not, did you enable detailed logging via control panel.

6. Have you scanned logs of Liferay, Elastic Search, Database, Web server, etc. What errors are being thrown?

7. Did you check thread & heap dump, CPU, Memory, etc.? It will help in certain related situations.

8. Do you have Glowroot enabled? What is it saying about errors, slow traces, JVM, etc.?

9. Are all systems online like File Store, Elastic Search, Database, Web Server, WAF, CDN, etc.

10. Ultimately problems will be either in the product as a feature / bug, database, configurations or the ecosystem or similar logical areas. Isolating these early definitely helps.

11. Have you checked Liferay Learn, Liferay Blogs, Liferay Forums and Liferay Help Center plus Customer portal for similar problems / articles?

12. Are you at the latest minor patch version for your major release?

13. Are your customizations confirming to Liferay & Java official processes?

14. Are you compliant to Liferay support matrix?

Having a wholistic debugging view like above can help support cut through lot of unnecessary iterations.

Keywords from Day 5 of Online Workshop on Development and Deployment of AIoT based solution for Industrial Applications by NSUT, Delhi

Further to Day 1 & 2 / Day 3 & 4 keywords posted in the past as per the links here, below are the keywords for Day 5: Keywords from Day 1 & 2 of Online Workshop on Development and Deployment of AIoT based solution for Industrial Applications by NSUT, Delhi | LinkedIn, Keywords from Day 3 & 4 of Online Workshop on Development and Deployment of AIoT based solution for Industrial Applications by NSUT, Delhi | LinkedIn:

Session 1:

Speakers:

Dr. Sudeepta Mishra | Department of Computer Science & Engineering (iitrpr.ac.in)

Sudeepta Mishra | LinkedIn

Raushan Kumar Singh | LinkedIn

Keywords:

TinyML 

MicroControllers and it’s advantages in IoT / AIoT

Hardware Software Co-design 

Hacking / confusing signals to IoT / Electromagnetic Pulses  

Internet of Battlefield Things (IoBT) 

Sensor hacking 

Use case for border fencing security 

Hand gesture recognition practical 

www.EdgeImpulse.com – Build datasets, train models, and optimize libraries to run directly on device; from the smallest microcontrollers to gateways with the latest neural accelerators (and anything in between).

Programming on TinyML

Compressing models 

Sin wave generation and detection on TinyML 

On-Device Training with TensorFlow Lite

TensorFlow Lite for Microcontrollers 

TensorFlow Lite 

Hex file for microcontroller – header file 

EloquentTinyML library 

Arduino

Raspberry Pi

Wokwi – Online ESP32, STM32, Arduino Simulator 

Training your model and putting it directly in firmware

Quantization in TinyML

Session 2:

Speakers:

Prof. SRN Reddy – DIC-IGDTUW 

CSE (igdtuw.ac.in) 

Keywords:

Practical approach to Smart IoT devices 

ARM processor 

Peripherals

Embedded and IoT devices differences

Framework of Smart IoT – Smartphone

Jetson Nano NVidia

Hardware components for smart IoT / SmartPhone

My_OS

Commercialization of Smart IoT

Smart Healthcare product

Aquaculture product

Challenges with Aquaculture product – Scaling, reactions with water of sensors & dirty solar panels

Environmental monitoring system

Various MTech / PhD thesis discussion & demos for prototypes & products

ETI Labs Pvt. Ltd.

Various boards for Arduino, 8051 and products / kits on top of it

Instrumentation for above

Industry 4.0 testbed

Button marking & stitching machine – autonomous system

AIoT in agriculture – Temperature, Humidity, CO2 & AQI monitoring

Drone with multi-spectral imaging for farming for NPK (Nitrogen, phosphorus, and potassium) monitoring with AI

Production monitoring with AIoT

Ideas on Innovation around Technology. We Thrive On Ideas. We are Learner Centered, Open Source & Digital Focused.