Ashutosh pycon
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The document discusses the use of natural language processing (NLP) for solving logical puzzles, highlighting various methods and models including co-occurrence matrices and word embeddings like Word2Vec. It emphasizes the importance of representing words programmatically and the challenges of scalability and dimensionality in NLP. The presentation also covers the skip-gram model and demonstrates how word similarity and semantic relationships can be analyzed in low-dimensional vector spaces.
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![How do we represent a word?
Index in vocab..
30th word [0,0,.1,0,0,0,0]
30
45th word [0,0,.0,0,0,0,1,0]
45
How would a processor know that good at index 30 is
synonym for nice at index 45
Pycon-India-2015](https://image.slidesharecdn.com/ashutosh-pycon-151005185853-lva1-app6892/85/Ashutosh-pycon-9-320.jpg)
![Dimension of similarity
Test for linear relationships, examined by Mikolov et al.
a:b :: c: ?
man: woman :: king : ?
man
king
woman
queen
1
0.75
0.5
0.25
0
0.25 0.5 0.75 1
+ king [0.5, 0.2]
- man [0.25, 0.5]
+ woman [0.6, 1 ]
-
queen [0.85, 0.7]
Pycon-India-2015](https://image.slidesharecdn.com/ashutosh-pycon-151005185853-lva1-app6892/85/Ashutosh-pycon-28-320.jpg)
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This OrionX's 14th semi-annual report on the state of the cryptocurrency mining market. The report focuses on Proof-of-Work cryptocurrencies since those use substantial supercomputer power to mint new coins and encode transactions on their blockchains. Only two make the cut this time, Bitcoin with $18 billion of annual economic value produced and Dogecoin with $1 billion. Bitcoin has now reached the Zettascale with typical hash rates of 0.9 Zettahashes per second. Bitcoin is powered by the world's largest decentralized supercomputer in a continuous winner take all lottery incentive network.The State of Web3 Industry- Industry Report
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Web3 is poised for mainstream integration by 2030, with decentralized applications potentially reaching billions of users through improved scalability, user-friendly wallets, and regulatory clarity. Many forecasts project trillions of dollars in tokenized assets by 2030 , integration of AI, IoT, and Web3 (e.g. autonomous agents and decentralized physical infrastructure), and the possible emergence of global interoperability standards. Key challenges going forward include ensuring security at scale, preserving decentralization principles under regulatory oversight, and demonstrating tangible consumer value to sustain adoption beyond speculative cycles.vertical-cnc-processing-centers-drillteq-v-200-en.pdf
vertical-cnc-processing-centers-drillteq-v-200-en.pdfAmirStern2
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廬 CNC 廡 廬 廨 廬廨 廡 廨廬 廨 廢廨 廨. 廡 廖廣 廨 廢廨 -x 廢廣廬 廢廨 廡, 廬廚廖 廣" 廢廬 廬, 廩 廢廨 廢廣 setup (廬廬) 廩 廩 廡. FME for Good: Integrating Multiple Data Sources with APIs to Support Local Ch...
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Have-a-skate-with-Bob (HASB-KC) is a local charity that holds two Hockey Tournaments every year to raise money in the fight against Pancreatic Cancer. The FME Form software is used to integrate and exchange data via API, between Google Forms, Google Sheets, Stripe payments, SmartWaiver, and the GoDaddy email marketing tools to build a grass-roots Customer Relationship Management (CRM) system for the charity. The CRM is used to communicate effectively and readily with the participants of the hockey events and most importantly the local area sponsors of the event. Communication consists of a BLOG used to inform participants of event details including, the ever-important team rosters. Funds raised by these events are used to support families in the local area to fight cancer and support PanCan research efforts to find a cure against this insidious disease. FME Form removes the tedium and error-prone manual ETL processes against these systems into 1 or 2 workbenches that put the data needed at the fingertips of the event organizers daily freeing them to work on outreach and marketing of the events in the community.From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...
From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...Edge AI and Vision Alliance
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/from-enterprise-to-makers-driving-vision-ai-innovation-at-the-extreme-edge-a-presentation-from-sony-semiconductor-solutions/
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FIDO Seminar: Perspectives on Passkeys & Consumer AdoptionFME for Distribution & Transmission Integrity Management Program (DIMP & TIMP)
FME for Distribution & Transmission Integrity Management Program (DIMP & TIMP)Safe Software
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Peoples Gas in Chicago, IL has changed to a new Distribution & Transmission Integrity Management Program (DIMP & TIMP) software provider in recent years. In order to successfully deploy the new software we have created a series of ETL processes using FME Form to transform our gas facility data to meet the required DIMP & TIMP data specifications. This presentation will provide an overview of how we used FME to transform data from ESRIs Utility Network and several other internal and external sources to meet the strict data specifications for the DIMP and TIMP software solutions.Down the Rabbit Hole Solving 5 Training Roadblocks
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Feeling stuck in the Matrix of your training technologies? Youre not alone. Managing your training catalog, wrangling LMSs and delivering content across different tools and audiences can feel like dodging digital bullets. At some point, you hit a fork in the road: Keep patching things up as issues pop up or follow the rabbit hole to the root of the problems.
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FIDO Seminar: Authentication for a Billion Consumers - AmazonTrustArc Webinar - 2025 Global Privacy Survey
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How does your privacy program compare to your peers? What challenges are privacy teams tackling and prioritizing in 2025?
In the sixth annual Global Privacy Benchmarks Survey, we asked global privacy professionals and business executives to share their perspectives on privacy inside and outside their organizations. The annual report provides a 360-degree view of various industries' priorities, attitudes, and trends. See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar features an expert panel discussion and data-driven insights to help you navigate the shifting privacy landscape. Whether you are a privacy officer, legal professional, compliance specialist, or security expert, this session will provide actionable takeaways to strengthen your privacy strategy.
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Predictions for the future of privacy in 2025 and beyondCan We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
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Can We Use Rust to Develop Extensions for PostgreSQL?
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NTT DATA Japan CorporationFIDO Seminar: Evolving Landscape of Post-Quantum Cryptography.pptx
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FIDO Seminar: Evolving Landscape of Post-Quantum CryptographyAI VIDEO MAGAZINE - June 2025 - r/aivideo
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AI VIDEO MAGAZINE - r/aivideo community newsletter Exclusive Tutorials: How to make an AI VIDEO from scratch, PLUS: How to make AI MUSIC, Hottest ai videos of 2025, Exclusive Interviews, New Tools, Previews, and MORE - JUNE 2025 ISSUE -From Enterprise to Makers: Driving Vision AI Innovation at the Extreme Edge,...
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Can We Use Rust to Develop Extensions for PostgreSQL? (POSETTE: An Event for ...
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Ashutosh pycon
- 1. Solving Logical Puzzles with Natural Language Processing Pycon-India 2015 by Ashutosh Trivedi Founder, bracketPy www.bracketpy.com Pycon-India-2015
- 2. The NLP Story I know Natural Language I know Processing Pycon-India-2015Pycon-India-2015
- 3. To understand natural language programatically But how ? Part of Speech Vocabulary n-gram Word style Noun detection The NLP Story.. Pycon-India-2015
- 4. The NLP Story.. Hey, I am a sentence.. can you process me ? current word previous word next word current word n-gram POS tag surrounding POS tag sequence word shape (all Caps?) surrounding word shape presence of word in left/right window Information Pycon-India-2015
- 5. What to Do With the Information? Lets learn a model F1 ) current word F2 ) previous word F3) next word F4) current word n-gram F5) POS tag F6) surrounding POS tag sequence F7) word shape (all Caps?) F8) surrounding word shape F9) presence of word in left/right window Features - Pycon-India-2015
- 6. What to Do With the Information? W1F1 + W2F2 + .+ WnFnsentence 1 positive W1F1 + W2F2 + .+ WnFnsentence 2 negative W1F1 + W2F2 + .+ WnFnsentence 3 positive W1F1 + W2F2 + .+ WnFnsentence x negative . . . Pycon-India-2015
- 7. Is that it ? Data features Through other processes Machine Learning learn weights (optimise weights) Is it scalable ? Pycon-India-2015
- 8. So what is wrong ? We are talking about AI Pycon-India-2015
- 9. How do we represent a word? Index in vocab.. 30th word [0,0,.1,0,0,0,0] 30 45th word [0,0,.0,0,0,0,1,0] 45 How would a processor know that good at index 30 is synonym for nice at index 45 Pycon-India-2015
- 12. So how do we remember words.. ? Our human hooks associated word person context taste smell time visual feelings Pycon-India-2015
- 13. how to represent words programmatically ? You, as an individual is average of 5 people you spend time with everyday. You shall know a word by the company it keeps -J.R.Firth 1957 Pycon-India-2015
- 14. How to make neighbours represent the word ? One of the most successful ideas of statistical NLP Co-occurrence matrix Capture both syntactical and semantical information Pycon-India-2015
- 15. Window based co-occurrence matrix Example corpus: 1) I like deep learning. 2) I like NLP. 3) I enjoy 鍖ying. Pycon-India-2015
- 16. Problem with simple co-occurrence matrix Increase size with vocabulary very high dimensional Requires a lot of storage subsequent classi鍖cation model has sparsity issues Pycon-India-2015
- 17. Reduce the dimension store information in few dimension - 25 to 1000 dense vectors less space Pycon-India-2015
- 18. Singular Value Decomposition (SVD) Pycon-India-2015
- 20. Problem with SVD Computational cost scales quadratically for N x M matrix Bad for million of words or document Hard to incorporate new words Pycon-India-2015
- 21. Word2Vec Directly learn low dimensional vectors Instead of capturing co-occurrence counts directly, Predict surrounding words of every word Glove: Global Vectors for Word Representation by Pennington et al. (2014) Faster and can easily incorporate a new sentence/ document or add a word to the vocabulary Pycon-India-2015
- 22. Word2Vec The Skip-gram Model objective of the Skip-gram model is to 鍖nd word representations by the surrounding words in a sentence or a document. W1, W2, W3, . . . , Wt sentence/document Maximise the log probability of any context word given the current centre word. Pycon-India-2015
- 23. Word2Vec The Skip-gram Model For a window of T=3 w1,w2,w3,Wc,w4,w5,w6 Maximise the probability of (w1,w2,w3, w4,w5,w6) for given word Wc Pycon-India-2015
- 24. Word2Vec Unsupervised method We are just optimising the probability of words with respect to its neighbours creating a low dimensional space (probabilistic) Pycon-India-2015
- 25. Lower Dimensions Dimension of Similarity Dimension of sentiment ? Dimension of POS ? Dimension of all word having 5 vowels It can be anything . word embeddings Pycon-India-2015
- 26. Dimension of similarity Analogies testing dimensions of similarity can be solved quite well just by doing vector subtraction in the embedding space Syntactically. X X X X X X Syntactical - Singular, Plural Pycon-India-2015
- 27. Dimension of similarity Semantical Similarly for verb and adjective morphological forms Semantically (Semeval 2012 task 2) Xh X＄ Xh X＄ X X X Xゐn Pycon-India-2015
- 28. Dimension of similarity Test for linear relationships, examined by Mikolov et al. a:b :: c: ? man: woman :: king : ? man king woman queen 1 0.75 0.5 0.25 0 0.25 0.5 0.75 1 + king [0.5, 0.2] - man [0.25, 0.5] + woman [0.6, 1 ] - queen [0.85, 0.7] Pycon-India-2015
- 30. Pycon-India-2015
- 31. Pycon India , Ashutosh Trivedi Superlatives Pycon-India-2015
- 33. Reference Deep Learning for Natural Language Processing CS224d Stanfor.edu (http://cs224d.stanford.edu/) Mikolov, G. C. T., K. Chen, and J. Dean. "word2vec (2013). Pennington, Je鍖rey, Richard Socher, and Christopher D. Manning. "Glove: Global vectors for word representation." Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014) 12 (2014): 1532-1543. gensim : Topic Modeling for Humans (https://radimrehurek.com/gensim/) Pycon-India-2015