Yogendra Kashyap is seeking a career opportunity where he can utilize his 1 year, 10 month experience as a Production Engineer at Praxair Ind. Pvt. Ltd. He is looking to contribute to organizational growth and achieve personal development. He has a B.Tech in Chemical Engineering and skills in process analysis, distillation operations, and knowledge of automation systems and air separation processes. He also has training in process safety, instrumentation, and is a hazardous permit issuer.
Network analysis is the study of complex systems of interconnected parts. This document appears to be about a course on network analysis using the Python programming language. The course number is 5,337,584 and it is taught by Siddharth Chaudhary on the topic of Network Analysis in Python (Part 1).
Supervised machine learning involves using labeled examples to train models that can make predictions on new data. This document appears to be a course on supervised learning using the scikit-learn library in Python. The course will likely cover the basics of supervised learning algorithms like classification and regression, and how to apply them to problems using scikit-learn.
1) The document discusses predicting soil fertility using machine learning techniques such as decision trees, artificial neural networks, support vector machines, and k-nearest neighbors.
2) It analyzes soil data from Haryana, India to determine the most important properties for defining soil fertility and properties that are highly correlated. Conductivity, water holding capacity, and potassium were found to be most important based on a decision tree analysis.
3) Support vector machines using a radial basis kernel performed best with 80% accuracy compared to 63% for decision trees, 55% for artificial neural networks, and 70% for k-nearest neighbors.
This document describes Siddharth Chaudhary's MSc research project on forecasting solar electricity generation using time series models. The research aims to 1) forecast solar generation in Delhi and Jodhpur, India, 2) evaluate the performance of forecasting models, and 3) compare potential solar generation between the two cities. Four time series models - TBATS, ARIMA, simple exponential smoothing, and Holt's method - are applied to solar radiation data from each city and their accuracy is assessed.
Project on nypd accident analysis using hadoop environmentSiddharth Chaudhary
For this project NYC motor-vehicle-collisions dataset is processed in Hadoop ecosystem using map reduce, Pig script and Hive query for analysis and visualization.
Made a Visualisation project Report by using R packages(ggplot) on the Global terrorism dataset(1970-2015) using different interactive graphs, different combination of colours had been used so that colour blind people can also visualise the patterns.
Implemented Data warehouse on “Retail Stores of five states of USA” by using 3 different data sources including structured and unstructured using SSIS, SSAS and Power BI.
Implemented salesforce and CRM application, in this application employees and customers are sharing same platform which increases productivity and saves time for customers.
Developed a home security system to protect occupants from fire and intrusion. The device sends SMS to the emergency number provided to it via GSM (Global System for Mobile communications) module. Led my group and implemented the device successfully.
Generated a Statistical Report on air quality of Ireland (correlation and regression) using SPSS and religious belief of different age group people in their respective religion(Two way ANOVA) using R.
AI Competitor Analysis: How to Monitor and Outperform Your CompetitorsContify
AI competitor analysis helps businesses watch and understand what their competitors are doing. Using smart competitor intelligence tools, you can track their moves, learn from their strategies, and find ways to do better. Stay smart, act fast, and grow your business with the power of AI insights.
For more information please visit here https://www.contify.com/
1) The document discusses predicting soil fertility using machine learning techniques such as decision trees, artificial neural networks, support vector machines, and k-nearest neighbors.
2) It analyzes soil data from Haryana, India to determine the most important properties for defining soil fertility and properties that are highly correlated. Conductivity, water holding capacity, and potassium were found to be most important based on a decision tree analysis.
3) Support vector machines using a radial basis kernel performed best with 80% accuracy compared to 63% for decision trees, 55% for artificial neural networks, and 70% for k-nearest neighbors.
This document describes Siddharth Chaudhary's MSc research project on forecasting solar electricity generation using time series models. The research aims to 1) forecast solar generation in Delhi and Jodhpur, India, 2) evaluate the performance of forecasting models, and 3) compare potential solar generation between the two cities. Four time series models - TBATS, ARIMA, simple exponential smoothing, and Holt's method - are applied to solar radiation data from each city and their accuracy is assessed.
Project on nypd accident analysis using hadoop environmentSiddharth Chaudhary
For this project NYC motor-vehicle-collisions dataset is processed in Hadoop ecosystem using map reduce, Pig script and Hive query for analysis and visualization.
Made a Visualisation project Report by using R packages(ggplot) on the Global terrorism dataset(1970-2015) using different interactive graphs, different combination of colours had been used so that colour blind people can also visualise the patterns.
Implemented Data warehouse on “Retail Stores of five states of USA” by using 3 different data sources including structured and unstructured using SSIS, SSAS and Power BI.
Implemented salesforce and CRM application, in this application employees and customers are sharing same platform which increases productivity and saves time for customers.
Developed a home security system to protect occupants from fire and intrusion. The device sends SMS to the emergency number provided to it via GSM (Global System for Mobile communications) module. Led my group and implemented the device successfully.
Generated a Statistical Report on air quality of Ireland (correlation and regression) using SPSS and religious belief of different age group people in their respective religion(Two way ANOVA) using R.
AI Competitor Analysis: How to Monitor and Outperform Your CompetitorsContify
AI competitor analysis helps businesses watch and understand what their competitors are doing. Using smart competitor intelligence tools, you can track their moves, learn from their strategies, and find ways to do better. Stay smart, act fast, and grow your business with the power of AI insights.
For more information please visit here https://www.contify.com/
Defense Against LLM Scheming 2025_04_28.pptxGreg Makowski
https://www.meetup.com/sf-bay-acm/events/306888467/
A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold.
While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence?
The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces.
However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces.
Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)
