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🗣️ Geoffrey Hinton: AI will be wonderful for productivity, but it may not be wonderful for society. Because, in the current system, increased productivity benefits the rich and big companies, and ordinary people may suffer due to job losses
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GeeksforGeeks brings you everything you need to crack GATE 2026 – 900+ live hours, 300+ recorded sessions, and expert mentorship to keep you on track.
What’s inside?
✔ Live & recorded classes with India’s top educators
✔ 200+ mock tests to track your progress
✔ Study materials - PYQs, workbooks, formula book & more
✔ 1:1 mentorship & AI doubt resolution for instant support
✔ Interview prep for IITs & PSUs to help you land opportunities
Learn from Experts Like:
Satish Kumar Yadav – Trained 20K+ students
Dr. Khaleel – Ph.D. in CS, 29+ years of experience
Chandan Jha – Ex-ISRO, AIR 23 in GATE
Vijay Kumar Agarwal – M.Tech (NIT), 13+ years of experience
Sakshi Singhal – IIT Roorkee, AIR 56 CSIR-NET
Shailendra Singh – GATE 99.24 percentile
Devasane Mallesham – IIT Bombay, 13+ years of experience
Use code UPSKILL30 to get an extra 30% OFF (Limited time only)
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Physicists think AI is physics.
Statisticians think AI is statistics.
Mathematicians think AI is mathematics.
Psychologists think AI is psychology.
Neuroscientists think AI is neuroscience.
And they’re all right.
Statisticians think AI is statistics.
Mathematicians think AI is mathematics.
Psychologists think AI is psychology.
Neuroscientists think AI is neuroscience.
And they’re all right.
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Essential Data Analysis Techniques Every Analyst Should Know
1. Denoscriptive Statistics: Understanding measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation) to summarize data.
2. Data Cleaning: Techniques to handle missing values, outliers, and inconsistencies in data, ensuring that the data is accurate and reliable for analysis.
3. Exploratory Data Analysis (EDA): Using visualization tools like histograms, scatter plots, and box plots to uncover patterns, trends, and relationships in the data.
4. Hypothesis Testing: The process of making inferences about a population based on sample data, including understanding p-values, confidence intervals, and statistical significance.
5. Correlation and Regression Analysis: Techniques to measure the strength of relationships between variables and predict future outcomes based on existing data.
6. Time Series Analysis: Analyzing data collected over time to identify trends, seasonality, and cyclical patterns for forecasting purposes.
7. Clustering: Grouping similar data points together based on characteristics, useful in customer segmentation and market analysis.
8. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables in a dataset while preserving as much information as possible.
9. ANOVA (Analysis of Variance): A statistical method used to compare the means of three or more samples, determining if at least one mean is different.
10. Machine Learning Integration: Applying machine learning algorithms to enhance data analysis, enabling predictions, and automation of tasks.
Like this post if you need more 👍❤️
Hope it helps :)
1. Denoscriptive Statistics: Understanding measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation) to summarize data.
2. Data Cleaning: Techniques to handle missing values, outliers, and inconsistencies in data, ensuring that the data is accurate and reliable for analysis.
3. Exploratory Data Analysis (EDA): Using visualization tools like histograms, scatter plots, and box plots to uncover patterns, trends, and relationships in the data.
4. Hypothesis Testing: The process of making inferences about a population based on sample data, including understanding p-values, confidence intervals, and statistical significance.
5. Correlation and Regression Analysis: Techniques to measure the strength of relationships between variables and predict future outcomes based on existing data.
6. Time Series Analysis: Analyzing data collected over time to identify trends, seasonality, and cyclical patterns for forecasting purposes.
7. Clustering: Grouping similar data points together based on characteristics, useful in customer segmentation and market analysis.
8. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables in a dataset while preserving as much information as possible.
9. ANOVA (Analysis of Variance): A statistical method used to compare the means of three or more samples, determining if at least one mean is different.
10. Machine Learning Integration: Applying machine learning algorithms to enhance data analysis, enabling predictions, and automation of tasks.
Like this post if you need more 👍❤️
Hope it helps :)
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🖥 Free Courses on Large Language Models
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#ai #generativeai
▪ChatGPT Prompt Engineering for Developers
▪LangChain for LLM Application Development
▪Building Systems with the ChatGPT API
▪Google Cloud Generative AI Learning Path
▪Introduction to Large Language Models with Google Cloud
▪LLM University
▪Full Stack LLM Bootcamp
#ai #generativeai
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Forwarded from Artificial Intelligence - ChatGPT & AI Tech News
🔅 Grok AI is now on telegram
Pavel Durov just confirmed it: after hitting 1 billion monthly active users, Telegram is integrating Grok AI. The bot will be available free for all Telegram Premium subscribers.
This is Grok’s first big move beyond X and another step in Elon’s mission to put his AI everywhere. The timing? Perfect. Telegram’s growth meets Elon’s ambition.
Pavel Durov just confirmed it: after hitting 1 billion monthly active users, Telegram is integrating Grok AI. The bot will be available free for all Telegram Premium subscribers.
This is Grok’s first big move beyond X and another step in Elon’s mission to put his AI everywhere. The timing? Perfect. Telegram’s growth meets Elon’s ambition.
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Ford Looks to Innovate Faster With AI Agents and Nvidia GPUs
Bryan Goodman, Ford Motor’s director of artificial intelligence, said he’s pushing forward a number of use cases for artificial intelligence at the company, as part of an effort to design and engineer new cars as quickly as its competitors.
Bryan Goodman, Ford Motor’s director of artificial intelligence, said he’s pushing forward a number of use cases for artificial intelligence at the company, as part of an effort to design and engineer new cars as quickly as its competitors.
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