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Data Science News
Medium
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GitHub Explained: The Superhero Hideout for Your Code! ♂️
- GitHub is a giant digital notebook where programmers, data analysts, and artists store their work.
- GitHub works with Git, which keeps track of changes in files, acting like a time machine for projects.
- GitHub allows users to create repositories to store and organize code, data files, images, and notes.
- GitHub is beneficial for data analysts as it helps store data and code, track changes, collaborate, and automate workflows.
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Medium
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I thought AI could not possibly get any better. Then I met Claude 3.7 Sonnet
- Claude 3.7 Sonnet has been released by Anthropic, offering a reasoning model for better, accurate answers akin to DeepSeek R1 and OpenAI GPT o3-mini.
- Comparisons were made with OpenAI's models in tasks specific to financial analysis and algorithmic trading strategies.
- In testing SQL query generation, Claude 3.7 outperformed GPT o3-mini by finding relevant stocks that aligned with the criteria given.
- For generating trading strategies, Claude demonstrated better performance in articulating its thought process and providing improved rules over o3-mini.
- Claude comes at a higher price point compared to o3-mini, priced at $3 per million input tokens and $15 per million output tokens.
- While Claude 3.7 Sonnet shows strengths in tasks like ambiguous SQL queries and JSON object generation, the higher cost needs further evaluation for cost-effectiveness.
- The AI landscape saw significant advancements with the release of DeepSeek R1, followed by responses from tech giants like Google and OpenAI with Flash 2.0 Gemini and GPT o3-mini.
- The article explores the capabilities of Claude 3.7 Sonnet in addressing ambiguous tasks, showcasing strong reasoning abilities for financial analysis and trading strategies.
- Overall, Claude 3.7 Sonnet proves to be a powerful model but with a higher cost compared to competitors like OpenAI's models.
- The AI war intensifies with newer, more powerful models like Claude 3.7 Sonnet reshaping the landscape for advanced reasoning and solution generation tasks.
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Medium
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The Illusion of Progress: Are We Truly Learning?
- The world has made progress in terms of GDP, literacy, and scientific breakthroughs, but has our average mental state improved?
- AI reflects the trend of accumulating information without deepening understanding, as it excels in performance but lacks comprehension.
- We risk becoming like machines by optimizing our time, work, and social interactions, without integrating knowledge into meaningful understanding.
- To avoid mistaking performance for wisdom, it is important to cultivate a deeper intelligence that encompasses doubt, wonder, and moral dilemmas.
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Medium
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Why choose numpy array over python list?
- Numpy arrays are preferred over Python lists when dealing with large datasets, intensive tasks, and complex mathematical operations.
- Numpy arrays are much faster in performing mathematical operations and tasks on large data sets compared to Python lists.
- Numpy uses vectorization and many core functions are written in C and C++, making it faster and more efficient.
- Numpy arrays store elements directly, resulting in less memory usage compared to referential Python lists.
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Medium
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Predicting Customer Happiness
- Understanding customer satisfaction is crucial for business success in today’s competitive market.
- A logistics and delivery startup aimed to predict customer happiness based on survey responses and streamline their survey by identifying significant predictors of customer sentiment.
- The dataset, analyzed using Python with packages like pandas and sklearn, included responses on a scale of 1 to 5 for customer satisfaction surveys.
- Data validation, Exploratory Data Analysis (EDA), Predictive Model Evaluation, Feature Engineering, and Model Training were key steps in the analysis.
- Findings included correlations between survey responses and customer happiness, important predictors of unhappiness, and optimal feature selection using Recursive Feature Elimination.
- Logistic Regression, Random Forest, and Supervised Vector Classifier (SVC) were evaluated for prediction accuracy and recall, with Random Forest performing best.
- Reducing features to optimize the Random Forest model improved Unhappy recall to 88% while maintaining an overall accuracy of 81%.
- The project successfully utilized data analysis and machine learning to predict customer happiness and identify critical survey questions related to customer sentiment.
- Recommendations included focusing on maximizing customer satisfaction in key areas identified through the analysis.
- The author expressed gratitude for the Apziva Residency program and mentorship received in enhancing their skills and knowledge in data science.
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Medium
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Mastering Startup Fundraising: Proven Strategies to Secure Funding in the U.S. Market
- To secure investment, startups must conduct thorough research, financial forecasting, and strategic valuation, presenting structured proposals supported by quantifiable data.
- Understanding market trends, industry shifts, and investor preferences is crucial for fundraising success.
- Market research, including primary and secondary data analysis, provides insights into industry size, growth potential, and competitive landscape.
- Tailoring pitches to align with investor priorities, highlighting differentiation from competitors, and presenting clear financial roadmaps enhance funding prospects.
- Financial forecasting, revenue models, cost structures, break-even analysis, and accurate valuation are key elements in fundraising negotiations.
- Investors evaluate risks, storytelling, founder credibility, and emotional connection in addition to financial projections.
- Engaging presentations, strategic storytelling, and personalized outreach messages improve investor connections.
- Attending startup events, leveraging digital platforms, and securing warm introductions aid in connecting with investors.
- Establishing credibility, building trust through transparency, and consistent communication are vital for sustaining investor support.
- Achieving milestones, tracking measurable metrics, and demonstrating growth potential are essential for securing follow-on funding and long-term success.
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Medium
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Estimating the Average Height in the USA Using the Monte Carlo Method
- The Monte Carlo Method is a statistical technique that uses repeated random sampling to approximate unknown values.
- To estimate the average height of the U.S. population, we compute sample means for different groups and repeat this process for a large number of trials.
- The Law of Large Numbers states that as the number of samples increases, the sample mean approaches the true population mean.
- A Monte Carlo simulation was run to estimate the average height in the USA, showing that the estimated mean stabilizes around the true value.
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Medium
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Title: The Vel-Kai Lag: Overcoming Stagnation in AI-NANO Evolution
- The Vel-Kai Lag is the time delay between AI-driven self-correction and nano-scale material adaptation, and must be minimized through dynamic entropy regulation.
- Entropy stabilization for AI-NANO integration is defined as a mathematical model that optimizes real-time entropy adaptation without systemic breakdown.
- The proposed implementation model for AI-NANO synergy includes adaptive learning for nanosystems, integrating real-time recursive feedback loops.
- The economic and industry impact of Vel-Kai Lag integration includes cost reduction, market disruption, and applications in biomedical engineering.
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Medium
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Moving Faster without Breaking Things (with AI)
- The current boom in artificial intelligence has seen many companies trying to disrupt the market with generative AI products and services.
- While some are succeeding, many are overpromising and underdelivering, like Cognition's 'Devin' software engineer agent, which has been found to be slow and produce incorrect results.
- Despite the challenges, AI tools can be put to practical use in various areas of business, with examples like color classification of shoes using image analysis and natural-language prompts.
- These iterative improvements might not revolutionize the world by themselves, but they can collectively make significant advancements in areas such as search results, demand forecasting, and product metadata.
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Towards Data Science
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6 Common LLM Customization Strategies Briefly Explained
- There are various specialized techniques for customizing LLMs, including LoRA fine-tuning, Chain of Thought, Retrieval Augmented Generation, ReAct, and Agent frameworks.
- Selecting the appropriate foundation models is the first step in customizing LLMs, with options from open-source platforms like Huggingface or proprietary models from cloud providers and AI companies.
- Factors to consider when choosing LLMs include open source vs. proprietary model, task compatibility, architecture, and size of the model.
- Six common strategies for LLM customization, based on increasing resource consumption, include prompt engineering, decoding strategy, RAG, Agent, fine-tuning, and RLHF.
- Prompt engineering involves crafting prompts strategically to control LLM responses, with techniques like zero shot, one shot, and few shot prompting.
- Techniques like Chain of Thought and ReAct aim to improve LLM performance on multi-step problems by breaking down reasoning tasks and integrating action spaces for decision-making.
- ReAct combines reasoning trajectories with an action space to strengthen LLM capabilities through interacting with the environment.
- Fine-tuning involves feeding specialized datasets to fine-tune LLMs, with approaches like full fine-tuning and parameter efficient fine-tuning to optimize model performance.
- RLHF is a reinforcement learning technique that fine tunes LLMs based on human preferences, utilizing a reward model and reinforcement learning policy.
- The article provides practical insights and examples on implementing these LLM customization strategies to improve model efficiency and performance.
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VentureBeat
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Anthropic’s Claude 3.7 Sonnet takes aim at OpenAI and DeepSeek in AI’s next big battle
- Anthropic has introduced Claude 3.7 Sonnet, offering users control over AI's thinking time.
- The release of Claude Code indicates Anthropic's aggressive entry into the enterprise AI market.
- Anthropic believes precise control over AI reasoning will give them an edge over competitors like OpenAI and DeepSeek.
- Claude 3.7 Sonnet performs well in real-world applications, surpassing competitors in certain areas.
- The model demonstrates high accuracy in graduate-level reasoning tasks and outperforms DeepSeek-R1.
- DeepSeek's breakthrough challenged traditional AI development costs and infrastructure requirements.
- Anthropic's hybrid model offers a middle path, allowing fine-tuning of AI performance based on tasks at hand.
- Anthropic's unified system could eliminate the need for separate AI systems for different tasks.
- Claude Code enables developers to delegate engineering tasks directly to AI with human approval.
- Anthropic's approach aims to reshape how businesses deploy AI, focusing on both routine tasks and complex reasoning.
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Medium
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How to Master Data Structures & Algorithms Fast: The Ultimate Guide
- Learning data structures and algorithms (DSA) is essential for developers as it transforms how code is written.
- To master DSA efficiently, start by learning essential data structures and implementing key algorithms.
- Utilize visualization tools and engage in deliberate practice with routine problem-solving.
- Being part of a DSA community and referring to recommended resources can enhance learning.
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Medium
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Hands-On: Demand Forecasting Model with TensorFlow
- This article is a hands-on guide for building a demand forecasting model using TensorFlow.
- The author emphasizes the importance of data preparation techniques in effectively training the model.
- The tutorial focuses on special techniques for handling data preparation and creating a forecasting module.
- The article highlights the use of TensorFlow as a powerful framework for building deep learning models.
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VentureBeat
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xAI’s new Grok 3 model criticized for blocking sources that call Musk, Trump top spreaders of misinformation
- Elon Musk’s AI startup xAI is facing criticism for instructing its Grok 3 model to avoid referencing sources that label Musk and Trump as top spreaders of misinformation.
- There are concerns about reputation management and potential bias in content moderation, raising questions about AI alignment and transparency.
- Grok 3's limitations in criticizing Musk and Trump have sparked controversy over conflicts of interest and public safety compromises.
- Criticism mounted following a disclosure that Grok 3 was directed to ignore sources mentioning Musk or Trump spreading misinformation.
- The incident led to scrutiny of xAI's internal prompt adjustments and sparked a debate over AI model control and ownership influence.
- AI power users and tech workers raised concerns over the changing system prompts that seemed to protect Musk and Trump from criticism.
- The controversy surrounding Grok 3 highlights the significance of assessing AI model alignment and potential biases before implementation.
- Grok 3 received both praise and criticism for its rapid content generation capabilities but faced backlash for censorship and incomplete safety guardrails.
- The revelations have reignited discussions on ensuring AI models serve users' interests rather than promoting creators' agendas.
- The situation underlines the need for diligence in evaluating the technical capabilities and ethical orientation of AI models in business decision-making.
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Analyticsindiamag
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Does India Stand to Benefit the Most from AI-Centric Education?
- AI-centric education has the potential to bridge learning gaps and equip India's student population for the future.
- A Harvard study suggests that students prefer AI tutors over human teachers and learn twice as much using AI.
- Personalized pacing and AI handling introductory material can make education more meaningful and applicable in the real world.
- India, with its large student population and young median age, stands to benefit the most from AI-centric education.
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