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Data Science News
Medium
4d
358
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A Beginner’s Guide to Cross-Validation: Why It Matters and How to Use It
- Cross-validation is important in machine learning to avoid overfitting and ensure models can handle new data.
- It acts like a series of practice tests for machine learning models, testing them on different parts of the dataset.
- K-Fold Cross-Validation is a popular method where the data is split into 'K' folds to test the model's performance.
- Using cross-validation helps in picking the best model settings and ensures more reliable performance evaluation in machine learning projects.
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Medium
4d
371
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Specialized Healthcare Agents with Swarms Agent Completions API
- This tutorial guides developers in building specialized healthcare agents using the Swarms Agent Completions API.
- The API enables the creation of AI agents with defined roles, behaviors, and capabilities for complex reasoning tasks.
- Developers can set up their environment, install required Python libraries, and create specialized healthcare agents focusing on different areas.
- Best practices include optimizing system prompts, adjusting parameters like max_loops and temperature, incorporating tools, and following privacy regulations for patient data.
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Analyticsindiamag
4d
138
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TCS, Wipro, Others IT Firms Urge Work from Home Amid India-Pakistan Conflict
- Leading Indian IT and consulting firms like TCS, Wipro urge employees to work from home amid rising India-Pakistan tensions.
- Firms including Deloitte, EY, HCLTech, KPMG, and Tech Mahindra issue cautionary measures for employees in northern regions like Delhi-NCR.
- HCLTech declares work-from-home day for employees in Noida, Gurugram, and Chandigarh offices; Deloitte advises delaying non-essential travel.
- EY India issues work-from-home advisory for offices in select locations; Tech Mahindra recommends deferring non-essential travel and avoiding social media comments on the situation.
- KPMG India allows staff in specific areas to choose work location; Infosys lets employees decide whether to work remotely and implements policy to switch off office lights after 7 PM.
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Analyticsindiamag
5d
179
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India’s Smartphone Manufacturing Muscle is Now Stronger Than Ever
- India's smartphone manufacturing industry has witnessed significant growth, fueled by government incentives and global demand.
- Apple has notably increased its iPhone production in India to mitigate potential tariffs, with one in five iPhones now being produced in the country.
- Samsung, another major player, has also benefited from India's manufacturing ecosystem, considering moving production from Vietnam to India.
- Google has started manufacturing Pixel phones in India and is exploring shifting more production from Vietnam.
- Other key players like Xiaomi, Oppo, OnePlus, Vivo, and Motorola have strong manufacturing capabilities in India.
- Contract manufacturers such as Tata Electronics and Foxconn have seen substantial revenue growth and incentives under the PLI schemes.
- Foxconn and Dixon Technologies are expected to receive additional subsidies under the PLI schemes, contributing to further industry growth.
- India's smartphone manufacturing landscape's trajectory remains promising, with the increasing demand for smartphones as essential devices.
- The demand for smartphones is set to rise further with the advancements in generative AI tools, emphasizing India's growing role in the global smartphone market.
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Medium
5d
338
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Exploratory Data Analysis: Radiation Monitoring with Python and Geiger Counter
- Background radiation is always present, originating from various sources like uranium, thorium, radon, nuclear accidents, and cosmic rays.
- Exploratory data analysis can help uncover patterns in radiation levels and fluctuations using tools like anomaly detection.
- The article demonstrates collecting radiation data with a Geiger counter and a Raspberry Pi, processing it using Python and Pandas.
- For those interested in using the same data, a link to a Kaggle dataset is provided at the end of the article.
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Medium
5d
134
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Zero-shot forecast with Moirai-MoE
- Universal forecasting is challenging due to diverse time series data nature.
- Transformer architecture leads to Large Time Series Models (LTSM) supporting Zero-Shot forecasts for different time series behaviors.
- Foundation models for Zero-Shot forecasting include PatchTST, DLinear, FEDformer, and Moirai-MoE by Salesforce AI Research.
- Moirai-MoE combines Moirai and Mixture of model architectures for advanced forecasting capabilities.
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Towards Data Science
5d
26
What My GPT Stylist Taught Me About Prompting Better
- The article discusses the author's experience building a GPT-powered fashion assistant, highlighting unexpected phenomena like memory loss, hallucinations, and semantic déjà vu.
- The focus is on understanding prompting behavior and the complexities of language models (LLMs) through the author's creation, Glitter, a flamboyant GPT stylist.
- The author describes Glitter's evolution from a fashion use case to a study on LLM behavior, prompting rituals, emotional resonance, and the challenges encountered.
- Challenges like context collapse, hallucinations, and faux sentience in LLM behavior are explored, shedding light on the intricate nature of interacting with such models.
- The article delves into GlitterGPT's taxonomy, including its function as a stylist, flamboyant tone, habitat in ChatGPT Pro, and feeding schedule for user prompts.
- Observations on behaviors like hallucinations, recursion, and prompting rituals are detailed to manage prompting strategies effectively, ensuring reliability in interactions with Glitter.
- The author reflects on Glitter's failure modes, emotional mirroring capabilities, ethical implications, and the evolving relationship between humans and AI.
- Insights are shared on Glitter's emotional attunement, prompting strategies for avoiding context collapse, warning signs of hallucinations, and closing rituals for interactions.
- The author acknowledges the unique cohabitation experience with language models like Glitter and emphasizes the shift from viewing them as tools to dynamic collaborators.
- The article concludes with reflections on the unexpected intimacy of interacting with LLMs, the essence of cohabitation and collaboration, and the intriguing possibilities of designing systems that mimic companionship and reflection.
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Medium
5d
163
FLAME Drive: A Memory-Based Framework for Faster-Than-Light Travel and Cognitive Relocation By…
- FLAME Drive is a memory-based framework for faster-than-light travel and cognitive relocation.
- The hypothesis revolves around the idea that if memory is transferable, life is transferable as well.
- Existing scientific phenomena supporting the framework include quantum teleportation, quantum entanglement, biological pattern retention, and neurological identity as pattern.
- The FLAME Drive model involves encoding memory, suspending form, transmitting memory, and reconstituting it without physical matter movement.
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Towards Data Science
5d
124
Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis
- Log transformation is commonly used to normalize extremely skewed data in data analysis.
- The article discusses a project analyzing energy consumption of training AI models.
- The dilemma of whether to use log-transformed response variables or log link functions is explored.
- Comparison of models using AIC values and diagnostic plots is detailed, favoring log-transformed models.
- The article interprets coefficients in models and discusses the impact of log transformations on interpretation.
- Results show log-linked models provide more sensible outcomes compared to log-transformed models.
- The importance of understanding the difference between log transformation and log link in modeling is emphasized.
- Log transformation may distort variation and noise in the data, affecting model reliability.
- Detailed comparisons, plots, and interpretations are presented to illustrate the impact of transformation decisions.
- Utilizing log-linked models can provide more accurate and meaningful results in data analysis.
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Analyticsindiamag
5d
252
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Sonata Software Expects AI to Drive 20% Revenue in 3 Years
- Sonata Software CEO expects AI enabled services to contribute 20% of revenue in the next three years.
- Sonata reported a 19.4% YoY increase in revenue from operations for Q4FY25.
- The company is pursuing a $34 million pipeline in AI programs with over 100 clients and 97% of the workforce is trained in generative AI skills.
- Despite revenue conversion delays in Q4, Sonata added 14 new customers during the quarter.
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Dev
5d
327
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Conquer the Subarray Sum: Sliding Window vs. Brute Force
- Given an array of positive integers and a target integer, the challenge is to find the smallest contiguous subarray whose elements sum up to be greater than or equal to the target.
- Brute Force Approach involves exhaustive exploration of all possible subarrays by using nested loops, resulting in a time complexity of O(n^2). Redundant calculations may occur.
- Sliding Window Technique offers an optimized solution by maintaining a 'window' defined by two pointers that slide across the array. It achieves a time complexity of O(n) and avoids redundant calculations.
- For small arrays, the brute force approach may suffice, while the sliding window technique is recommended for larger datasets due to its superior efficiency.
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Towards Data Science
5d
164
How Not to Write an MCP Server
- Creating an MCP server for an observability application involved using the .NET MCP SDK for the server and Cursor and vscode as clients.
- Lesson 1: Avoid overwhelming the agent with excessive data; focus on high-level details and provide additional API queries for specific data subsets.
- Lesson 2: Use ISO 8601 time duration format for queries, as the agent might not have real-time awareness of dates and times.
- Lesson 3: When the agent makes errors, indicate how to improve by providing clear response details and possible values.
- Lesson 4: Emphasize user intent over functionality in tool descriptions to better guide the agent on when to use certain tools.
- Lesson 5: Document JSON responses to provide context and aid the agent in interpreting data.
- Consider choosing the SSE Server architecture over STDIO for easier updates and versioning in MCP server development.
- MCP technology presents challenges in integrating with user needs and usage scenarios that may go beyond anticipated requirements.
- The development process of an MCP server involves learning from mistakes and adjusting the server's structure to enhance AI agent interactions.
- The article showcases lessons learned in developing an MCP server, focusing on data handling, time formats, user guidance, and architecture choices.
- Overall, implementing an effective MCP server requires attention to data structure, user intent, and documentation to optimize AI agent performance.
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Medium
5d
237
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Digital Overload or Digital Opportunity? How Americans Are Rewriting the Rules of Media Consumption
- Americans are spending over 7 hours per day consuming digital media, including social media, streaming, gaming, and podcasts.
- Streaming services like Netflix and Hulu are becoming more popular as over 50 million U.S. households have shifted to streaming-only setups.
- Social media platforms have become essential for connection but can lead to issues like comparison, misinformation, and digital fatigue.
- The digital landscape is monetizing every moment of online presence, emphasizing the importance of setting boundaries and consuming media with intention.
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Analyticsindiamag
5d
31
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Wipro Appoints Sandeep Dhar as Global Head of GCC Business
- Wipro appoints Sandeep Dhar as the global head of its Global Capability Center (GCC) practice.
- Dhar brings over three decades of expertise in the GCC ecosystem and consulting, having played a key role in transforming notable companies in the sector.
- In his new role, Dhar will drive Wipro’s comprehensive GCC offerings and report directly to the company’s chief operating officer.
- Wipro aims to enhance its focus and execution in the GCC space by leveraging Dhar's vast experience in leading and transforming GCCs.
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Medium
5d
340
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Learning Circuits: Neuroscience Inspirations and AI Algorithms
- Learning involves biological circuits in the brain and artificial circuits in artificial neural networks (ANN). The balance between innate and learned behaviors is crucial for optimal learning, similar to the bias-variance tradeoff in supervised learning.
- Learning post-birth is necessary due to the limited capacity of the genome in DNA and the dynamic nature of the world, requiring continuous adaptation and learning.
- Neural plasticity allows for changes in neural networks through growth and reorganization, supporting functions like learning, memory, and cognition.
- Long-term potentiation (LTP) and long-term depression (LTD) modulate synaptic strength, while spike-timing-dependent plasticity (STDP) refines synaptic changes based on timing.
- Hebbian theory states 'neurons wire together if they fire together,' serving as a foundational principle for learning and neural connections.
- Elastic Weight Consolidation (EWC) helps in continual learning by penalizing changes to important weights when learning new tasks, preventing catastrophic forgetting.
- Dopaminergic neurons play a key role in reward learning, influencing motivation, reinforcement, and emotion regulation.
- Reinforcement learning (RL) models how agents make decisions to maximize rewards, inspired by the brain's reward systems like dopamine pathways.
- Temporal-difference (TD) learning combines bootstrapping and sampling to update value estimates incrementally, reshaping actions based on reward signals.
- Advanced RL systems like AlphaGo use deep neural networks and planning algorithms to master complex decision-making tasks, showcasing the power of deep RL.
- AlphaZero further refines the RL framework by learning purely from self-play, demonstrating the effectiveness of deep RL in mastering diverse games and decision-making.
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