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Analyticsindiamag
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Reinforcement Learning Won Again, This Time With Microsoft
- Microsoft's Phi family of models, part of their AI research, includes lightweight and high-performing models that outshine competitors.
- Phi-4 Reasoning, a 14 billion-parameter model, was improved using supervised fine-tuning and reinforcement learning.
- These models excel in coding, math, and scientific tasks, surpassing larger models like DeepSeek R1.
- The success is attributed to high-quality training datasets with over 1.4 million prompts and answers generated by OpenAI.
- Reinforcement learning (RL) in Phi models allows for varied answers as long as the outcome is correct.
- The RL process in Microsoft's models focuses on mathematical reasoning by incentivising correctness and proper formatting.
- RL with Microsoft models requires less data compared to supervised fine-tuning, improving accuracy across evaluations.
- Despite successes, challenges remain, including resource consumption, slower response times, and contradictions in reasoning steps.
- Issues like reward hacking and discrepancies between reasoning chains and actual processes raise concerns in the AI community.
- Efforts to enhance interpretability and safety of reasoning models continue, aiming to understand model behaviors and improve overall performance.
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Analyticsindiamag
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HPE Appoints Bhawna Agarwal as MD of India Business
- Hewlett Packard Enterprise (HPE) appoints Bhawna Agarwal as the senior vice president and managing director of HPE India, replacing Som Satsangi who is retiring after over 27 years at the company.
- Agarwal joined HPE in 2019 and has experience in leading digital start-ups, media houses, and tech companies, bringing valuable expertise to her new role.
- Agarwal aims to build on the existing foundation, drive growth, and focus on innovation in the region as expressed in her statement.
- HPE recently launched the HPE Private Cloud AI solution in India to enable enterprises to deploy AI applications swiftly at scale, integrating NVIDIA's AI computing with HPE's capabilities.
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Analyticsindiamag
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L&T Semiconductor Aims to Be the NVIDIA India Never Had
- L&T Semiconductor Technologies (LTSCT) aims to become India's first major semiconductor product company, departing from the traditional services model.
- Led by CEO Sandeep Kumar, LTSCT focuses on building chip IP and becoming a full-fledged chipmaker to rival global players like NVIDIA.
- With an initial capital of ₹830 crore, LTSCT targets a revenue of $1 billion in the next four to five years by adopting a fabless model initially.
- Recognizing the need for product companies, LTSCT aims to bring Indian semiconductor products to the global market, not just manufacturing chips for others.
- The company plans to develop a variety of chips targeting sectors like mobility, industrial, energy, and telecom globally, with a pipeline of over 50 clients.
- India's semiconductor market is projected to grow significantly, presenting opportunities for companies like LTSCT in various sectors such as mobile handsets, IT, and automotive.
- While currently fabless, LTSCT plans to establish its own fabs in the future, based on demand, to prevent empty fab scenarios and maintain optimal production costs.
- LTSCT's strategic approach focuses on product quality and global competitiveness rather than direct competition with fabs, aiming to establish a strong semiconductor product brand from India.
- By aiming to create India's own NVIDIA, LTSCT is poised to pave the way for India to have a globally recognized semiconductor brand in the future.
- As India emphasizes semiconductor self-reliance, initiatives like LTSCT play a crucial role in reducing dependency on foreign players and boosting the domestic semiconductor industry.
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Analyticsindiamag
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WiDS Bangalore @ Intuit Returns
- The WiDS Bangalore @ Intuit conference, organized by Intuit India, is set to return with a focus on Gen AI and Agentic AI.
- The event will feature vision talks by industry leaders, networking sessions, and opportunities for attendees to learn and collaborate.
- WiDS aims to elevate women in data science by providing a platform for community building and fostering opportunities in the field.
- The conference is scheduled for June 12 at Intuit India, Bengaluru, offering a platform for knowledge-sharing and networking in the data science community.
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Medium
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How To Start Strong In Your First Week As An Engineering Manager
- Starting as an engineering manager (EM) can be challenging, requiring thorough preparation and effective communication in the first week.
- Prepare for initial meetings by gathering context and asking insightful questions to understand team dynamics and ongoing projects.
- Initiate a strong foundation with your manager by suggesting ideas and setting up regular check-ins to align on priorities.
- Establish clear communication channels with your manager, adapt to their preferences, and ensure clarity in all discussions.
- Build strong relationships with your team by reintroducing yourself, showing genuine curiosity, and proactively addressing any power dynamics.
- Maintain a professional demeanor, be mindful of your words, and set new boundaries as you navigate the transition from peer to manager.
- Schedule regular one-on-one meetings with your engineers to build trust, understand their challenges, and provide support.
- Embrace the shift from hands-on coding to managerial responsibilities, understanding that your team's technical capacity will rely on your guidance.
- Reflect on your progress at the end of the week, identify obstacles, and focus on continuous improvement to enhance team productivity.
- Encourage a culture of continuous improvement within your team by seeking small but impactful changes and fostering proactive problem-solving.
- Invest time in learning about your team, company, and aligning technical direction while prioritizing relationships and team growth.
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Medium
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Roadmap to Become a Big Data Engineer: Your Path to Mastering Data
- 1. Master Python, Java, and Scala for big data applications.
- 2. Gain expertise in Hadoop, Apache Spark, and Apache Flink for big data frameworks.
- 3. Learn SQL, NoSQL, and data warehousing with Amazon Redshift and Google BigQuery for databases and data warehousing.
- 4. Utilize Apache Kafka, Apache NiFi, and AWS Kinesis for efficient data ingestion with data ingestion tools.
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Medium
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Catch Breakouts Smarter: Build a CVD Divergence Detector in Pine Script
- Breakouts often fail due to lack of real buying pressure, not bad patterns.
- Using Cumulative Volume Delta (CVD) can help in detecting fakeouts early.
- Building a CVD Divergence Detector in Pine Script can assist in spotting breakout weakness signs.
- CVD divergence is a useful warning sign that something might be out of sync in the market.
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Medium
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"Skincare for Oily Skin: Controlling Shine and Reducing Acne:
- Summary of recommended skincare products for oily skin: Kiehl’s Rare Earth Deep Pore Daily Cleanser, PanOxyl Acne Creamy Wash, Cetaphil DermaControl Oil Removing Foam Wash, Paula’s Choice Skin Balancing Pore-Reducing Toner, Neutrogena Pore Refining Toner, Mario Badescu Glycolic Acid Toner, CeraVe PM Facial Moisturizing Lotion, SkinMedica Ultra Sheer Moisturizer, Origins Clear Improvement Moisturizer, The INKEY List Niacinamide Oil Control Serum, Primally Pure Clarifying Serum, Paula’s Choice Defense Antioxidant Pore Purifier, Skinceuticals Clarifying Clay Masque, Neutrogena Pink Grapefruit 100% Hydrogel Mask, Andalou Naturals Pumpkin Honey Glycolic Mask, Origins Clear Improvement Active Charcoal Mask, and Paula’s Choice... (list continues)
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Analyticsindiamag
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Google Cloud to Collaborate with IndiaAI Mission to Ramp Up AI Infrastructure
- Google Cloud to collaborate with IndiaAI Mission to ramp up AI infrastructure, as announced by Bikram Singh Bedi, VP and MD of Google Cloud India.
- Google Cloud aims to work closely with the Indian government and the electronics and information technology ministry to serve public sector needs and support the AI mission opportunity.
- The company's focus on public sector, particularly companies associated with the Indian government, remains strong with plans to introduce new versions of technology locally.
- Google Cloud is actively expanding its presence in India, offering cloud services, with data centres in Delhi and Mumbai and considerations for investment in Navi Mumbai to cater to digital needs.
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Dev
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3337. Total Characters in String After Transformations II
- Given a string s, an integer t, and an array nums of size 26 representing transformations on s.
- Each character in s is replaced by the next nums[s[i] - 'a'] consecutive characters, wrapping around if needed.
- Return the length of the resulting string after t transformations, modulo 10^9 + 7.
- Implementing matrix multiplication efficiently handles large values of t.
- Strategy involves representing transformations as matrices, using matrix exponentiation, and calculating dot products.
- Example transformations and their impact on the length of the string are illustrated.
- Functions in PHP for matrix operations like matrix multiplication and exponentiation are provided.
- Matrix Construction builds a matrix indicating characters generated during transformations.
- Matrix Exponentiation efficiently handles large exponents using exponentiation by squaring.
- Contribution Calculation computes character contributions to the final length using precomputed matrices.
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Medium
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Your Next Sales Rep Might Be an AI Avatar. But Your Best One Will Still Be Human.
- AI is increasingly being utilized in sales and customer service, with chatbots and virtual agents streamlining operations and reducing costs.
- Functions like prioritizing high-value leads, predicting churn, and personalizing offers are now being efficiently managed by AI tools.
- However, AI still falls short in areas like empathy, building client relationships, storytelling, and handling complex situations with the finesse of humans.
- The future of sales lies in a symbiotic relationship between AI and human intelligence, leveraging the strengths of both to drive business growth and enhance customer experiences.
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Medium
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The Math Behind the Magic: Why Data Science Needs More Than Code
- Data science relies heavily on mathematics to power insights and predictions, going beyond just coding and tools like Python and Tableau.
- Statistics helps in making sense of real-world data, while linear algebra is essential for machine learning techniques like training models.
- Calculus plays a key role in optimizing models for efficiency, and probability theory is crucial for making informed predictions in areas like spam detection.
- Understanding the math behind data science tools builds intuition, allowing data scientists to trust, tune, and troubleshoot models effectively.
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Medium
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Linear Algebra in Machine Learning: Smoothie Analogy for Learning Math
- Linear algebra, often perceived as complex, is fundamental to machine learning and can be understood through a fun analogy like making smoothies.
- Linear algebra serves as the backbone of many machine learning algorithms, helping in calculations for input-output relationships.
- Using the example of a smoothie shop with different recipes, the concept of matrices and vectors is explained in relation to linear algebra.
- By visualizing linear algebra concepts through everyday analogies, such as smoothie-making, one can develop a better understanding of its applications in machine learning.
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Towards Data Science
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Get Started with Rust: Installation and Your First CLI Tool – A Beginner’s Guide
- Rust is a popular programming language known for its security and high performance, combining features of C, C++, and simplicity of modern languages like Python.
- Installation of Rust is made easy through the official installer rustup, available for free on the Rust website.
- For Windows installation, downloading rustup-init.exe and running it through command line completes the process.
- On Linux, Rust can be installed through the terminal using a specific command.
- For macOS, installation via Homebrew or a script is possible.
- Using cargo, the official package manager and build system of Rust, a new project can be initiated with ease.
- Cargo assists in project management by handling dependencies, compilation, tests, and builds.
- Dependencies like serde and serde_json facilitate working with data formats like JSON.
- By following set-up steps and writing Rust code, a simple CLI tool to parse and display JSON content can be created.
- The process includes creating a project, defining dependencies, writing code for JSON parsing, and testing the CLI tool.
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Towards Data Science
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Non-Parametric Density Estimation: Theory and Applications
- Density estimation is essential in statistical analysis for inferring the probability density function of a random variable given a sample data. It can be used for distribution analysis, classification tasks, and more.
- Histograms and Kernel Density Estimators (KDEs) are popular non-parametric methods for density estimation, with KDEs being a smoother alternative to histograms.
- Density estimation methods may be parametric (assuming a known distribution) or non-parametric (making no rigid assumptions about the distribution). Non-parametric methods like KDEs typically have lower bias and higher variance.
- Histograms partition data into bins, while KDEs compute weighted sums of neighboring points. KDEs generalize the histogram approach and are commonly used in practice.
- Kernels play a crucial role in KDE, with choices like Gaussian, Epanechnikov, rectangular, and triangular, influencing the smoothness of the density estimate.
- The accuracy of density estimators is influenced by bias and variance trade-offs, with bandwidth selection impacting the estimation quality.
- In classification tasks, density estimation can be used to build classifiers like Naive Bayes, where parametric and non-parametric density estimates affect decision boundaries and classification accuracy.
- Non-parametric Naive Bayes classifiers may provide more flexible decision boundaries but could introduce roughness, compared to smoother decision boundaries from parametric approaches.
- Understanding density estimation theory, methods like histograms and KDEs, and their applications in classification tasks offer valuable insights for statistical analysis.
- Resources like notes on nonparametric statistics, statistical learning textbooks, and datasets like the famous Iris dataset can aid in further exploration of density estimation.
- The choice between parametric and non-parametric density estimation depends on the dataset characteristics, with parametric assumptions often offering smoother decision boundaries in classification tasks.
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