Dwarves Foundation’s Post

🌐 Graph Algorithms: Navigating Networks and Relationships Graph Algorithms are powering some of today’s most complex technologies—from social media networks to logistics to machine learning. Here’s why mastering them is more than just a technical skill; it’s about understanding connections in our world. 🌎 Why Graph Algorithms Matter: Relationship Analysis: From LinkedIn connections to route-finding in Google Maps, graphs model real-world relationships effectively. Optimization: Algorithms like Dijkstra’s and A* help find the most efficient routes in transportation and logistics. Pattern Detection: Graphs help detect fraud patterns in finance, by uncovering suspicious connections or cycles. 🗺️ Popular Graph Algorithms: Breadth-First Search (BFS) & Depth-First Search (DFS): Essential for traversing and exploring graphs. Dijkstra’s Algorithm: Finds the shortest path, critical in navigation and networking. PageRank: Google’s original algorithm for ranking pages, based on node relationships! Which graph algorithm are you most excited to work with? Or do you have a favorite? Share in the comments how it’s impacted your projects or business insights! #GraphAlgorithms #Networks #DataScience #TechnologyInsights

📢Let’s Deep Dive into 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬!🤖 1️⃣ 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 & 𝐐𝐮𝐞𝐮𝐞𝐢𝐧𝐠 🔗: 👉🏻 Explore the fundamentals of data structures. 👉🏻 Understand stacks, queues, and binary search trees. 2️⃣ 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 & 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐆𝐞𝐨𝐦𝐞𝐭𝐫𝐲 📐: 📣 Unravel the secrets behind computational problem-solving. 📣 Master pattern matching and optimization techniques. 3️⃣ 𝐇𝐚𝐬𝐡𝐢𝐧𝐠 & 𝐆𝐫𝐚𝐩𝐡 𝐓𝐡𝐞𝐨𝐫𝐲 📊: 🪃 Delve into the power of hashing and its applications. 🪃 Navigate the world of graphs—undirected, directed, and their traversal methods. 4️⃣ 𝐒𝐡𝐨𝐫𝐭𝐞𝐬𝐭 𝐏𝐚𝐭𝐡𝐬 & 𝐒𝐩𝐚𝐧𝐧𝐢𝐧𝐠 𝐓𝐫𝐞𝐞𝐬 🌐: 🔓 Unlock the secrets of algorithms like Dijkstra and Bellman-Ford. 🪬 Explore minimum spanning trees with Prim and Kruskal's algorithms. 5️⃣ 𝐆𝐫𝐚𝐩𝐡 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 & 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 🔄: 🧑🏻🏫 Learn the ins and outs of BFS and DFS. 🚨 Demystify linear-time sorting methods and graph-related algorithms. 6️⃣ 𝐃𝐢𝐯𝐢𝐝𝐞 𝐚𝐧𝐝 𝐂𝐨𝐧𝐪𝐮𝐞𝐫, 𝐁𝐚𝐜𝐤𝐭𝐫𝐚𝐜𝐤𝐢𝐧𝐠 & 𝐌𝐨𝐫𝐞 🧩: ♨️ Embrace Divide and Conquer techniques for problem-solving. ♨️ Discover the magic behind backtracking and dynamic programming. 7️⃣ 𝐒𝐭𝐫𝐢𝐧𝐠 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 & 𝐆𝐫𝐞𝐞𝐝𝐲 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 🔍: 🧑🏻💻 Master algorithms like Knuth-Morris-Pratt, Boyer-Moore, and Rabin-Karp. 📚 Explore the efficiency and simplicity of greedy algorithms. 🔄Follow me Priyanshu Sharma 📌 for more contents. 🤝🏻Join My Telegram Channel: - https://lnkd.in/gZ_FRXGd 📍Credit: - Santosh Kumar Mishra 💠 #dsa #datastructure #algorithms #dsachallenge #dsacoding #learning #learningandgrowing #learningeveryday #learninginprogress #learninganddevelopment #skillup

𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆: 𝐔𝐧𝐥𝐨𝐜𝐤𝐢𝐧𝐠 𝐋𝐋𝐌 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐨𝐧 𝐧𝐚𝐫𝐫𝐚𝐭𝐢𝐯𝐞 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐝𝐚𝐭𝐚 💬 Exploring New Frontiers in LLMs with GraphRAG by Microsoft Research Large Language Models (LLMs) hold the potential not only to process and understand data they've been trained on but also to apply their capabilities to entirely new, unseen data. This is where the real opportunity lies – in extending LLMs to uncover insights in novel datasets, particularly private and proprietary to businesses. Microsoft Research introduces GraphRAG, a pioneering advancement that significantly enhances LLMs' abilities in this area. 🔑 Key Highlights: ➡️ Retrieval-augmented generation (RAG) is a core technique in many LLM tools. It typically uses vector similarity for information retrieval. GraphRAG innovates by integrating LLM-generated knowledge graphs for improved query responses. ❗ Challenges with Baseline RAG: ➡️ Difficulty in connecting disparate information for comprehensive insights. ➡️ Inadequacy in grasping summarized semantic concepts across large datasets or documents. ✅ GraphRAG's Solution: ➡️ Utilizes knowledge graphs created from private datasets by LLMs, coupled with graph machine learning, to enhance prompt responses at query time, showing marked improvement in: ✔️ Synthesizing insights from varied information. ✔️ Understanding complex semantic concepts in extensive data collections. GraphRAG represents a leap forward in utilizing LLMs for private dataset analysis, offering intelligence and capability that surpasses previous methods. It's a testament to the tech community's ongoing efforts to refine and expand LLMs' utility, ensuring they remain at the cutting edge of data analysis and insight generation. cc Microsoft https://lnkd.in/gsa64H4m #GraphRAG #LargeLanguageModels #DataPrivacy #MicrosoftResearch #KnowledgeGraphs #DataScience #MachineLearning #FragmentStudio

Databricks acquires Lilac to supercharge data quality efforts for gen AI apps  Databricks announced the acquisition of Lilac, a Boston-based applied research startup offering tools for data understanding and manipulation. The terms of the deal were not disclosed.    Databricks is planning to bring over Lilac’s team and technology to its data intelligence platform, formerly known as the data lakehouse, giving users across domains a more seamless way to improve the quality of their datasets for developing production-quality large language model (LLM) applications.    With this move, Databricks is planning to become the Onestop shop for Gen AI Solutions, over this Databricks also invested in Mistral to get over the Models.    What is Lilac?    Lilac is a tool for exploration, curation and quality control of datasets for training, fine-tuning and monitoring LLMs.    Lilac runs on-device using open-source LLMs with a UI and Python API.    Background: In the era of AI-fueled by Data, having well-governed and curated data with the Garbage is critical in getting the Models. This is where Lilac plays a vital role.    Explore your data interactively with LLM-powered search, filter, clustering and annotation.   Curate AI data, applying best practices like removing duplicates, PII and obscure content to reduce dataset size and lower training cost and time. Inspect and collaborate with your team on a single, centralized dataset to improve data quality. Understand how data changes over time. Lilac is an open-source tool that enables data and AI practitioners to improve their products by improving their data. (After this Acquisition if Lilac Will still be Open Source is a Question) Lilac Garden comes with Blazing fast dataset computations Lilac Cluster and title 1 million data points in 20 mins Embed your dataset at half a billion tokens per min Accelerate your data transformations Databricks also Acquired Mosaic ML that offers a platform to prepare the ML models with the Curated Data from Databricks. A Short Video @https://lnkd.in/gzxBXp4G Open Source @ https://lnkd.in/g5GGueD3

I'm excited to share that I've completed the Big Data Analytics track in R through DataCamp.com! This journey has been both challenging and rewarding, providing me with a deep dive into the world of big data, all through the lens of R programming. The courses I completed for this track are as follows: - Writing Efficient R Code Efficiency in code is crucial, especially when dealing with large datasets. This course equipped me with strategies to improve the performance of my R scripts, making them faster and more resource-effective. - Visualizing Big Data with Trelliscope in R: The power of visualization in making sense of big data cannot be overstated. Trelliscope allowed me to create flexible and scalable visual representations, turning complex datasets into insightful, understandable plots. - Scalable Data Processing in R: As data volumes grow, traditional processing methods fall short. This course taught me techniques to handle and analyze data at scale, ensuring my analyses remain robust and timely. - Introduction to Spark with sparklyr in R: Spark is a powerhouse for big data processing, and sparklyr brings this capability into R. Learning to use sparklyr has opened up new avenues for data manipulation and analysis, allowing me to tackle datasets of massive proportions with ease. Completing this track has not only expanded my skill set but also deepened my appreciation for the complexities and potentials within big data analytics. I look forward to applying these skills in real-world scenarios, driving insights that make a difference. I want to extend my gratitude to DataCamp for providing such a comprehensive and engaging learning path and to my network for the continuous support and encouragement. Here's to many more learning adventures ahead! #DataAnalytics #BigData #RProgramming #DataCamp #ContinuousLearning

𝐑𝐞𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐍𝐮𝐦𝐞𝐫𝐢𝐜𝐚𝐥 𝐔𝐧𝐝𝐞𝐫𝐟𝐥𝐨𝐰 𝐢𝐧 𝐍𝐚𝐢𝐯𝐞 𝐁𝐚𝐲𝐞𝐬 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬 Early programmers had significant challenges representing decimals in computer memory. While much progress has been made in this regard, we sometimes still have to deal with extremely small numbers (decimals) that are beyond the computer's precision. This 'underflow problem' happens rather frequently in machine learning, particularly in Naive Bayes classification. 𝐇𝐨𝐰 𝐔𝐧𝐝𝐞𝐫𝐟𝐥𝐨𝐰 𝐀𝐟𝐟𝐞𝐜𝐭𝐬 𝐭𝐡𝐞 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝐘𝐨𝐮𝐫 𝐍𝐚𝐢𝐯𝐞 𝐁𝐚𝐲𝐞𝐬 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 Naive Bayes classification relies on the 'naive' assumption that all features of a model are independent. This assumption then makes it acceptable to multiply the probabilities of these features belonging to a certain class when computing the Bayes calculations. However, in certain cases, we end up with very large or very small products that are very difficult for the computer to handle. For example, when building a Naive Bayes spam classifier, multiplying the values of the probabilities of each word of a 3000-word email appearing in a spam email can be very difficult. This can lead to an undesired increase in the occurrence of 'false positives' (a situation where the algorithm mistakenly classifies ham as spam). One of the most common approaches to resolving this is to use log probabilities. 𝐋𝐨𝐠 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 As a simple workaround to the complexity of multiplying so many small or large numbers, we can 'take the log' of each probability. It's mathematically proven that 𝐥𝐨𝐠(𝐚*𝐛) = 𝐥𝐨𝐠(𝐚) + 𝐥𝐨𝐠(𝐛). Since the classifier solely compares the probabilities of an input falling into a certain class, the specific values of the resulting probabilities are not critical. This means we can transform the products using a strictly increasing function that preserves the relationship between the resulting probabilities. Log probabilities achieve this while using a large summation to improve numerical stability. Empirically, this approach can boost your classifier's prediction accuracy by as much as 20%! #datascience #datascientist #machinelearning #bayesian #dataanalysis #classification

Perhaps the greatest challenge – and opportunity – of LLMs is extending their powerful capabilities to solve problems ....   GraphRAG, developed by Microsoft Research, is an innovative tool that integrates text extraction, network analysis, and large language models (LLMs) for a deep understanding of text-dense datasets.   Advantages:   - Extracts a knowledge graph automatically from unstructured text - Delivers hierarchical summaries of data, providing an overview without the need to predefine questions - Surpasses basic RAG methods in the comprehensiveness and diversity of the answers generated   My tutorial on medium: https://lnkd.in/d76Vz99N Microsoft repo: https://lnkd.in/dySSAx3J Microsoft blog: https://lnkd.in/d3xXNFMJ Microsoft Alon Bilorovsky Shamma R. #RAG #Knowledgraph #DataSciences

How online platforms are useful to learn deep and colab about "MACHINE LEARNING". #snsinstituitions #snsdesignthinking #snsdesignthinkers Many machine learning practitioners swear by Google Colab’s ability to solve storage problems and financial constraints. Hosted by Jupyter Notebook, Colab is also popular as it does not require any setup. However, we understand that its limited space, lack of live editing functionality, and time-consuming tasks tempt you to look for alternatives. We will discuss the best Google Colab alternatives to help you with your data science lifecycle, including data mining, modeling, processing, and day-to-day tasks. For better decision-making, we’ll take you through crucial factors responsible for the general use cases of notebooks for individuals, data savvy business teams, educators, and researchers. Real-time collaboration: Choose a platform that offers a collaborative environment like screen-sharing Manageable environment: Look for a platform that makes the conda environment work on your computer simple Coding embeds: Make sure the alternatives to Google Colab allow you to create and embed code blocks in a single place Data visualization: Look for easy no-code options that are getting more attention from data experts when it comes to exploring data while building data apps

In today's digital age, the rapid proliferation of information through online platforms has revolutionized how we consume news and engage with current events. However, this unprecedented accessibility to information has also given rise to a concerning phenomenon—fake news. Fake news, characterized by false or misleading information presented as factual news, has become a pervasive issue with far-reaching consequences. Its impact spans from influencing public opinion and elections to inciting social discord and undermining trust in credible sources. The approach for this project involves a fusion of TF-IDF methodology with LSTM (Long Short-Term Memory) and Bidirectional LSTM (BiLSTM) models for the detection of fake news. Initially, the FakeNewsNet and ISOT datasets are merged to create a comprehensive corpus of labeled news content. Following this, the text data undergoes preprocessing, including cleaning, tokenization, and normalization, ensuring uniformity across articles. The key pivot lies in the TF-IDF representation, where the text data is transformed into TF-IDF vectors to highlight the importance of specific terms within each article, emphasizing crucial words in the context of individual documents. Finally, the model is trained and evaluated, showcasing the potential of this approach in combating the spread of fake news. This project was a significant step towards understanding and mitigating the impact of fake news. I am excited to share more details and insights. Check out the complete project on GitHub: https://lnkd.in/d2rqcijM. #FakeNewsDetection #MachineLearning #DataScience #AI #LSTM #BiLSTM #TFIDF #Python #DeepLearning #TechForGood #Innovation

A great read to learn more about how Hugging Face built their pretraining large-scale dataset for Large Language Models 📚 While observing the ⚔ Arena Leaderboard (https://lnkd.in/egfspfDU) for the best-ranked LLMs, we often overlook the impact of the pre-training datasets that significantly affect their performance. Hugging Face recently released 🍷 FineWeb dataset accompanied by a great blog post (https://lnkd.in/eVqfhunP) where they explain everything regarding their large-scale dataset design choices and implementations: a 275GB dataset with cleaned and deduplicated data under an Open Data Commons license. Data source selection and preparation are often opaque because of their commercial advantages and legal implications (copyrights, privacy). This blog post helped us understand the challenges of data engineering at scale! 📖 My takeaways: • Crawling the web yields approximately 200TiB/month of textual content. • Filter and deduplication with their custom library datatrove (https://lnkd.in/e-AvWXQR). • Use "early-signal" benchmark tasks to identify and retain the best data by testing its impact in intermediate steps with model evaluation. • Text extraction is one of the most costly steps of processing. • Deduplication using MinHash, which scales efficiently; configure the similarity thresholds to avoid having documents that are 75% similar. • Applying some filters to the C4 dataset to remove useless data helped—for example, the terminal punctuation filter gives the biggest individual boost but removes around 30% of all tokens. • Use llama-3-70b-instruct to annotate 500k samples from FineWeb, scoring each on a scale from 0 to 5 for its educational quality. • English still dominates the LLM landscape by being the number 1 choice of a pre-trained dataset, but this process could be applied to any language. Building a pre-trained large-scale dataset reminds me of the fundamentals of data engineering: filter, duplicate, tag, and use metadata while scaling infrastructure. I hope to find more open and transparent ways of working in this area in the future 🚀 #genai #llm #data #dataengineering