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Data Science News
Medium
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177
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The Power of Proximity(KNN Algorithm
- The kNN algorithm can be compared to someone looking for the best neighborhood based on their preferences of neighborhood features just like similar features are compared in data points in kNN.
- Each neighborhood is compared based on the features it has, which could be the average rent cost, community vibe or even distance to local schools.
- To find the best neighborhood, based on the individual's preferences, the distance between their preferences and a neighborhood's features is calculated.
- The Majority Voting process in the k-Nearest Neighbors (kNN) algorithm is important for making predictions, especially in classification tasks.
- The kNN algorithm can be computationally intensive in its basic form, so several variants and extensions have been developed to deal with large datasets and high-dimensional feature spaces.
- kNN is used in recommendation systems to suggest products to customers that are similar to what they have liked before. It is also used in medical fields for predicting diseases in patients, credit rating prediction and image recognition tasks.
- kNN works well in complex and subtle data patterns analysis such as medical diagnosis, where making predictions is based on a clear and understandable reasoning process.
- In recommendation systems, the algorithm can quickly identify the most similar items or users and make recommendations accordingly.
- In image recognition tasks, the algorithm can classify images by comparing pixel values, taking advantage of its ability to handle multi-class cases and work well with little pre-processing of image data.
- The kNN algorithm has played a significant role in the evolution of AI and has been useful in finding the nearest neighbors that match specific criteria, making it an excellent tool for data analysis and prediction.
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Medium
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143
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From Words to Wisdom
- Text data is unstructured and requires specialized techniques in data science to extract useful information.
- Text preprocessing is a critical step in making raw text data ready for analysis.
- Stemming algorithms and lemmatization are two methods of text preprocessing, with lemmatization being more precise.
- The Bag of Words (BoW) model and Term Frequency-Inverse Document Frequency (TF-IDF) are foundational techniques used in text analysis and natural language processing.
- BoW treats text as a mere collection of words, ignoring the grammar and the order in which words appear.
- TF-IDF is a statistical measure used to evaluate how important a word is to a document in a collection or corpus.
- BoW and TF-IDF have limitations in handling synonyms and polysemy.
- In handling large amounts of text data, BoW can result in high-dimensional and sparse data.
- Without additional processing like stop-word removal or term weighting, BoW models may be biased towards frequent, less informative words.
- Service industry companies like hotels or airlines collect vast amounts of text data to enhance customer satisfaction, improve services, and tailor marketing strategies.
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Medium
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154
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The Art of Data Transformation
- Data transformation is essential in data science to make raw data useful for analysis, enhancing data quality, and facilitating integration from multiple sources. They enhance the performance and accuracy of statistical models and algorithms, facilitate meaningful data comparison, and ensure consistency across different data sets. Techniques like tokenization, stemming, and lemmatization are used to reduce the number of unique words the model has to handle, thereby focusing on the essence rather than the form of the word in text data transformations. In numerical data processing, transformations can reduce the effects of skewness and outliers leading to improvements in model accuracy and robustness. Transforming categorical data into numerical formats allows machine learning models to process and learn from the data, and proper encoding of categorical variables impacts the model's performance.
- Transformations can be used to enhance or isolate certain features within an image that are important for a specific analysis. Before feeding images into a model, it is often necessary to preprocess them to make them suitable for analysis. Techniques like Bag of Words, Term Frequency-Inverse Document Frequency, and word embeddings not only convert text into numerical values but also help in reducing the dimensionality so that the model can be trained using less computational power. Normalizing an image’s intensity values can reduce the effect of lighting variations and improve the consistency of input data, which is particularly important for achieving high performance in many image processing and machine learning applications.
- Categorical data transformations are important in machine learning because many models and algorithms cannot handle categorical data directly. These algorithms require numerical inputs, making it necessary to transform categorical variables into numerical formats. One-Hot Encoding creates a new binary column for each category of the variable, while Label Encoding assigns an integer to categorical data based on an explicit ordering. Replacing categories with their frequencies and values derived from the average value of the target variable for that category are useful when the frequency of categories is an essential characteristic for the model.
- Text data transformations, such as converting text into numerical formats like vectors, allow algorithms to perform statistical analysis, find patterns, and make predictions. Transformations such as lowercasing all letters, removing punctuation and standardizing terms ensure consistency across the dataset, which reduces complexity and improves the model’s performance.
- Transforming data to be more normally distributed or linearizing relationships between variables, can improve the effectiveness and predictiveness of statistical methods and machine learning algorithms. Many algorithms perform better when numerical input variables are on a similar scale, and transformations can be used to scale them. Translations can also be used to reduce the effects of skewness and outliers, leading to improvements in model accuracy and robustness in numerical data processing.
- Data augmentation using image transformations is essential for good performance while training deep learning models and for proper model training. Techniques like shifts, flips, rotations, and color changes increase the diversity of the dataset. Transformations can be utilized to enhance or isolate specific features within an image that are essential for a particular analysis. Random brightness and contrast adjustments, color separations, scaling pixel values, feature scaling, selective color channel usage, and the addition of random noise are examples of image transformations.
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Medium
1d
170
Updates on Federated Learning part2(AI 2024)
- Federated learning (FL) is a privacy-preserving machine learning approach.
- Recently, gradient inversion attacks have been recognized as a privacy risk in FL.
- A novel Gradient Inversion attack based on the Style Migration Network (GI-SMN) is proposed.
- GI-SMN outperforms state-of-the-art gradient inversion attacks and can overcome certain defenses.
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Medium
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222
Updates on Federated Learning part1(AI 2024)
- Deep learning has shown incredible potential across various tasks, but accessing data stored on personal devices poses privacy challenges.
- Federated learning (FL) has emerged as a privacy-preserving technology that enables collaborative training of machine learning models without sending raw data to a central server.
- This survey paper provides a literature review of privacy attacks and defense methods in FL, identifies limitations, and discusses successful industry applications.
- The paper also explores the efficacy of a hybrid federated-continual learning paradigm for robust web phishing detection, achieving high accuracy and outperforming traditional approaches.
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Medium
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98
How Iterative Magnitude Pruning works part4(AI 2024)
- Sparse shrunk additive models and sparse random feature models have been developed separately as methods to learn low-order functions, where there are few interactions between variables.
- Inspired by the success of the iterative magnitude pruning technique in finding lottery tickets of neural networks, a new method called Sparser Random Feature Models via IMP (ShRIMP) is proposed to efficiently fit high-dimensional data with sparse variable dependencies.
- ShRIMP combines the process of constructing and finding sparse lottery tickets for two-layer dense networks.
- Experimental results show that ShRIMP achieves better or comparable test accuracy compared to other sparse feature and additive methods, while offering feature selection with low computational complexity.
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Medium
1d
64
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What is Bitcoin Mining?
- Bitcoin mining is the process by which new bitcoins are minted. It is analogous to the process of gold mining, with the expenditure of CPU time and electricity in place of the manual labor and tools. Miners use specialized hardware to solve complex mathematical problems in exchange for new bitcoins. Bitcoin mining ensures that the network remains honest and protects it from attacks. Bitcoin mining is also a highly competitive industry, with slim profit margins.
- Bitcoin mining is critical to enabling people to securely make Bitcoin transactions. To understand why, let’s look in more detail at how Bitcoin works. The Bitcoin network is a globally distributed public ledger consisting of a giant list of timestamped transactions. Miners are the ones who propose updates to the ledger and only miners who have successfully completed the Proof-of-Work (PoW) are permitted to add a new block. Miners are free to select valid transactions from a pool of potential transactions that are broadcast to the network by nodes.
- Winning the right to create a new block is settled through a competition known as “Proof-of-Work.” Proof-of-Work (PoW) mining is a way to mathematically prove that a network participant has skin in the game. It works by forcing participants to prove that they have completed some arbitrary calculations that consume energy (work)
- Most nodes simply store the history of the ledger, validate the authenticity of new transactions according to the rules of the protocol, and pass on new blocks of transactions to other nodes. In this way, the state of the network propagates around the world until all nodes have the same information. At that point, there is a new ‘truth’ about who owns what
- Note that a block which doesn’t end up becoming part of the longest chain (version B) is known as an orphan block. It is estimated that such blocks are created between 1 and 3 times per day. Transactions that are included in an orphan block are not lost as they will end up being added to the next block of the longest chain
- Bitcoin mining is a naturally equilibrating system. As the price of Bitcoin rises, miner margins increase. This entices more miners to join the market. However, new entrants cause the difficulty of minting new blocks to increase. Sustained downturns in the price of Bitcoin have historically resulted in a portion of miners quitting due to costs exceeding revenue
- Bitcoin mining is a highly competitive industry with narrow profit margins. The primary input is electricity, although significant upfront investments in hardware and facilities are also required. The key hardware involved is the Application Specific Integrated Circuit (ASIC), which is a computing device specialized for running the Bitcoin hashing algorithm exclusively. Profitably relies mainly on consistent access to low-cost electricity applied to the most efficient ASIC hardware
- Bitcoin miners are awarded BTC when they find a random number that can only be generated by running the hashing algorithm over and over again. Bitcoin mining is legal in most countries, including the U.S. and Europe. In some regions, local regulators have imposed or moved to impose restrictions on Bitcoin mining due to its negative impact on electricity grids or the environment
- Miners sell a significant portion of their earned bitcoins to cover the costs associated with mining. These costs contribute to the net sell pressure. Miner's attempts to maximize profitability by holding or selling Bitcoin based on market momentum may have an impact on Bitcoin’s price volatility. Bitcoin’s energy use is certainly a concern, it is a multifaceted issue that needs a nuanced examination
- Bitcoin’s environmental impact is also a concern, which needs to be weighed against the potential benefits, which in Bitcoin’s case include reducing international remittance fees, financial inclusivity, and the creation of economic freedom
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Medium
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280
Artificial Intelligence Automations Explained
- Artificial intelligence automations mimic human cognitive functions and can revolutionize industries by streamlining processes, improving accuracy, and driving innovation.
- Artificial intelligence automations consist of three key components: perception, learning, and decision-making.
- Benefits of artificial intelligence automations include enhanced productivity, cost reduction, and improved customer experience.
- Real-world applications of AI automations span healthcare, manufacturing, finance, retail, transportation, and education.
- Challenges of AI automations include ethical considerations, biases in algorithms, and technological constraints.
- Future trends of AI automations include enhanced machine learning algorithms, increased integration with other technologies, and personalized user experiences.
- Successful implementation of AI automations requires careful planning, data management, workforce impact analysis, and effective change management.
- Ethical considerations when using AI automations encompass privacy, fairness, and accountability.
- The impact of AI automations on jobs requires organizations to adapt and reskill their workforce and recognize the collaborative nature of AI with human expertise.
- Barriers to adoption of AI automations include resistance to change, lack of expertise, and concerns about job displacement, which can be overcome by investing in training and upskilling programs.
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Medium
1d
246
Title: Unlocking Success with "Klick-Tipp": A Comprehensive Guide
- Klick-Tipp is a comprehensive toolset for streamlined communication with your audience, beyond email marketing.
- Key features include segmentation and personalization, automation, responsive design, and analytics and tracking.
- Practical applications include lead nurturing, customer onboarding, and e-commerce.
- Getting started with Klick-Tipp is user-friendly, with ample resources and support available.
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Medium
1d
34
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Joyce’s picks: musings and readings in AI/ML, May 6, 2024
- Google DeepMind released Med-Gemini, a family of multimodal models fine-tuned and specialized for medicine.
- Amazon Q, a generative AI assistant for business and developers, has launched.
- Reinforcement Learning Problem Solving with Large Language Models (technical paper)
- An AI-controlled fighter jet took the Air Force leader for a historic ride. What that means for war (Politico)
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Medium
1d
30
Welcome to TON MOLE
- TONMole is a platform harnessing the transformative power of NFTs and cryptocurrency to make a real-world impact.
- Joining TONMole means becoming part of a community committed to positive change and achieving goals together.
- TONDIGGER is dedicated to unlocking the potentials of the digital world and viewing NFTs as seeds for a brighter future.
- TON MOLE offers an exciting opportunity to redefine community and innovation in the cryptocurrency space.
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Medium
1d
311
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Modulo Cropping Operation
- Modulo Cropping is a rule used to organize crayons neatly based on division and remainder.
- It helps in keeping things within a specific range and is used in various applications such as looping, array indexing, and cryptography.
- In programming, it is often used to repeat actions at regular intervals or cycle through an array in a circular manner.
- A simple example in Python demonstrates the Modulo Operation and outputs the remainder result.
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Towards Data Science
1d
64
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Demo AI Products Like a Pro
- Data scientists often struggle to present AI products to audiences effectively and overcome technical difficulties.
- Gradio is a framework used to demonstrate machine learning/AI models and it integrates with the Hugging Face ecosystem.
- Gradio demos resolve common live demo problems, including engaging audiences, controlling the user experience and error-free product presentation.
- The application of Gradio can be extended to StreamLit with Python or Shiny with R.
- Blocks are the building blocks of Gradio applications used to gain more control over the demo display and add new tabs to control user flows.
- Gradio demos can be shared publicly with prospective customers using free platform Spaces, which provide permanent links but have costs attached to GPU instances ranging from $0.40 to $5 per hour.
- Custom components have been developed by other data scientists and developers as extensions on top of the Gradio framework.
- Gradio is useful for demonstrating machine learning models and AI. In the example, a linear regression model is used with the California House Prices dataset.
- Markdown can be utilized as the component to present information in text on Gradio.
- Gradio helps control the user experience and provides an easier way to engage audiences and control user flows.
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Medium
1d
266
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Turning $100M Of Bitcoin Ethereum into $1B Using Grid-Bots-75% Daily For 75Days.
- A blogger has developed an ambitious strategy that he believes will make over $1bn before mid-2025 by reducing the risks of liquidation using grid bots. The blogger claims an estimated ROI of 75% daily over 75 days using the strategy, despite suffering a negative ROI in recent weeks. The current capital invested is $105m over all investment opportunities, with the blogger using Bitcoin OTC desk, Hi-Table, for sell volumes of bitcoin. The strategy sees the accumulation of BTC and ETH as the market recovers upwards to increase overall USDT.
- The blogger set up his Bitcoin Grid Bot on February 14 with an initial investment of $70m followed by an additional $30m on an Ethereum Grid Bot for a total initial capital investment of $100m. The blogger is currently holding a total capital of $5m in USDT, 78.20 BTC and 7 ETH with a market price of $62,312 and $3,171 respectively. Although the ROI in the last few weeks has been negative, the blogger has accumulated more BTC and ETH in comparison to investing $70m in BTC without a grid bot.
- The blogger plans further adjustments to adapt to the market situation. The current economic downturn in risky assets calls for preparations for either a Bitcoin market ranging or going down to the $52,000 region. In the case of a market range, no adjustments are required while the blogger has adjusted the stop loss to $58,826 for Bitcoin, and to $2,699 for Ethereum. If the market goes below this mark, the blogger will move the capital to Hi-Table to use grid bot on HiCOIN-Mining.
- This allows the blogger to trade an asset with itself instead of pairing it with USD, especially in the case of a Bitcoin price reduction. The blogger aims to acquire more BTC instead of reselling BTC at a lower price by converting it into USDT. However, the blogger may terminate the grid bots early and re-initiate them with a tighter range, as $90,000 is more unlikely in the short term. As long as the COIN-M mechanism sustains, the blogger is interested in it while in a bull market.
- The blogger emphasizes the importance of taking profits along the way and not overinvesting during a bull market. The blogger expects to publish an updated results blog post once a month to determine if they are on track to reach their goals. However, they also believe things may grow faster than expected later in the cycle. The blogger's hypothetical ROI is impressive, but they advise caution in taking profits along the way and staying prepared for uncertainty.
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Towards Data Science
1d
129
Image Credit: Towards Data Science
Text to Knowledge Graph Made Easy with Graph Maker
- This article is a sequel to the article I wrote a few months ago about how to convert any text into a Graph.
- Recently, the aforementioned article was attributed in a paper published by Prof Markus J. Buehler at MIT.
- The article received an overwhelming response with more than 80K readers on the medium and more than 180 Forks and 900 Stars on the GitHub repository shared in the article.
- The article is about making text to knowledge graph very easy with the help of Graph Maker, a new python package.
- The article discusses various challenges encountered, and observations received while extracting graphs with LLMs, which can subsume traditional methods.
- The package enables the creation of large knowledge graphs with as large a corpus of text as desired, using the LLM through prompting and chunking.
- The metadata assists in adding context with every extracted relation from every document, which helps to contextualize relationships across multiple documents.
- An example Python notebook is included in the repository to get started quickly, and the code can be taken for a spin.
- The Graph Maker is primarily useful for RAG applications and can be leveraged by mix Cypher queries and Network algorithms with Semantic Search.
- The article ends by inviting readers to share their use cases and contributions to this open source project, which the author developed for a few of his pet projects.
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