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Data Science News
Medium
1w
152
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The History of AI
- AI originated from ancient myths and stories, but took shape in the 20th century.
- Alan Turing posed the question of whether machines can think, leading to the creation of the Turing Test.
- AI saw progress in the 1960s and 1970s, but faced setbacks in the AI Winter of the 1970s and 1980s.
- Expert systems and the rise of machine learning revived AI in the 1980s and 2000s respectively.
- AI gained prominence in the 2010s with advancements in big data and cloud computing.
- The future of AI involves its pervasive presence and challenges in ethics and regulation.
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Medium
1w
101
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Build a Real-Time Stock Price Prediction App with Flask, Yahoo Finance & Machine Learning
- This article discusses how to build a real-time stock price prediction app using Flask, Yahoo Finance, and machine learning.
- Flask is a lightweight and flexible Python web framework, ideal for building a stock prediction API.
- The app fetches live stock data from Yahoo Finance using the yfinance Python package.
- A machine learning model is trained on the data and used to make real-time stock price predictions.
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Medium
1w
22
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Now change the color of your choice, WhatsApp introduces a new feature
- WhatsApp has introduced a new feature to change the main theme colors of the business app.
- Users can now customize the traditional green color of WhatsApp to a color of their choice.
- The light theme color has been changed to black, while the dark theme is now white.
- WhatsApp Business adopted black and white colors for its light and dark themes, while WhatsApp Messenger retains its green color.
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Medium
1w
119
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Banter: Physics-Fueled Games With Friends
- Banter, a free-to-play Social VR app, has undergone a major update.
- The update features an advanced physics engine and movement system called FlexaPhysics™️.
- Banter offers a variety of games, events, and customizable avatars.
- The app aims to provide a space for social interactions and immersive VR experiences.
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Dev
1w
360
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Binary Search: Binary Search, Efficient Algorithms, Advanced Applications
- Binary search is a search algorithm that operates on a sorted array or list. It uses the divide-and-conquer approach to repeatedly divide the search interval in half until the target value is found or determined to be not in the array.
- Binary search requires the array to be sorted. With a time complexity of O(log n), it's much faster than linear search for large datasets.
- Binary search starts with two pointers, low and high, to represent the bounds of the search interval. The middle index is calculated with (low+high)//2, and comparison is made against the middle element.
- Binary search can be implemented both iteratively and recursively. The base case for the recursive implementation is low>high indicating that the target is not in the array.
- The time complexity of binary search, whether iterative or recursive, is O(log n), which makes binary search suitable for large sorted datasets.
- Practical considerations include ensuring the array is sorted and using the non-recursive approach to avoid overhead of recursive function calls.
- Applications of binary search go beyond simply finding an element in an array. Some of the key applications include finding lower and upper bounds, searching in infinite lists, and solving optimization problems.
- Finding the lower bound of a value in a sorted array is finding the index of the first element that is not less than the target value.
- Binary search is a versatile algorithm with numerous applications beyond simply finding an element in an array.
- Binary search is a widely used and efficient algorithm that is simple to implement and guarantees deterministic results when used correctly.
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Medium
1w
318
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Unlocking the Power of Data with XnY: Empowering the Open Data Economy.
- XnY bridges the gap between Data Creators and Data Demanders through blockchain technology.
- XDAT tokens enable data assetization, creating a decentralized marketplace for data monetization.
- Blockchain ensures data ownership, traceability, and validation for trust and authenticity.
- XnY fosters an open data ecosystem, empowering developers, researchers, and enterprises.
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Medium
1w
16
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Minimalist Design Meets Bold Vision: The XnY Inspiration.
- XnY's website offers a sleek and interactive experience, showcasing decentralized data management and innovation.
- Key features include interactive storytelling, iconic datasets, and a future-focused design.
- The ecosystem integrates X-Data, Y-Data, and Frontier Data for collaboration and progress.
- XnY's mission is to foster an equitable ecosystem where data is a catalyst for innovation.
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Medium
1w
38
5G Networks: A Revolution That Will Change Connectivity Beyond
- 5G networks are the fifth generation of wireless communication networks.
- The key features of 5G networks include ultra-fast speeds and low latency.
- 5G can achieve download rates of up to 10 Gbps, allowing for seamless streaming and fast file downloads.
- With low latency of as low as 1 millisecond, 5G enables real-time communication for applications like autonomous vehicles and AR.
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Feedspot
1w
23
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Top 13 AI Conferences to Attend in 2025
- Attending AI conferences is one of the best ways to gain insights into the latest trends, network with industry leaders, and enhance your skills.
- The World Summit AI, scheduled for October 15-16, 2025, in Amsterdam, is a leading global event that gathers AI innovators and industry experts.
- Held in London on June 10-11, 2025, the Generative AI Summit focuses on the future of AI, showcasing innovations in generative models and machine learning.
- The AI & Big Data Expo Global, taking place on November 25-26, 2025, in London, is a major event for AI and big data professionals.
- Scheduled for May 7-8, 2025, in Berlin, the Rise of AI Conference is a key European event that explores AI advancements, ethics, and industry applications.
- In London, the Gartner Digital Workplace Summit is set for October 20-21, 2025.
- AI Expo Asia, happening on September 15-16, 2025, in Singapore, focuses on AI applications in business.
- The AI in Healthcare Summit in Boston is scheduled for April 22-23, 2025.
- Organized by the United Nations, the AI for Good Global Summit in Geneva is set for June 3-4, 2025.
- NeurIPS in Vancouver, scheduled for December 7-12, 2025, is a premier AI research conference.
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Feedspot
1w
183
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What is Overparameterization in LLMs? From Overfitting Myths to Power Laws!
- Overparameterization is a strategy that allows LLMs to become flexible learners of human language with billions of parameters.
- The concept involves adding more parameters than necessary to a neural network like LLM to fit the training data and represent complex patterns within the data.
- One of the primary challenges of overparameterization is the significant computational resources required for training and inference.
- Another challenge is that overparameterization may lead to overfitting, where the model memorizes the training data instead of learning to generalize from it.
- Understanding the relationship between the model size, data, and compute resources is essential for the effectiveness of LLMs and needs proper attention.
- Overparameterization myths include: overparameterization always leads to overfitting, more parameters always harm generalization, and overparameterization is unnecessary.
- Implications of overparameterization include capturing complex patterns in data, flexible learning, and smoother loss landscapes and better convergence in optimization.
- Overparameterized LLMs can transform various sectors by leveraging their advanced capabilities, such as few-shot and zero-shot learning.
- Efficient and sustainable LLMs are essential, and theoretical insights into overparameterization could lead to significant breakthroughs in developing the models.
- The future of LLMs demands innovations aimed at balancing overparameterization with efficiency and addressing open questions will be vital in shaping the future landscape of AI.
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Feedspot
1w
301
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How To Make an LSTM Model with Multiple Inputs?
- LSTM models are used for processing sequential data.
- To enhance the performance of LSTM models, multiple inputs can be added.
- LSTM model is designed to learn from patterns within sequential data.
- The multiple inputs are added as a part of a time-step sequence.
- The S&P 500 dataset can be used to create an LSTM model with multiple inputs.
- Multiple inputs help in capturing price swings, market volatility, and offer increased data granularity.
- LSTM models require input in the form [samples, time steps, features].
- The attention mechanism helps the LSTM model focus on the most important parts of a sequence.
- The integration of the attention layer into the LSTM model aids the improved ability to predict trends.
- The LSTM model can be trained using parameters like epochs, batch size, and validation data.
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Feedspot
1w
204
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Top 23 Data Science Conferences to Attend in 2025
- Attending data science conferences provide a unique platform for professionals to gain insights into the latest trends, technologies, and best practices.
- Here are some of the top data science conferences to attend in 2025:
- The AI & Big Data Expo – UK
- Chief Data and Analytics Officer (CDAO) – UK
- Gartner Data & Analytics Summit – USA
- Big Data & AI World – UK
- Google Cloud Next – USA
- The Open Data Science Conference (ODSC) East/West – USA/Europe
- European Data Innovation Summit – Stockholm, Sweden
- ODSC East – USA
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Feedspot
1w
260
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Streaming Langchain: Real-time Data Processing with AI
- Langchain is an AI and natural language processing (NLP) framework that simplifies the development of advanced, real-time AI systems that react instantly to user input and real-time data.
- Streaming enables developers to build applications that react dynamically to ever-changing inputs and can be used for live data such as real-time queries from users, sensor data, financial market movements, or even continuous social media posts.
- Traditional batch processing workflows often introduce delays in response time, whereas streaming in Langchain allows for immediate data processing in real-time, ensuring applications are more interactive and efficient.
- Streaming drastically reduces the time it takes to process incoming data and allows AI models to adapt and evolve as new data becomes available. This is especially useful for predictive analytics systems and recommendation engines.
- Langchain’s streaming functionality is well-suited for applications that need to scale and handle large volumes of data in real-time. Streaming LangChain ensures scalable performance, handling large data volumes and concurrent interactions efficiently.
- Setting up streaming in Langchain is straightforward and designed to seamlessly integrate real-time data processing into your AI models. Langchain provides two main APIs supported by any component which implements the Runnable Interface.
- While Langchain’s streaming capabilities offer powerful features, it’s essential to be aware of a few challenges when implementing real-time data processing. Streaming real-time data can place significant demands on system resources, whereas it can introduce latency and data interruptions which can affect application stability.
- Streaming with Langchain opens exciting new possibilities for building dynamic, real-time AI applications that are more responsive and adaptive. Langchain’s streaming capabilities empower developers to build more intelligent applications that can evolve as they interact with users or other data sources.
- As Langchain continues to evolve, we can expect even more robust tools to handle streaming data efficiently. Future updates may include advanced integrations with various streaming services, enhanced memory management, and better scalability for large-scale, high-performance applications.
- Developers who are ready to explore the world of real-time data processing and leverage Langchain’s streaming power can dive in and start creating highly responsive, innovative AI solutions.
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Feedspot
1w
47
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Discrete vs Continuous Data Distributions: Which One to Use?
- Understanding data distributions is crucial for better data analysis and making informed decisions.
- Discrete vs continuous data distribution plays a key role in understanding the behavior of data and how to analyze it.
- Data distribution describes how points in a dataset are spread across different values or ranges, and mapping data points provides a clear picture of the data’s behavior.
- Discrete data consists of distinct, separate values that are countable and finite, while continuous data consists of values that can take on any number within a given range.
- Common examples of discrete data distributions include binomial, geometric, and Poisson, while continuous data distributions include normal, exponential, and Weibull.
- Discrete data is best represented using bar charts or histograms, while continuous data is best represented using line graphs, frequency polygons, or density plots.
- Understanding the type of data distribution is crucial for selecting the right statistical tests and tools, which can lead to more accurate predictions and better models.
- Data types have practical applications in various business areas, such as customer behavior analysis, marketing campaigns, and financial forecasting.
- Knowing your data type and distribution is the foundation for accurate analysis, effective decision-making, and successful business strategies.
- By mastering discrete and continuous data distributions, you can choose the right methods to uncover meaningful insights and make data-driven decisions with confidence.
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Feedspot
1w
391
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Simplifying API Interactions with LangChain’s Requests Toolkit and ReAct Agents
- With LangChain, a Requests Toolkit, and a ReAct agent, talking to your API with natural language is easier than ever.
- LangChain is a community-developed Requests Toolkit which enables one to converse with APIs using natural language.
- To interact with an API via LangChain, you need to obtain the OpenAPI specification for that API which will provide details about the available endpoints, request methods and data formats.
- We first import the relevant LangChain classes and select the HTTP tools from the requests toolkit, one for each of the 5 HTTP requests that we can make to a RESTful API.
- A ReAct agent is a specialized tool provided in LangChain which combines cognition and action and generates responses from natural language inputs.
- Once the ReAct agent is configured, it can be invoked to perform API requests, and the results can be stored and used as required.
- Using LangChain’s Requests toolkit to execute API requests with natural language opens up new possibilities for interacting with data.
- LangChain implementation involving the Requests toolkit and a ReAct agent is effective, reliable, and flexible to integrate natural language processing for interacting with APIs.
- There are other approaches for NLP based API communication, like Dialogflow, but Requests Toolkit making use of LangGraph-based ReAct agents seems to be the most feasible approach.
- This functionality has already been tested with a variety of APIs including Slack, ClinicalTrials.gov, and TMDB with impressive results.
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