⚡️A Scalable Dataset for Tuning Instructions in Software Mathematical Reasoning

The Mathematical Reasoning pipeline emphasizes separating numbers from mathematical problems to synthesize number-independent programs, enabling efficient and flexible scaling while minimizing dependence on specific numerical values.

As the authors note in their paper, experiments in fine-tuning open-source language and code models such as Llama2 and CodeLlama demonstrate the practical benefits of the InfinityMATH dataset.

In addition, these models have shown high reliability on the GSM8K+ and MATH+ benchmarks, which are improved versions of the benchmarks with minor changes to the numerical values.

📊Dataset
📖Research paper

🧐What is the difference between DICOM and NIfTI medical image formats

Before we look at the differences between DICOM and NIfTI, let's take a closer look at what each of these formats is individually

🤔What is the DICOM standard?

The DICOM standard — Digital Imaging and Communications in Medicine (DICOM) — is used to exchange images and information, it has been popular for more than a decade. Today, almost every device used in radiology (including CT, MRI, ultrasound and radiography) is equipped with support for the DICOM standard. According to the information from the standard developer (), DICOM allows you to transfer medical images in an environment of devices from different manufacturers and simplify the development and expansion of image archiving and communication systems.

🤔What is the NIfTI standard?

The Neuroimaging Informatics Technology Initiative (NIfTI) was created to work with users and manufacturers of medical devices to address some of the problems and shortcomings of other imaging standards. NIfTI was specifically designed to address these issues in the field of neuroimaging, with a focus on functional magnetic resonance imaging (fMRI). According to the NIfTI definition, the primary mission of NIfTI is to provide coordinated, targeted services, education, and research to accelerate the development and usability of neuroimaging informatics tools. NIfTI consists of two standards, NIfTI-1 and NIfTI-2, the latter being a 64-bit enhancement of the former. It does not replace NIfTI-1, but is used in parallel and supported by a wide range of medical neuroimaging devices and operating systems.

❓What is the difference between DICOM and NIfTI?

1. NIfTI files have less metadata: An NIfTI file does not require as many tags to be filled in as a DICOM image file. There is much less metadata to inspect and analyze, but this is in some ways a disadvantage because DICOM provides users with different layers of image and patient data.

2. DICOM files are often bulkier: DICOM data transfer is governed by strict formatting rules that ensure that the receiving device supports SOP classes and transfer syntaxes, such as the file format and encryption used to transfer the data. When transferring DICOM files, one device talks to another. If one device cannot process the information that the other is trying to send, it will inform the requesting device so that the sender can roll back to a different object (e.g. a previous version) or send the information to a different receiving end. Therefore, NIfTI files are usually easier and faster to process, transfer, read, and write than DICOM image files.

3. DICOM works with 2D layers, while NIfTI can display 3D details: NIfTI files store images and other data in a 3D format. It is specifically designed to overcome the spatial orientation issues of other medical imaging file formats. DICOM image files and associated data are made up of 2D layers. This allows for viewing different sections of an image, which is especially useful when analyzing the human body and different organs. However, with NIfTI, neurosurgeons can quickly identify objects in images in 3D, such as the right and left hemispheres of the brain. This is invaluable when analyzing images of the human brain, which is extremely difficult to evaluate and annotate.

4. DICOM files can store more information: As mentioned above, DICOM files allow medical professionals to store more information in different layers. Structured reports can be created and even images can be frozen so that other clinicians and data scientists can clearly see what the opinion/recommendation is based on.

🌎TOP DS-events all over the world in September
Sep 2-4 - CDAO Melbourne - Melbourne, Australia - https://cdao-mel.coriniumintelligence.com/
Sep 6-7 - Big Data Conference 2024 - Harvard, USA - https://cmsa.fas.harvard.edu/event/bigdata_2024/
Sep 7 - Platzi Conf 2024 - Bogota, Colombia - https://platzi.com/conf/
Sep 9-11 - ECDA 2024 - Sopot, Poland - https://www.ecda2024.pl/
Sep 10-11 - Civo Navigate Europe 2024 - Berlin, Germany - https://www.civo.com/navigate/europe
Sep 11-13 - RTC.ON 2024 - Krakow, Poland - https://rtcon.live/
Sep 18-19 - THE UK’S LEADING DATA, ANALYTICS & AI EVENT - London, UK - https://www.bigdataldn.com/
Sep 23-25 - Data Makers Fest - Alfândega do Porto, Portugal - https://www.datamakersfest.com/
Sep 24 - hayaData 2024 - Tel Aviv, Israel - https://www.haya-data.com/
Sep 24 - APAC Data 2030 Summit 2024 - Singapore, Singapore - https://apac.data2030summit.com/
Sep 24-26 - ICBDE 2024 - London, UK - https://www.icbde.org/
Sep 25-26 - BIG DATA & ANALYTICS MONTRÉAL SUMMIT 2024 - Montreal, Canada - https://bigdatamontreal.ca/
Sep 26-27 - Big Data Conference 2024 - Smaland, Sweden - https://lnu.se/en/meet-linnaeus-university/current/events/2024/conferences/big-data-2024---26-27-sep/
Sep 28-30 - GovAI Summit 2024 - Arlington, United States - https://www.govaisummit.com/
Sep 30-Oct 2 - Ray Summit 2024 - San Francisco, United States - https://raysummit.anyscale.com/flow/anyscale/raysummit2024/landing/page/eventsite

⚠️Text2SQL is no longer enough

I recently came across an article in which the authors describe in detail the innovative TAG approach.

Table Augmented Generation (TAG) is a unified general-purpose paradigm for answering natural language questions using databases. The essence of this approach is that we have a model that accepts a natural language query, processes it, and returns a natural language answer.

Thus, Text2SQL only represents the spectrum of interactions between LM and the database. The very essence of these interactions is described using TAG.

📚 Article with a detailed denoscription
🛠 Implementation of the approach

😎Universal database with embeddings

✅txtai is a universal database of embeddings designed for semantic search, orchestration of large language models (LLM), and management of machine learning workflows. This platform allows you to efficiently process and extract information, use semantic search for text search, and organize and automate tasks related to training and applying machine learning models.

Key features of txtai:

— Includes vector search using SQL, object storage, graph analysis, and multimodal indexing
— Supports embeddings for various data types, including text, documents, audio, images, and videos
— Allows you to build pipelines based on language models to perform various tasks, such as generating suggestions for LLM, answering questions, labeling data, trannoscription, translation, summarization, and more

🖥 GitHub
🟡 Documentation

😎3 useful tools for working with SQL tables

SQL Fiddle - A tool for simple testing, debugging and sharing SQL fragments. Add text to the panel, and SQL Fiddle turns it into a noscript for creating the necessary table. Suitable for both working with databases and practicing SQL skills.

SQL Database Modeler - can create the structure of new tables and relationships between them, connect to existing databases and design changes to them. And all this in a nice graphical interface and with a link to GitHub.

SQLFlow - a simple tool for visualizing SQL queries and displaying dependencies. Allows you to track data lineage and transformations in data when executing queries.

🤔Conducting a data quality assessment at Airbnb

✅Airbnb is an online platform for posting and searching for short-term rentals of private housing around the world.

I recently came across an article, where the author describes the process of developing and implementing a data quality assessment methodology, as well as the principles, criteria, and parameters used for this assessment.

As the author notes, the assessment is based on the following principles:

1. Full coverage is an assessment method that can be applied to all data from an entire array, ensuring analysis and processing of information without omissions or limitations. This principle allows for a more complete and accurate study of data, covering the entire set, regardless of its volume or complexity.
2. Automation is a process in which the collection of input data required for the assessment is fully automated, without the need for manual intervention. This principle ensures high speed, accuracy and efficiency in collecting and processing data, which improves the quality of analysis and reduces the time for decision-making.
3. Actionable is a characteristic that means that the data quality assessment is easily accessible and understandable for both producers and consumers of data. This ensures transparency and ease of use of the assessment results, which contributes to more effective interaction and increased trust between all parties.
4. Multidimensionality is a property of the assessment that allows it to be decomposed into various basic components of data quality. This helps to analyze in detail individual aspects affecting the overall quality, such as accuracy, completeness, relevance and consistency, providing a deeper understanding and the ability to target improvement of each component.
5. Evolvability is a characteristic of the assessment, meaning that the criteria and their definitions can adapt and change over time. This flexible approach allows the assessment to remain relevant and effective in the face of changing requirements, new data and technological advances.

💡🤖😎10 AI Terms and Aspects That Everyone Needs to Understand and Be Aware of Today
🧐Today, we’ll look at 10 aspects that most broadly cover the field of AI in its various manifestations:
✅ Reasoning/Planning: Modern AI systems can solve problems by using patterns they’ve learned from historical data to understand the information, which is similar to the process of reasoning. The most advanced systems can go further, tackling more complex problems by creating plans and determining a sequence of actions to achieve a goal.
✅ Learning/Inference: There are two stages to creating and using an AI system: learning and inference. Learning can be compared to the process of educating an AI, where it’s given a set of data and it learns to perform tasks or make predictions based on that data.
Inference is the process by which an AI uses learned patterns and parameters to, for example, predict the price of a new home that will soon go on sale.
✅ Small Language Models (SLMs): Compact versions of Large Language Models (LLMs). Both of these types use machine learning techniques to recognize patterns and relationships, allowing them to generate realistic and natural language responses. However, unlike LLMs, which are huge and require a lot of computing power and memory, SLMs like Phi-3 are trained on smaller, curated datasets and have fewer parameters.
✅ Grounded: Generative AI systems can create stories, poems, jokes, and answer research questions. However, they sometimes have difficulty separating fact from fiction or use outdated data, leading to erroneous answers called “hallucinations.” Developers aim to make AI interactions with the real world more accurate through a process called grounding, where the model is connected to current data and specific examples to improve accuracy and produce more relevant results.
✅ Retrieval Augmented Generation (RAG): When developers give AI access to external data sources to make it more accurate and relevant, a technique called Retrieval Augmented Generation (RAG) is used. This approach saves time and resources by adding new knowledge without having to retrain the AI.
✅ Orchestration: AI programs perform many tasks when processing user requests, and an orchestration layer manages their actions in the right order to get the best response. The orchestration layer can also follow the RAG pattern, searching the web for fresh information and adding context.
✅ Memory: Modern AI models technically do not have memory. However, they may have orchestration instructions that help them “remember” information by performing specific steps with each interaction.
✅ Transformers and Diffusion Models: Humans have been training AI systems to understand and generate language for decades, but one of the breakthroughs that has accelerated progress is the Transformer model. Among generative AIs, Transformers are the ones that understand context and nuance the best and fastest.
Diffusion models are typically used to generate images. These models continue to make small adjustments until they create the desired output.
✅ Frontier Models: Frontier models are large-scale systems that push the boundaries of AI and can perform a wide range of tasks with new and advanced capabilities. They are becoming key tools for a variety of industries, including healthcare, finance, scientific research, and education.
✅ GPU: A graphics processing unit is a powerful computing unit. Initially created to improve the graphics in video games, they have now become the real “muscles” of the computing world. And since AI essentially deals with a huge number of computational problems in order to understand language and recognize images or sounds, GPUs are indispensable for AI both at the training stage and when working with finished models.

💡Creating recommendations for applications with minimal complexity using vector databases

This data not only trains AI systems, but is also the final output that you continue to work with. That's why it's so important to use "good" data. No matter how powerful the model is, if the input is bad data, the output will be the same.

This article is about an example of using the Weaviate database in Streamlit format to simplify working with vector databases. The authors believe that this will allow you to create a powerful search and recommendation system taking into account technical and cost factors.

📚For information, it is worth noting that:
✅Weaviate is an open-source vector database that allows users to store data objects and vector data from machine learning models and easily scales to billions of data objects. .

✅Streamlit is a Python framework. It contains a set of software tools that allow you to transfer a machine learning model to a website. The written "smart" program with this framework can be quickly turned into web applications.

📊Quick Tips for Handling Large Datasets in Google's Pandas

Pandas is a great tool for working with small datasets, typically between two and three gigabytes in size.

For datasets larger than this threshold, using Pandas is not recommended. This is because if the dataset size exceeds the available RAM, Pandas loads the entire dataset into memory before processing. Memory issues can arise even with smaller datasets, as preprocessing and rewriting create duplicate DataFrames.

⚠️Here are some tips for efficient data processing in Pandas:

✅ Use efficient data types: Use more memory-efficient data types (e.g. int32 instead of int64, float32 instead of float64) to reduce memory usage.
✅ Load less data: Use the use-cols parameter to load only the columns you need, reducing memory consumption.pd.read_csv()
✅ Chunking: Use the chunksize parameter in to read the dataset in smaller chunks, processing each chunk iteratively.pd.read_csv()
✅ Optimize Pandas dtypes: Use the astype method to convert columns to more memory-efficient types after loading the data, if appropriate.
✅ Parallelize Pandas with Dask: Use Dask, a parallel computing library, to scale Pandas workflows to larger-than-memory datasets by leveraging parallel processing.

🖥Learn more here

🧐💡A Brief Introduction to MapReduce: Advantages and Disadvantages

MapReduce is a programming model and associated framework for processing large data sets in parallel on distributed computing systems. It includes two main phases: Map (projection) and Reduce (reduction).

Advantages of MapReduce:

✅Scalability: MapReduce easily scales to thousands of machines, allowing it to process huge amounts of data

✅Parallelism: MapReduce automatically distributes tasks across available nodes, executing them in parallel, reducing computational time

✅Fault tolerance: Built-in fault tolerance allows tasks to be restarted in the event of node failure, ensuring completion without data loss

Disadvantages of MapReduce:

✅High I/O Cost: One of the key disadvantages is that data is written and read from disk between the Map and Reduce stages, significantly reducing performance in tasks where fast data transfer is important

✅Lack of interactivity: MapReduce is designed for batch processing, making it inefficient for interactive queries or real-time analysis

✅Shuffle phase requirement: The shuffle phase is often resource intensive and time, making this process a bottleneck in MapReduce performance

✅Low performance for complex tasks: For complex algorithms that require many steps of communication between nodes (e.g. iterative tasks), MapReduce performance degrades

You can also learn more about MapReduce from here

😎💡🔥A selection of unpopular but very useful Python libraries for working with data

Bottleneck is a library that speeds up NumPy methods up to 25 times, especially when processing arrays containing NaN values. It optimizes calculations such as finding minima, maxima, medians, and other aggregate functions. By using specialized algorithms and handling missing data, Bottleneck significantly speeds up work with large data sets, making it more efficient than standard NumPy methods.

Nbcommands is a tool that simplifies code search in Jupyter notebooks, eliminating the need for users to search manually. It allows you to find and manage code by keywords, functions, or other elements, which significantly speeds up working with large projects in Jupyter and helps users navigate their notes and code blocks more efficiently.

SciencePlots is a style library for matplotlib that allows you to create professional graphs for presentations, research papers, and other scientific publications. It offers a set of predefined styles that meet the requirements for data visualization in scientific papers, making graphs more readable and aesthetically pleasing. SciencePlots makes it easy to create high-quality graphs that meet the standards of academic publications and presentations.

Aquarel is a library that adds additional styles to visualizations in matplotlib. It allows you to improve the appearance of graphs, making them more attractive and professional. Aquarel simplifies the creation of custom styles, helping users create graphs with more interesting designs without having to manually configure all the visualization parameters.

Modelstore is a library for managing and tracking machine learning models. It helps organize, save, and version models, as well as track their lifecycle. With Modelstore, users can easily save models to various storages (S3, GCP, Azure, and others), manage their updates and restore. This makes it easier to deploy and monitor models in production environments, making working with models more convenient and controllable.

CleverCSV is a library that improves the process of parsing CSV files and helps avoid errors when reading them with Pandas. It automatically detects the correct delimiters and format of CSV files, which is especially useful when working with files that have non-standard or heterogeneous structures. CleverCSV simplifies working with data by eliminating errors associated with incorrect recognition of delimiters and other file format parameters.

🌎TOP DS-events all over the world in October
Oct 1-2 - AI and Big Data Expo Europe - Amsterdam, Netherlands - https://www.ai-expo.net/europe/
Oct 7-10 - Coalesce - Las Vegas, USA - https://coalesce.getdbt.com/
Oct 9-10 - World Summit AI - Amsterdam, Netherlands - https://worldsummit.ai/
Oct 9-10 - Big Data & AI World - Singapore, Singapore - https://www.bigdataworldasia.com/
Oct 10-11 - COLLIDE 2024: The South's largest data & AI conference - Atlanta, USA - https://datasciconnect.com/events/collide/
Oct 14-17 - Data, AI & Analytics Conference Europe 2024 - London, UK - https://irmuk.co.uk/data-ai-conference-europe-2024/
Oct 16-17 - Spatial Data Science Conference 2024 - New York, USA - https://spatial-data-science-conference.com/2024/newyork
Oct 19 - Oktoberfest - London, UK - https://datasciencefestival.com/event/oktoberfest-2024/
Oct 19 - INFORMS Workshop on Data Science 2024 - Seattle, Washington, USA - https://sites.google.com/view/data-science-2024
Oct 20-25 - TDWI Transform - Orlando, USA - https://tdwi.org/events/conferences/orlando/information/sell-your-boss.aspx
Oct 21-25 - SIAM Conference on Mathematics of Data Science (MDS24) - Atlanta, USA - https://www.siam.org/conferences-events/siam-conferences/mds24/
Oct 23-24 - NDSML Summit 2024 + AI2R Expo - Stockholm, Sweden - https://ndsmlsummit.com/
Oct 28-29 - Cyber Security Summit - San Paulo, Brazil - https://www.cybersecuritysummit.com.br/index.php
Oct 29-31 - ODSC West - California, United States - https://odsc.com/

💡😎3 unpopular but very necessary visualization libraries

Supertree is a Python library designed for interactive and convenient visualization of decision trees in Jupyter Notebooks, Jupyter Lab, Google Colab and other notebooks that support HTML rendering. With this tool, you can not only visualize decision trees, but also interact with them directly in the notebook.

Mycelium is a library for creating graphical visualizations of machine learning models or any other directed acyclic graphs. It also provides the ability to use the Talaria graph viewer to visualize and optimize models

TensorHue is a Python library designed to visualize tensors directly in the console, making it easier to analyze and debug them, making the process of working with tensors more visual and understandable.

😎⚡️A powerful dataset generated using Claude Opus.

Synthia-v1.5-I is a dataset of over 20,000 technical questions and answers designed to train large language models (LLM). It includes system prompts styled like Orca to encourage the generation of diverse answers. This dataset can be used to train models to answer technical questions more accurately and comprehensively, improving their performance on a variety of technical and engineering problems.

✅To load the dataset using Python:

from datasets import load_dataset
ds = load_dataset("migtissera/Synthia-v1.5-I")

⚡️HTTP SQLite StarbaseDB

StarbaseDB is a powerful and scalable open source database that is based on SQLite and runs over the HTTP protocol. This database is built to run in a cloud environment (e.g. on Cloudflare), allowing it to scale efficiently down to zero based on load. Key benefits of StarbaseDB include:

✅Ease of use: Provides the ability to work through HTTP requests, making it easy to integrate with various systems and services.
✅Scalability: Automatically adjusts to load volume with the ability to scale both ways.
✅Support for SQLite: Utilize the time-tested and lightweight SQLite database for data storage.
✅Open Source: Open source, allowing developers to customize and improve the system to suit their needs.

It is suitable for developers who are looking for a simple and reliable way to organize databases with minimal customization and high availability in cloud platforms such as Cloudflare.