The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

BYOK

Aipreneur • 39 implied HN points • 08 Mar 23
🕹 Technology AI Data science Machine Learning Cloud Computing Mobile technology
  1. BYOD (Bring Your Own Device) became popular in corporates due to iPhone's rise and employee preferences.
  2. BYOD is beneficial for companies in cost-saving, convenience, increased mobility, and changing workforce demographics.
  3. The emerging trend of BYOK (Bring Your Own Keys) is starting in AI platforms, where users need to pay for keys to access and use data responsibly.

Twitter open-sourced their recommendation algorithm

MLOps Newsletter • 39 implied HN points • 09 Apr 23
🕹 Technology Algorithms Machine Learning Open Source Neural Networks Data science
  1. Twitter has open-sourced their recommendation algorithm for both training and serving layers.
  2. The algorithm involves candidate generation for in-network and out-network tweets, ranking models, and filtering based on different metrics.
  3. Twitter's recommendation algorithm is user-centric, focusing on user-to-user relationships before recommending tweets.

Introduction to R for Data Science (Part Seven Final)

The Software & Data Spectrum • 39 implied HN points • 06 Apr 23
🕹 Technology Data science Visualization Themes
  1. Boxplots are common for visualizing data like stock pricing, and you can customize them with colors and flips.
  2. Variable plotting can include heat maps to show occurrences, and you can adjust the appearance with features like scale_fill_gradient().
  3. Coordinate your graphs using functions like coord_cartesian() and facet them based on specific variables for more detailed insights.
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Introduction to R for Data Science (Part Five)

The Software & Data Spectrum • 39 implied HN points • 30 Mar 23
🕹 Technology Data science Data Manipulation Programming
  1. Using apply functions in R like lapply and sapply can help apply functions to elements in a vector or list.
  2. Math functions in R like abs(), sum(), mean(), and round() are useful for basic calculations and rounding numbers.
  3. Data manipulation in R using dplyr involves functions like filter(), arrange(), select(), and mutate() to filter, sort, and create new columns in datasets.

🥟 Chao-Down #70 ChatGPT reads financial headlines and Federal Reserve speeches, Google uses generative AI for ads, IBM and Moderna develop vaccines with AI and quantum computing

Chaos Theory • 39 implied HN points • 24 Apr 23
🕹 Technology AI Quantum Computing Data science Wearable Tech Machine Learning
  1. ChatGPT reads financial headlines and Federal Reserve speeches for prediction
  2. Google employs generative AI for advanced ad campaigns
  3. IBM and Moderna collaborate on AI and quantum computing for vaccine development

How to Create Good Documentation in Software Engineering and Tech[Technique Tuesdays]

Technology Made Simple • 59 implied HN points • 19 Oct 22
🕹 Technology Software Engineering Data science Machine Learning Documentation Tech
  1. Good documentation in software engineering is crucial as it provides clarity to the team about goals and work done, enhancing productivity.
  2. Key pillars of good documentation include having a vision for the company and products, outlining resource/situational constraints, detailing data sources and processing, tracking projects in progress, sharing actual code, and establishing ownership.
  3. Benefits of good documentation in tech include aligning teams, clarifying vision and plans, reducing onboarding time, and promoting asynchronicity in an increasingly remote working environment.

Amazon BASE TTS Makers claim "Emergent Abilities"

Machine Economy Press • 2 implied HN points • 22 Feb 24
🕹 Technology AI Data science Text-to-Speech Generative AI Natural Language Processing
  1. Amazon has developed a new, massive text-to-speech model called BASE TTS with emergent abilities, enhancing its natural speech capabilities for AI assistants like Alexa.
  2. The 980 million parameter BASE TTS model is significant for audio and NLP advancements, as it's the largest text-to-speech model created so far.
  3. Text-to-speech and NLP innovations are paving the way for more human-like interactions with voice assistants, marking a shift towards ambient computing.

Your self

The Strategy Toolkit • 26 implied HN points • 22 May 23
🔬 Science Biology Data science Genetics Climate change
  1. Data is valuable, but not the only answer - combining mysteries, facts, and numbers leads to better understanding.
  2. Using historical data for predictions can be risky - correlation does not always imply causation.
  3. Human evolution is ongoing - recent studies show an acceleration in mutations due to environmental changes.

The Bias vs Variance Tradeoff [Math Mondays]

Technology Made Simple • 39 implied HN points • 06 Dec 22
🕹 Technology Data science Machine Learning Statistics Deep Learning Tech Education
  1. Understanding the Bias-Variance Tradeoff is crucial in Data Science and Machine Learning.
  2. Bias in a Machine Learning Model refers to prediction errors, while Variance accounts for the spread in predictions.
  3. High Bias can lead to underfitting, where the model doesn't grasp the data pattern fully, while High Variance can result in overfitting, where the model learns noise in the data.

Why Credit Card Data still makes money

Magis • 2 HN points • 03 Feb 24
💼 Business Data science Investment Hedge Funds Data Analysis Talent Acquisition
  1. Credit card data remains valuable despite its availability because of the infrastructure and talent required to utilize it effectively.
  2. Having the computational resources and expertise to analyze consumer spending data gives larger firms an advantage over smaller firms.
  3. Success in leveraging consumer spending data depends on the rarity of talent that can understand and apply it effectively.

Bayesian Thinking for Software Engineering [Math Mondays]

Technology Made Simple • 59 implied HN points • 03 May 22
🕹 Technology Software Engineering Data science Deep Learning Mathematics
  1. Bayes Theorem allows us to update beliefs based on evidence, crucial for software developers making decisions.
  2. Bayesian Thinking is implicit in many decisions we make, and recognizing its importance can prevent fallacies.
  3. Learning Bayesian Thinking involves understanding intuition behind the math, using resources like StatsQuest and 3Blue1Brown.

Why Sorry Dave?

Sorry Dave • 1 HN point • 03 Mar 24
🕹 Technology AI Software Development Bug Fixing Data science
  1. According to MIT, over 100 errors exist in every thousand lines of code, which can have serious consequences like known human deaths.
  2. Software defects cost more than $2 trillion annually, emphasizing the need for better software development methods.
  3. While AI can assist in creating safer code, it's essential to explore new approaches beyond just relying on machine learning models.

How to get into tech as a behavioral scientist

The Kahneman Bot • 19 implied HN points • 13 Feb 23
💼 Business Tech Start-ups Product Management Data science Behavioral Science
  1. To get into tech as a behavioral scientist, consider starting in a junior PM role, transferring internally, working at a startup, or starting your own company.
  2. Before transitioning into tech, make sure you enjoy building software and understand how tech teams work.
  3. Experienced behavioral scientists can enter tech by joining a big tech company as a researcher, rebranding as a data scientist, or joining a tech company that values behavioral science as part of its IP.

July Newsletter

RSS DS+AI Section • 11 implied HN points • 03 Jul 23
🕹 Technology Data science Artificial Intelligence Ethics Research Applications
  1. The newsletter features updates on industrial strength data science, including committee activities and upcoming events.
  2. Ethics, bias, and diversity remain hot topics in data science and AI, with examples of generative AI misuse and intentional misuse.
  3. The newsletter includes practical tips, developments in research, and fun projects in the data science and AI field.

June Newsletter

RSS DS+AI Section • 11 implied HN points • 02 Jun 23
🕹 Technology Data science Generative AI Open Source AI Development Ethics
  1. June newsletter focuses on Open Source special, including recent developments in the open source community.
  2. The newsletter highlights activities of the committee, discussions on AI ethics and diversity, and advancements in generative AI.
  3. An in-depth exploration of the open source explosion driven by the development of generative AI, showcasing the surge of open source capabilities and research contributions.

LLM Chronicles #5: GPT For Ecommerce Search Engine With Pinecone

Pratik’s Pakodas 🍿 • 8 implied HN points • 09 May 23
🕹 Technology ML Search Engine GPT Ecommerce Data science
  1. In certain scenarios, companies use 2 types of hybrid search: weighted scoring and filter and rerank, especially prevalent in e-commerce.
  2. GPT can be leveraged for query understanding to parse out complex queries and populate Elasticsearch/Solr with detected entities.
  3. Although using GPT-4 for this purpose may be costly and slow, training an open-source model like MPT-7B can be a more viable option.

Uncertainty in machine learning deployments

Denis’s Substack • 7 HN points • 07 Jun 23
🕹 Technology Machine Learning Data science Software Engineering API Automation
  1. Many machine learning projects never make it to production due to various reasons like lack of stakeholder buy-in and data quality issues.
  2. The traditional linear process of analyzing, extracting data, modeling, deploying, and operating models can be naive and not reduce uncertainty.
  3. Embracing uncertainty in machine learning deployments can involve starting the deployment phase before data extraction, leading to constant value addition throughout the process.