The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

[in case you missed it] Data Science Weekly - Issue 388

Data Science Weekly Newsletter • 0 implied HN points • 02 May 21
🕹 Technology Data science
  1. Cluster analysis can be tricky since you often don't know how many groups to create. A new method called clustergram helps visualize data better as you adjust the number of clusters.
  2. Bayesian and frequentist methods in statistics provide different types of results, so they shouldn't be compared directly. They answer different questions rather than yielding similar outputs.
  3. Netflix is working on a feature called 'Play Something' to combat decision fatigue. This feature plays a show automatically, similar to turning on a TV, making it easier for users to start watching.

[in case you missed it] Data Science Weekly - Issue 387

Data Science Weekly Newsletter • 0 implied HN points • 25 Apr 21
🕹 Technology Data science
  1. Goodreads lets users decide what counts as a classic book, showing how the definition has changed over time. This online platform helps readers share their thoughts in various ways.
  2. Scientists are trying to decode whale language using AI, aiming to understand how these marine animals communicate. This research could reveal insights about their behavior and society.
  3. New techniques allow neural networks to solve tough equations much faster. This improvement can help us better model complex systems, making it easier for researchers and engineers.

[in case you missed it] Data Science Weekly - Issue 386

Data Science Weekly Newsletter • 0 implied HN points • 18 Apr 21
🕹 Technology Data science
  1. Chartability focuses on making data visuals more accessible for people with disabilities. It's about ensuring everyone can understand the information presented.
  2. Data observability is important as companies handle more data, helping them maintain data quality. This can prevent issues like missing or stale data from affecting business decisions.
  3. Using advanced learning techniques like Graph Neural Networks can improve how we process complex data structures. These techniques can reveal deeper insights into various systems.

[in case you missed it] Data Science Weekly - Issue 385

Data Science Weekly Newsletter • 0 implied HN points • 11 Apr 21
🕹 Technology Data science
  1. Building a good machine learning rig can be expensive. But with careful planning and research, you can create an effective setup.
  2. Understanding adaptive data analysis is important for trusting your models. New methods are being developed to address issues with model evaluation.
  3. Model compression techniques can help enhance performance. This includes strategies like quantization and knowledge distillation to make models smaller and faster.

[in case you missed it] Data Science Weekly - Issue 384

Data Science Weekly Newsletter • 0 implied HN points • 04 Apr 21
🕹 Technology Data science
  1. AI is improving tools like Google Maps, making them smarter and more helpful with real-time updates.
  2. It's important to focus on building effective machine learning systems that provide real value, instead of just labeling everything as AI.
  3. Data can be powerful for decision-making, but relying too heavily on numbers can lead to mistakes and misinterpretation.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

[in case you missed it] Data Science Weekly - Issue 383

Data Science Weekly Newsletter • 0 implied HN points • 28 Mar 21
🕹 Technology Data science
  1. AI is making strides in drug discovery by addressing important problems, and there's great research available on the topic.
  2. Jupyter notebooks are loved for data exploration but can be tricky for production use, leading to mixed feelings among data scientists and machine learning engineers.
  3. Detecting names in user messages is a complex challenge that's important for creating better virtual assistants.

[in case you missed it] Data Science Weekly - Issue 382

Data Science Weekly Newsletter • 0 implied HN points • 21 Mar 21
🕹 Technology Data science
  1. Computers can't write good stories. It's a big claim, but they really don't understand literature like humans do.
  2. Using color scales is important when showing data visually. Choosing the right colors can make your data easier to understand.
  3. Data science can help fight illegal fishing with satellite data. By tracking boats, experts can prevent unlawful activities in our oceans.

[in case you missed it] Data Science Weekly - Issue 381

Data Science Weekly Newsletter • 0 implied HN points • 14 Mar 21
🕹 Technology Data science
  1. Data sharing in Africa faces challenges due to issues like historical power imbalances and Western-centric policies. It's important to recognize these factors when discussing data access and usage.
  2. Machine learning models can struggle when tested on data that is different from what they were trained on. Research is being done to improve how these models generalize to new situations.
  3. New tools like Dolt combine Git and MySQL to help data scientists collaborate better on datasets. This makes it easier for teams to work together without overwriting each other's changes.

[in case you missed it] Data Science Weekly - Issue 379

Data Science Weekly Newsletter • 0 implied HN points • 28 Feb 21
🕹 Technology Data science
  1. Writing a book about data science can be a fun way to share knowledge and inspire others. It's also possible to make money online while doing it.
  2. Understanding Python concurrency is important for data scientists. Learning about topics like async and threads can boost your software engineering skills.
  3. Feature stores are essential for operationalizing machine learning. They help teams manage and deploy machine learning features efficiently.

[in case you missed it] Data Science Weekly - Issue 378

Data Science Weekly Newsletter • 0 implied HN points • 21 Feb 21
🕹 Technology Data science
  1. Creating robots that can think morally is similar to parenting. Teaching them right from wrong can be approached in the same way we teach children.
  2. Transformers are important in both language and image processing. Understanding how to use them can help with many tasks in data science.
  3. Building systems for data quality and observability is essential. By using tools like SQL, we can keep track of how our data changes and ensure it stays reliable.

[in case you missed it] Data Science Weekly - Issue 377

Data Science Weekly Newsletter • 0 implied HN points • 14 Feb 21
🕹 Technology Data science
  1. Using Active Learning can save time and effort in machine learning. It allows models to learn with less labeled data by letting them ask questions about unclear data.
  2. There is a growing shift from Excel to Python in many industries. This change is driven by the need for more advanced data analysis and the capabilities Python offers.
  3. Understanding the importance of machine learning in healthcare is crucial. Innovations like AI systems that can identify smells may lead to new diagnostic tools and enhance medical practices.

[in case you missed it] Data Science Weekly - Issue 376

Data Science Weekly Newsletter • 0 implied HN points • 07 Feb 21
🕹 Technology Data science
  1. Data quality is really important in high-stakes AI because it can greatly affect results in areas like health and finance. Many people focus on building models instead of ensuring good data quality.
  2. DanNet was a game-changer in computer vision when it was released ten years ago. It showed that deep learning models could even surpass human performance in certain tasks.
  3. Cohort analysis helps businesses understand their customers better by tracking different groups over time. It's useful for figuring out things like customer engagement and product performance.

[in case you missed it] Data Science Weekly - Issue 375

Data Science Weekly Newsletter • 0 implied HN points • 31 Jan 21
🕹 Technology Data science
  1. Building a machine learning (ML) team starts small but can grow significantly. As projects develop, different challenges arise that require specific team structures to tackle them.
  2. Effective machine learning should help systems generalize beyond the data they are trained on. This means creating algorithms that can learn from observations and apply that knowledge to new situations.
  3. AI is starting to influence many fields, like music technology, by learning characteristics of sound and improving products like guitar amplifiers. This shows how machine learning can apply to real-world problems in creative ways.

[in case you missed it] Data Science Weekly - Issue 374

Data Science Weekly Newsletter • 0 implied HN points • 24 Jan 21
🕹 Technology Data science
  1. Controlled experiments are important in data science to understand how new features perform. They help ensure that changes really make a difference and aren't just random results.
  2. AI is being used in various fields, including drug discovery and medical diagnostics, to improve accuracy and efficiency. Innovations like AI techniques can lead to faster and more accurate results in critical areas like cancer diagnosis.
  3. Understanding the theory behind machine learning can help data scientists create better models. Learning about tools like Support Vector Machines can enhance model performance and application.

[in case you missed it] Data Science Weekly - Issue 373

Data Science Weekly Newsletter • 0 implied HN points • 17 Jan 21
🕹 Technology Data science
  1. Machine learning is becoming an important tool in developmental biology, helping to analyze large datasets efficiently. It can aid in tasks like image analysis and cell grouping.
  2. There is a growing need for data engineers, with many more job openings in this area compared to data science roles. Training and skills in data engineering are becoming more valuable.
  3. The FDA has released its first action plan for using AI and machine learning in medical software. This shows a commitment to improving healthcare with technology.

[in case you missed it] Data Science Weekly - Issue 371

Data Science Weekly Newsletter • 0 implied HN points • 03 Jan 21
🕹 Technology Data science
  1. Real-time machine learning is becoming important for many companies, with some investing heavily in the necessary infrastructure. This has led to positive financial returns for them.
  2. There is a growing list of tools for machine learning operations, with many new entries improving how developers can manage their ML projects.
  3. Different techniques like Markov models can help in planning and optimizing tasks, like workout routines, by predicting the next steps based on previous actions.

[in case you missed it] Data Science Weekly - Issue 370

Data Science Weekly Newsletter • 0 implied HN points • 27 Dec 20
🕹 Technology Data science
  1. 2020 saw significant advancements in AI, especially with neural volume rendering and models that can learn rules themselves.
  2. Data scientists are in high demand, and platforms like Vettery can help job seekers connect with employers.
  3. Resources are available to help aspiring data scientists improve their skills, build portfolios, and create impactful resumes.

[in case you missed it] Data Science Weekly - Issue 369

Data Science Weekly Newsletter • 0 implied HN points • 20 Dec 20
🕹 Technology Data science
  1. Companies are now changing how they present information because machines and AI read their reports too. They're trying to make it easier for algorithms to understand, sometimes even avoiding negative words that might confuse them.
  2. Monitoring machine learning in production is crucial. It's important to catch any unusual patterns or changes in how models behave to ensure they keep performing well.
  3. Artificial intelligence is being developed to better interact with humans. By using virtual environments, researchers are teaching AI to mimic human behaviors and improve interaction quality.

[in case you missed it] Data Science Weekly - Issue 368

Data Science Weekly Newsletter • 0 implied HN points • 13 Dec 20
🕹 Technology Data science
  1. Hyperparameters and latent variables are important in machine learning. We need better methods to create reliable systems that make a real impact.
  2. Understanding how deep neural networks work can help us harness their power effectively. A new method called network dissection can help explain the roles of different units in these networks.
  3. Creating a successful data science team involves building strong collaborations and having the right tools in place. Focus on understanding goals and measuring performance to drive improvements.

[in case you missed it] Data Science Weekly - Issue 366

Data Science Weekly Newsletter • 0 implied HN points • 29 Nov 20
🕹 Technology Data science
  1. Pinterest improved its data infrastructure by moving from Lambda to Kappa architecture to better handle its visual signals for machine learning. This change aimed to streamline costs and enhance signal availability.
  2. When building machine learning models, companies like DoorDash face huge data challenges. Choosing the right feature store is crucial for managing this data effectively, ensuring performance without overspending.
  3. Differentially private learning still faces challenges in performance compared to traditional models. For effective results, more private data or improved features from public data may be necessary.

[in case you missed it] Data Science Weekly - Issue 365

Data Science Weekly Newsletter • 0 implied HN points • 22 Nov 20
🕹 Technology Data science
  1. There's a new newsletter called The Batch that shares important AI events and insights. It's easy to read and aimed at both engineers and business leaders.
  2. Dynamic data testing is different from software testing. It requires tests that can adapt to how data changes over time.
  3. Isolation Forest is currently a top choice for detecting anomalies in big data, thanks to its simplicity and effectiveness.

[in case you missed it] Data Science Weekly - Issue 364

Data Science Weekly Newsletter • 0 implied HN points • 15 Nov 20
🕹 Technology Data science
  1. Organizing data in spreadsheets helps reduce errors. Use consistent formats, avoid empty cells, and save backups to make analysis easier.
  2. AI is creating convincing fake music performances of famous artists. This raises legal concerns as the music industry watches closely.
  3. Monitoring performance is crucial in data science. Tools like Mona help track data and model performance to avoid issues like biases and errors.

[in case you missed it] Data Science Weekly - Issue 363

Data Science Weekly Newsletter • 0 implied HN points • 08 Nov 20
🕹 Technology Data science
  1. Synthetic biology has advanced significantly in its second decade, showcasing real achievements beyond just hype from the first decade.
  2. Data poisoning attacks can seriously impact machine learning models by manipulating their predictions, so it's important to use trusted data.
  3. Building a strong data science portfolio and tailoring your resume are key steps in landing a data science job.

[in case you missed it] Data Science Weekly - Issue 362

Data Science Weekly Newsletter • 0 implied HN points • 01 Nov 20
🕹 Technology Data science
  1. Using AI for form extraction can greatly help fields like journalism and medicine. This could be more impactful than just predictive models.
  2. Data intuition is an important skill for data scientists. It helps them avoid being misled by bad data and analyses.
  3. Data engineering and data science are interconnected, but they have different focuses. Data engineering deals with preparing data, while data science analyzes it for insights.

[in case you missed it] Data Science Weekly - Issue 361

Data Science Weekly Newsletter • 0 implied HN points • 25 Oct 20
🕹 Technology Data science
  1. Data infrastructure is becoming more complex, focusing on how data is analyzed rather than just the software. It's important to understand the latest technologies and best practices in this area.
  2. Many companies are using AI but only a small number see a real return on their investment. It's crucial to examine why some businesses succeed with AI while others struggle.
  3. Machine learning models need to be effectively put into production to solve real problems. Deployment is just as important as building the model itself.

[in case you missed it] Data Science Weekly - Issue 360

Data Science Weekly Newsletter • 0 implied HN points • 18 Oct 20
🕹 Technology Data science
  1. Making machine learning models run fast on GPUs is important for research and production. It can help speed up improvements and make coding more efficient.
  2. Companies like BMW are creating ethical guidelines for AI use to ensure it benefits people. This is a proactive step to use AI responsibly.
  3. There are various learning resources and tools available for anyone interested in data science. These can help you build a solid foundation and advance your career.

[in case you missed it] Data Science Weekly - Issue 359

Data Science Weekly Newsletter • 0 implied HN points • 11 Oct 20
🕹 Technology Data science
  1. Arduino is making it easier for everyone to use machine learning by providing resources to get started quickly. You can learn to set up voice recognition on devices like the Arduino Nano.
  2. TensorSensor is a new tool that helps programmers understand and debug deep learning code easier by visualizing tensor operations. This can be really helpful for those new to coding in this area.
  3. Papers with Code now links machine learning research with relevant code, making it easier to access both studies and their implementations for better understanding and usage.

[in case you missed it] Data Science Weekly - Issue 358

Data Science Weekly Newsletter • 0 implied HN points • 04 Oct 20
🕹 Technology Data science
  1. Data quality is really important for machine learning to work well. If the data is bad, it can mess up the whole project and make people doubt the results.
  2. The State of AI Report covers current trends and future predictions in artificial intelligence. It looks into research advances, talent availability, and the impact of AI on industries.
  3. Using mobile phone data can help understand and manage the COVID-19 pandemic. However, it's crucial to consider what types of behaviors and populations this data represents.

[in case you missed it] Data Science Weekly - Issue 357

Data Science Weekly Newsletter • 0 implied HN points • 27 Sep 20
🕹 Technology Data science
  1. Good communication can help teams solve technical problems better and make a bigger impact on their work.
  2. There are exciting competitions, like the C3.ai COVID-19 Grand Challenge, where data science projects can help tackle global issues.
  3. New tools like TensorFlow Recommenders and platforms like Dynabench are making it easier to build AI and benchmark its performance effectively.

[in case you missed it] Data Science Weekly - Issue 356

Data Science Weekly Newsletter • 0 implied HN points • 20 Sep 20
🕹 Technology Data science
  1. The ICML conference is a big deal for machine learning professionals, bringing together people from different backgrounds to share ideas.
  2. Apache Arrow is an essential library for data processing that aims to improve how we handle and share data efficiently.
  3. Transformers, a popular type of neural network, are closely related to Graph Neural Networks and have made significant contributions to natural language processing.

[in case you missed it] Data Science Weekly - Issue 355

Data Science Weekly Newsletter • 0 implied HN points • 13 Sep 20
🕹 Technology Data science
  1. DeepMind and Google Maps teamed up to improve travel time predictions using advanced technology called Graph Neural Networks. This helps users get even more accurate arrival times in busy cities.
  2. AI technology is now being used to spot edited videos, like deepfakes, by detecting hidden signals called 'deepfake heartbeats'. This could make it easier to tell which video was made with what software.
  3. A new book aims to teach machine learning from scratch, breaking down complex algorithms to make them understandable. It's a good resource for anyone wanting to learn the basics of machine learning.

[in case you missed it] Data Science Weekly - Issue 354

Data Science Weekly Newsletter • 0 implied HN points • 06 Sep 20
🕹 Technology Data science
  1. A new machine learning algorithm helped identify 50 new planets by analyzing old NASA data. This shows how AI can unlock discoveries from existing information.
  2. There has been a significant drop in deep learning job postings recently, especially among smaller companies. This indicates a shift in the demand for deep learning talent after the pandemic.
  3. Apple has launched a residency program for people with STEM backgrounds to improve their machine learning skills. This offers participants hands-on experience and personalized training.

[in case you missed it] Data Science Weekly - Issue 353

Data Science Weekly Newsletter • 0 implied HN points • 29 Aug 20
🕹 Technology Data science
  1. Testing machine learning systems is different from testing traditional software. It's important to do this testing well to ensure the models work as intended.
  2. Fast.ai has released new resources for deep learning, including a complete course and several libraries. These tools can help people learn and apply deep learning more effectively.
  3. AI systems can make decisions that seem efficient but might also cause unfair outcomes. It's vital to consider ethical implications when using algorithms in important areas like hiring or policing.

[in case you missed it] Data Science Weekly - Issue 352

Data Science Weekly Newsletter • 0 implied HN points • 23 Aug 20
🕹 Technology Data science
  1. minGPT is a simple way to understand and train GPT models with only 300 lines of code. It's designed to be clean and educational.
  2. Bias in datasets like CoNLL-2003 can affect how well AI models recognize names. If a model only learns from biased data, it may perform poorly on names that aren't represented.
  3. Real-world challenges in reinforcement learning can hinder its effectiveness. Researchers are working on solutions to make RL more applicable in practical situations.

[in case you missed it] Data Science Weekly - Issue 351

Data Science Weekly Newsletter • 0 implied HN points • 16 Aug 20
🕹 Technology Data science
  1. The Mona Lisa Effect is a fun digital experience where a portrait's eyes seem to follow you. You can try it by using your webcam.
  2. Maintaining machine learning models in production is challenging, but there are practical ways to manage issues like data contamination and model misbehavior.
  3. AI economics are important to understand, especially for long-tailed data distributions, so that machine learning teams can create better and more profitable AI applications.

[in case you missed it] Data Science Weekly - Issue 350

Data Science Weekly Newsletter • 0 implied HN points • 09 Aug 20
🕹 Technology Data science
  1. GPT-3 can create very human-like text and it can even write computer programs with just a few examples. This shows how advanced AI language models are becoming.
  2. Many languages are spoken around the world, but most natural language processing work has focused only on English. It's important to include other languages in research.
  3. Graph technologies are being used to solve complex business problems, such as making recommendations and detecting fraud. They are becoming essential tools in data science.

[in case you missed it] Data Science Weekly - Issue 349

Data Science Weekly Newsletter • 0 implied HN points • 02 Aug 20
🕹 Technology Data science
  1. Deep learning has important historical ideas that everyone in the field should know. Learning these basics can help new learners understand current research.
  2. As technology like GPT-3 emerges, understanding the hype around it is key. It helps to have a framework for sorting through the excitement and noise.
  3. There are challenges in using machine learning in production. It's easy to create a simple model, but making it work well with changing data is much harder.

[in case you missed it] Data Science Weekly - Issue 348

Data Science Weekly Newsletter • 0 implied HN points • 26 Jul 20
🕹 Technology Data science
  1. Deep learning papers can be overwhelming for beginners, so having a reading roadmap can help newcomers start with the right materials.
  2. Machine learning is creating valuable opportunities in different industries, and knowing where this value will occur can help companies stay competitive.
  3. New techniques in machine learning, like those for detecting earthquakes or improving developer experiences, show how technology is continuously evolving to solve real-world problems.

[in case you missed it] Data Science Weekly - Issue 347

Data Science Weekly Newsletter • 0 implied HN points • 19 Jul 20
🕹 Technology Data science
  1. Netflix is improving its data efficiency by using a dashboard that helps everyone see costs and usage trends. This way, decision-makers can make better choices based on clear information.
  2. Creating a strong portfolio and resume is really important for landing a data science job. Focus on showcasing your best skills and experiences to attract employers.
  3. There's a shift in building robots to assist humans instead of replacing them. The future should focus on robots that enhance our capabilities rather than take over our jobs.

[in case you missed it] Data Science Weekly - Issue 346

Data Science Weekly Newsletter • 0 implied HN points • 12 Jul 20
🕹 Technology Data science
  1. A workshop at the Santa Fe Institute explored the meaning and understanding in AI, involving participants from different fields to discuss how machines might understand like living beings.
  2. The cost of training AI is dropping much faster than expected, making it easier for companies to adopt AI technology in the coming years.
  3. Training Generative Adversarial Networks (GANs) presents challenges, but new algorithms are being developed to improve stability and performance in machine learning.