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 462

Data Science Weekly Newsletter • 0 implied HN points • 02 Oct 22
🕹 Technology Data science
  1. Teaching students about scientific failure is important. It helps them understand resilience and learn from mistakes.
  2. AI systems are advancing rapidly, with new tools like video generation from text prompts. This opens up new opportunities for creators.
  3. Understanding uncertainties in deep learning is key for improving model performance. It helps practitioners make better decisions.

[in case you missed it] Data Science Weekly - Issue 461

Data Science Weekly Newsletter • 0 implied HN points • 25 Sep 22
🕹 Technology Data science
  1. NLP is a growing field, but using it effectively is still a challenge for many. People are eager to learn how to make NLP useful in their work.
  2. Curating social media accounts can be a rewarding experience. It helps to connect with a community and share insights in fun ways.
  3. Generative AI can boost productivity and creativity significantly. It has the potential to create a lot of economic value by making workers faster and more effective.

[in case you missed it] Data Science Weekly - Issue 460

Data Science Weekly Newsletter • 0 implied HN points • 18 Sep 22
🕹 Technology Data science
  1. Data scientists need soft skills like communication and teamwork. These skills help them work better with others and tell stories from data.
  2. There's a lot of free, live-streamed data science content available on Twitch. This makes it easier for everyone to learn and connect with the data science community.
  3. Understanding how to use AI tools for content generation can open up new creative possibilities. These tools can help enhance projects in various ways.

[in case you missed It] Data Science Weekly - Issue 459

Data Science Weekly Newsletter • 0 implied HN points • 11 Sep 22
🕹 Technology Data science
  1. Organizations should work on improving their data quality because it directly impacts their success and competitive edge. Creating better data can lead to better decisions and outcomes.
  2. The modern data stack's activation layer is crucial for turning data into actionable insights. This allows companies to go beyond just looking at data and actually use it to improve their products and services.
  3. Using the right tools, like ONNX for model deployment, can help make machine learning models more portable and less tied to specific programming environments. This makes it easier to run models across different programming languages.

[in case you missed it] Data Science Weekly - Issue 458

Data Science Weekly Newsletter • 0 implied HN points • 04 Sep 22
🕹 Technology Data science
  1. Machine learning has best practices that can help improve projects. A document from Google shares these tips for those who have some background in ML.
  2. There is a lot of hype around deep learning technology, leading to confusion about its actual capabilities. People have been predicting big changes in jobs and advancements, but many advancements are still awaited.
  3. AI can create interesting art from text prompts using tools like DALL·E 2. This showcases how technology can blend creativity and machine learning.
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[in case you missed it] Data Science Weekly - Issue 457

Data Science Weekly Newsletter • 0 implied HN points • 28 Aug 22
🕹 Technology Data science
  1. AI has limits when it comes to understanding human language. It can't fully replicate how humans think because language itself is restrictive.
  2. Observable now offers Free Teams, making it easier for data people to collaborate publicly. You can create teams quickly and share notebooks without complicated setups.
  3. The backpropagation algorithm in machine learning is often misunderstood. It is more complex than just applying the chain rule repeatedly, and oversimplifying it can lead to problems.

[in case you missed it] Data Science Weekly - Issue 456

Data Science Weekly Newsletter • 0 implied HN points • 21 Aug 22
🕹 Technology Data science
  1. Machine learning models need regular maintenance. Even after they're deployed, the changing world means they require constant updates to stay effective.
  2. Specialized skills in data science can lead to better job opportunities. Understanding different roles can help you maximize your impact in the field.
  3. Learning resources for machine learning and data science are widely available. Whether through courses, videos, or discussions, there's plenty of help to get started in this exciting area.

[In case you missed it] Data Science Weekly - Issue 454

Data Science Weekly Newsletter • 0 implied HN points • 07 Aug 22
🕹 Technology Data science
  1. NASA is using AI to categorize millions of astronaut photos of Earth, making it easier for scientists to find specific images.
  2. Data-driven companies can have a competitive edge, especially in industries where expertise and speed matter.
  3. Understanding and explaining complex models is important for making ethical and business decisions before automating processes.

[in case you missed it] Data Science Weekly - Issue 452

Data Science Weekly Newsletter • 0 implied HN points • 24 Jul 22
🕹 Technology Data science
  1. Data scientists are still in demand and well-paid, with job growth expected to continue into the future.
  2. Large Language Models (LLMs) are playing a big role in innovation and are becoming a part of everyday life.
  3. There's a growing need for domain experts in deep learning, allowing more people without advanced degrees to contribute to the field.

[In case you missed it] Data Science Weekly - Issue 450

Data Science Weekly Newsletter • 0 implied HN points • 10 Jul 22
🕹 Technology Data science
  1. AI forecasting contests are being used to predict future progress in AI, showing how forecasts can be evaluated based on actual results.
  2. The demand for analytics engineers is growing, shifting from a less desirable role to one of great interest in the job market.
  3. A new multilingual translation model called NLLB-200 helps translate between 200 low-resource languages, making high-quality translation more accessible.

[in case you missed it] Data Science Weekly - Issue 448

Data Science Weekly Newsletter • 0 implied HN points • 26 Jun 22
🕹 Technology Data science
  1. Machine learning can help the IRS by better analyzing the large amount of tax data they collect, making tax enforcement more effective.
  2. New models like Denoising Diffusion Probabilistic Models are showing great promise in generating high-quality images and audio from simpler inputs.
  3. There is a focus on improving machine learning practices, such as being careful with training data and understanding how to boost model performance through proper methods.

[in case you missed It] Data Science Weekly - Issue 447

Data Science Weekly Newsletter • 0 implied HN points • 19 Jun 22
🕹 Technology Data science
  1. Natural Language Processing is advancing quickly, with AI starting to mimic human-like conversation. This technology could change how we interact with machines.
  2. DeepMind is using AI for significant medical discoveries, showing real-world applications of machine learning beyond just technology.
  3. There's a debate in the AI community about the limits of scaling language models. Some believe that simply making them bigger may not solve all problems.

[in case you missed it] Data Science Weekly - Issue 446

Data Science Weekly Newsletter • 0 implied HN points • 12 Jun 22
🕹 Technology Data science
  1. The connection between literature and AI has a long history. There are many examples of how machines have been used to create and assist in writing over the years.
  2. Jupyter Notebooks are versatile tools for data science. They can be used in surprising ways beyond just coding, mixing visualizations and markdown effectively.
  3. Understanding how to use AI responsibly is important. As AI increasingly relies on crowdworkers for data, it raises ethical questions about oversight and compliance.

[in case you missed it] Data Science Weekly - Issue 445

Data Science Weekly Newsletter • 0 implied HN points • 05 Jun 22
🕹 Technology Data science
  1. There are new best practices for using large language models responsibly. This is important as AI technology continues to grow and impact many areas.
  2. The world is producing more food without increasing the amount of land used for farming, which means we can help the environment while feeding more people.
  3. Training large models can be demanding in terms of resources. Techniques like using compact word vectors can help make machine learning more efficient.

[in case you missed it] Data Science Weekly - Issue 444

Data Science Weekly Newsletter • 0 implied HN points • 29 May 22
🕹 Technology Data science
  1. Good ML systems need careful design and planning. It's important to know the difference between research and real-world applications.
  2. Data isn't always the best way to make decisions. Sometimes relying too much on data can lead to worse outcomes.
  3. New AI technologies are changing how we think about intellectual property. We might need new laws to keep up with inventions created by machines.

[in case you missed it] Data Science Weekly - Issue 443

Data Science Weekly Newsletter • 0 implied HN points • 22 May 22
🕹 Technology Data science
  1. There's a new initiative where you can share what you're up to, and they might include your story in the newsletter. It's a nice way to connect with others in the data science community.
  2. There's a focus on improving software development skills for data scientists by following best practices like version control and automatic testing. This can help teams work better together.
  3. AI-generated art is being debated, with some arguing it's just imitation and not true art. It raises questions about the value of creativity and human experience in art.

[in case you missed It] Data Science Weekly - Issue 440

Data Science Weekly Newsletter • 0 implied HN points • 01 May 22
🕹 Technology Data science
  1. AI is getting smarter, but we need better ways to ask it questions about its decisions to understand it better.
  2. Synthetic data can help when there's not enough real data for training, allowing us to create more examples for our models.
  3. Data accessibility is really important because unlocking the data can help solve big problems and improve society as a whole.

[in case you missed it ] Data Science Weekly - Issue 436

Data Science Weekly Newsletter • 0 implied HN points • 03 Apr 22
🕹 Technology Data science
  1. Aggregating data too much can hide important details. It's better to keep the complexity to find new insights.
  2. Waymo is testing fully autonomous cars in San Francisco. This shows how self-driving technology is becoming part of everyday life.
  3. Graph Neural Networks can handle missing information in data efficiently. They help make better use of connected data even when some details are missing.

[in case you missed it] Data Science Weekly - Issue 435

Data Science Weekly Newsletter • 0 implied HN points • 27 Mar 22
🕹 Technology Data science
  1. Algorithmic impact assessments are important for using data in healthcare. They help ensure that the technology is safe and beneficial for everyone involved.
  2. Using deep learning on electronic medical records may not work well right now. It might be better to focus on improving IT support and fixing underlying structures instead.
  3. There's a problem with how we explain AI systems. Many explanations offered today don't truly help us understand how these systems work.

[in case you missed it] Data Science Weekly - Issue 433

Data Science Weekly Newsletter • 0 implied HN points • 13 Mar 22
🕹 Technology Data science
  1. Deep learning is facing challenges and needs more progress to improve its effectiveness. Experts are looking at what can be done to advance AI technology.
  2. MLOps, or machine learning operations, is currently chaotic but it’s an important area of growth. The ecosystem is rapidly evolving with new tools and practices appearing every week.
  3. There are new techniques and tools emerging to help in areas like data visualization and machine learning. These developments can make it easier for both beginners and experts in the field.

[in case you missed it] Data Science Weekly - Issue 430

Data Science Weekly Newsletter • 0 implied HN points • 20 Feb 22
🕹 Technology Data science
  1. Data businesses are a big part of tech, but not enough resources explain how they work. Understanding their models can help people navigate the industry better.
  2. Investors are interested in machine learning and see many opportunities and challenges in startups. Talking to them can give insights into what they're looking for.
  3. Learning how to make data visualization easier can help you communicate better. There are ways to think about it that make the process feel more natural.

[in case you missed it] Data Science Weekly - Issue 421

Data Science Weekly Newsletter • 0 implied HN points • 19 Dec 21
🕹 Technology Data science
  1. Lee Wilkinson made big contributions to how we visualize data, helping us understand graphics better.
  2. A new journal for machine learning research will use a transparent review process to improve scholarly communication.
  3. Feature engineering is still important in data science despite the rise of deep learning, showing that sometimes traditional methods still apply.

[in case you missed it] Data Science Weekly - Issue 416

Data Science Weekly Newsletter • 0 implied HN points • 14 Nov 21
🕹 Technology Data science
  1. ML platforms are crucial for turning models into valuable tools, and each tech company has its own approach and tools to integrate machine learning effectively.
  2. While Kubernetes has advantages for managing data engineering, it's not always necessary and can be frustrating for engineers just wanting to help the business use data better.
  3. New large language models are emerging, making GPT-3 less unique; people are working on creating similar models that could soon be available.

[in case you missed it] Data Science Weekly - Issue 413

Data Science Weekly Newsletter • 0 implied HN points • 24 Oct 21
🕹 Technology Data science
  1. Understanding your tools is essential for success in computer science. Learning how to use the command line and version control can help you a lot.
  2. Improving language models to reduce harmful content is a complex task. It's important to ensure these models are safe while still being effective.
  3. Getting a job in data science is easier when you know what companies look for. Keep an eye on the key skills and experiences employers value most.

[in case you missed it] Data Science Weekly - Issue 411

Data Science Weekly Newsletter • 0 implied HN points • 10 Oct 21
🕹 Technology Data science
  1. Freelancing in data visualization can be tricky. It's important to learn from mistakes and adjust strategies for better outcomes.
  2. Combining AI with art can bring lost masterpieces back to life. Using algorithms to mimic an artist's style can recreate vibrant colors in old, damaged artworks.
  3. Building a strong data team is essential for businesses. Companies need to focus on data strategy, governance, and analytics to harness the power of data effectively.

[in case you missed it] Data Science Weekly - Issue 410

Data Science Weekly Newsletter • 0 implied HN points • 03 Oct 21
🕹 Technology Data science
  1. Data science is growing quickly, and the best companies to work for vary depending on your career stage. It's important to find a workplace that helps you grow in your data science career.
  2. Recent research is improving weather prediction by looking at short-term changes, like predicting rain in the next hour. This can be really useful for planning daily activities.
  3. Using statistics can help us understand large groups by studying small samples. It simplifies the data and gives us insights without needing to look at everything.

[in case you missed it] Data Science Weekly - Issue 409

Data Science Weekly Newsletter • 0 implied HN points • 26 Sep 21
🕹 Technology Data science
  1. Trees are becoming a new model for understanding ecology and plant intelligence. They help researchers think more deeply about the environment.
  2. Effective machine learning often starts without actually using machine learning. It’s important to focus on gathering quality data and defining clear processes first.
  3. Business Intelligence (BI) tools are evolving, but they should focus on providing clear and complete answers to data-related questions for users.

[in case you missed it] Data Science Weekly - Issue 405

Data Science Weekly Newsletter • 0 implied HN points • 29 Aug 21
🕹 Technology Data science
  1. Data teams should treat their work as products for their colleagues, focusing on collaboration to create effective solutions. This helps ensure that the end result meets the needs of those using the data.
  2. Many machine learning funds in finance fail due to common mistakes, but the few that succeed can deliver impressive results for investors. Understanding these pitfalls is key to improving success rates.
  3. OpenAI's Ilya Sutskever has been a major influence in AI, contributing to key advancements in deep learning. His work has played a big role in the evolution of intelligence in machines.

[in case you missed it] Data Science Weekly - Issue 400

Data Science Weekly Newsletter • 0 implied HN points • 25 Jul 21
🕹 Technology Data science
  1. A new documentary used AI to generate Anthony Bourdain's voice, raising questions about ethics in media. It's important to think about how technology like this affects what we perceive as real.
  2. Deep learning is becoming more effective despite challenges, and understanding its success can help bridge gaps between traditional statistics and modern AI. Bigger and deeper models often yield better results, even with less data.
  3. Combining different AI models, like Transformers and convolutional neural networks, can lead to better performance in tasks like image recognition. This shows that mixing approaches can help overcome the limitations of each technology on its own.

[in case you missed it] Data Science Weekly - Issue 399

Data Science Weekly Newsletter • 0 implied HN points • 18 Jul 21
🕹 Technology Data science
  1. There's a growing movement called 'Data for Good', which focuses on using data to help improve society. It's important to understand the different groups and initiatives within this space.
  2. Peer review in data science is crucial, especially for startups, but the process can be tricky. It's good to learn from experiences about what works and what doesn't.
  3. Big companies like Amazon collect a lot of data about their users, often more than people realize. It's important to be aware of how this data is being tracked and used.

[in case you missed it] Data Science Weekly - Issue 398

Data Science Weekly Newsletter • 0 implied HN points • 11 Jul 21
🕹 Technology Data science
  1. Data science projects can analyze unique datasets, like personal music streaming from Apple Music, helping us understand our listening habits better.
  2. Language affects how cultures understand color, with some languages having fewer words for colors, which is interesting for studying cultural differences.
  3. Using advanced techniques like causal inference can help businesses make better pricing decisions, improving their competitiveness in the market.

[in case you missed it] Data Science Weekly - Issue 396

Data Science Weekly Newsletter • 0 implied HN points • 27 Jun 21
🕹 Technology Data science
  1. Understanding hype cycles can help us see how technology develops over time. It's interesting to look back at these cycles to learn from past trends.
  2. Multi-task learning is beneficial as it allows machines to make multiple predictions. This can lead to more effective and efficient models.
  3. AI struggles with understanding basic concepts like 'same' and 'different.' This limitation raises questions about how truly intelligent AI can become.

[in case you missed it] Data Science Weekly - Issue 395

Data Science Weekly Newsletter • 0 implied HN points • 20 Jun 21
🕹 Technology Data science
  1. TinyML is a growing field with many projects and papers exploring its potential. It's basically about running machine learning on small devices.
  2. There are different technologies like Dask and Vaex for processing large datasets in Python. Each has its own strengths, so it's good to know which one fits your needs.
  3. Understanding multi-objective optimization can help you make better decisions in complex situations. It's about looking at several goals at once instead of just one.

[in case you missed it] Data Science Weekly - Issue 394

Data Science Weekly Newsletter • 0 implied HN points • 13 Jun 21
🕹 Technology Data science
  1. The data economy harms our privacy by collecting personal information for profit. It's important to rethink this approach.
  2. New AI methods are improving tasks like chip design, allowing machines to do the work faster and better than humans.
  3. There's a growing interest in data management concepts like data mesh, which focuses on decentralized data ownership and treating data as a product.

[in case you missed it] Data Science Weekly - Issue 393

Data Science Weekly Newsletter • 0 implied HN points • 06 Jun 21
🕹 Technology Data science
  1. Fourier transforms can create 3D terrain or noise, and understanding how this works can be a fun and interesting challenge.
  2. Interactive tools are available to visualize complex data concepts like Gaussian processes, making it easier to grasp difficult ideas.
  3. Machine learning has potential issues in healthcare; it's important to approach this field carefully and thoughtfully.

[in case you missed it] Data Science Weekly - Issue 391

Data Science Weekly Newsletter • 0 implied HN points • 23 May 21
🕹 Technology Data science
  1. Major League Baseball is testing an automated system to call balls and strikes in games. This system aims to make calls accurately and fast so umpires can operate efficiently.
  2. A new tool called Flat makes it easy to manage and version datasets on Git and GitHub. This helps developers work more quickly with data while keeping track of changes.
  3. Twitter improved its image cropping algorithm to better serve all users. After receiving feedback, they are analyzing the model for fairness and accuracy.

[in case you missed it] Data Science Weekly - Issue 390

Data Science Weekly Newsletter • 0 implied HN points • 16 May 21
🕹 Technology Data science
  1. AI can solve complex puzzles better than humans, but humans still have unique skills. Don't give up on challenging word games just yet!
  2. Defining trees in biology is tricky because many plants don't fit into clear categories. It's surprising how many things that look like trees actually aren't.
  3. New technology makes searching through large image databases easier. With smart algorithms, you can quickly find the pictures you're looking for without remembering file names.

[in case you missed it] Data Science Weekly - Issue 389

Data Science Weekly Newsletter • 0 implied HN points • 09 May 21
🕹 Technology Data science
  1. Artificial intelligence is changing healthcare but raises important ethical questions, like the risk of bias and loss of doctors' decision-making power.
  2. Observable Plot is a new library designed to make data visualization easier and more enjoyable, built on the foundations of D3.
  3. Using SQL for data analysis can be very efficient, and it's worth remembering its capabilities compared to popular tools like Pandas.