The hottest Machine Learning Substack posts right now

And their main takeaways
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Top Business Topics

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

Data Science Weekly Newsletter • 0 implied HN points • 01 May 22
🕹 Technology Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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.
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[in case you missed it] Data Science Weekly - Issue 430

Data Science Weekly Newsletter • 0 implied HN points • 20 Feb 22
🕹 Technology Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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.

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

Data Science Weekly Newsletter • 0 implied HN points • 02 May 21
🕹 Technology Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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.

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

Data Science Weekly Newsletter • 0 implied HN points • 28 Mar 21
🕹 Technology Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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 Machine Learning
  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.