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 303

Data Science Weekly Newsletter • 0 implied HN points • 14 Sep 19
🕹 Technology Data science
  1. Stitch Fix is using machine learning to help customers pick outfits that match their style. It shows how technology can personalize shopping experiences.
  2. There's a push to protect workers from being replaced by automation. Some suggest taxing companies that use robots to keep people employed.
  3. AI is transforming fields like biology, especially in analyzing images. It highlights how technology is changing research and discovery in science.

[in case you missed it] Data Science Weekly - Issue 302

Data Science Weekly Newsletter • 0 implied HN points • 07 Sep 19
🕹 Technology Data science
  1. Yann LeCun is a key figure in deep learning, known for his work on convolutional neural networks, which help machines learn from data.
  2. Data scientists are in high demand, and understanding their salaries is important for those interested in entering the field.
  3. Deep learning techniques can swiftly perform tasks like face recognition, outperforming human experts in speed and accuracy.

[in case you missed it] Data Science Weekly - Issue 301

Data Science Weekly Newsletter • 0 implied HN points • 01 Sep 19
🕹 Technology Data science
  1. Effective management of data science teams requires specific skills and knowledge. Leaders should know how to build and sustain their teams well.
  2. Research takes time and effort, as shown by the history of neural networks. It's important to have patience and persistence in this field.
  3. Estimating the time for software projects can be difficult. This is often due to the unpredictable nature of problem-solving involved in the work.

[in case you missed it] Data Science Weekly - Issue 300

Data Science Weekly Newsletter • 0 implied HN points • 25 Aug 19
🕹 Technology Data science
  1. There's a new AI optimizer called RAdam that can help improve the accuracy of AI models. It automatically adjusts the learning rate based on training conditions.
  2. Deep learning is an area of study that's compared to classical methods and explores various neural network models. Understanding these models can help grasp the foundations of modern AI.
  3. Data scientists are in high demand, and there are resources available to help newcomers prepare for training programs. This can lead to job opportunities in the field.

[in case you missed it] Data Science Weekly - Issue 299

Data Science Weekly Newsletter • 0 implied HN points • 17 Aug 19
🕹 Technology Data science
  1. AI is now being used to improve video gaming, like training in soccer using a new football simulator. This shows how far technology has come in understanding games.
  2. Nvidia is making big strides in AI language models, making them faster and more efficient. This means we could see better and more responsive AI conversations soon.
  3. For those wanting to become data scientists, it's smarter to get a related job first and learn on the job. Skills can be built up as you go instead of trying to learn everything at once.
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[in case you missed it] Data Science Weekly - Issue 295

Data Science Weekly Newsletter • 0 implied HN points • 20 Jul 19
🕹 Technology Data science
  1. Netflix is moving away from collaborative filtering for recommendations, focusing on more effective strategies that drive revenue.
  2. Machine learning can play a big role in tackling climate change, helping us find solutions to one of our biggest challenges.
  3. There is a growing demand for data scientists to know a variety of tools like Python, R, and SQL, so it's important to keep learning and improving your skills.

[in case you missed it] Data Science Weekly - Issue 294

Data Science Weekly Newsletter • 0 implied HN points • 13 Jul 19
🕹 Technology Data science
  1. A new poker bot has learned strategies to beat skilled players, showing the advancements in AI technology. This could change how games are played and studied.
  2. Generative adversarial networks, which are known for creating deepfakes, may have positive uses in medical fields like cancer diagnosis. Before, they were mainly seen in a negative light.
  3. San Francisco is trying to use AI to reduce bias in the prosecution process, aiming to make the legal system fairer. This could help in addressing racial discrimination in the courtroom.

[in case you missed it] Data Science Weekly - Issue 293

Data Science Weekly Newsletter • 0 implied HN points • 06 Jul 19
🕹 Technology Data science
  1. The State of AI Report 2019 highlights how fast AI is growing and important developments from the past year.
  2. Machine learning is now being used to translate languages that were previously lost, opening up new ways to understand history.
  3. There are many resources and guides available for those wanting to get started in data science, covering everything from building a portfolio to writing a great resume.

[in case you missed it] Data Science Weekly - Issue 280

Data Science Weekly Newsletter • 0 implied HN points • 06 Apr 19
🕹 Technology Data science
  1. DeepMind is a big player in AI, and there's tension now that Google owns it. The question of who really controls AI is important.
  2. Warby Parker used an algorithm to help people try on glasses virtually, making shopping easier and more fun.
  3. MIT is experimenting with AI to create new types of food, showing that technology can change the way we think about flavors.

[in case you missed it] Data Science Weekly - Issue 279

Data Science Weekly Newsletter • 0 implied HN points • 30 Mar 19
🕹 Technology Data science
  1. Three scientists won the Turing Award for their work on neural networks. This award is a big deal in computing, kind of like a Nobel Prize.
  2. Machine learning is being used to create tools that can help doctors focus more on patients instead of taking notes. This could improve healthcare significantly.
  3. There's a new doodling app that uses AI to turn simple sketches into realistic images. This technology could be useful for video games and movies.

[in case you missed it] Data Science Weekly - Issue 269

Data Science Weekly Newsletter • 0 implied HN points • 20 Jan 19
🕹 Technology Data science
  1. Neural networks can be hard to understand. Researchers are trying to figure out what these models actually learn during training.
  2. Reinforcement learning is helping robots, like a robot dog, learn to move more like real animals without specific instructions.
  3. Using tools like Flask, you can quickly set up an API for machine learning models. This makes it easier to send data and get predictions.

[in case you missed it] Data Science Weekly - Issue 266

Data Science Weekly Newsletter • 0 implied HN points • 30 Dec 18
🕹 Technology Data science
  1. Netflix's internal debates show the clash between creative teams and data-driven decisions. Finding a balance between creativity and data analysis is important for success.
  2. Teaching AI to write stories can be funny but also highlights the challenges of using technology for creative tasks. It takes a lot of work to make machines understand human language.
  3. Data is never completely 'raw' and always involves some human judgment. Recognizing this helps us understand how data is shaped and used in decision-making.

[in case you missed it] Data Science Weekly - Issue 260

Data Science Weekly Newsletter • 0 implied HN points • 16 Nov 18
🕹 Technology Data science
  1. There are many resources available for learning machine learning, so it's helpful to gather them in one place for quick access.
  2. Lyft has developed tools to handle seasonal market changes, which could help predict when driver incentives are needed.
  3. Getting a data science job can be tough, but reflecting on the journey can show how previous challenges helped lead to success.

[in case you missed it] Data Science Weekly - Issue 257

Data Science Weekly Newsletter • 0 implied HN points • 27 Oct 18
🕹 Technology Data science
  1. Neural networks can help create new ideas, like unique Halloween costumes. This shows how AI can spark creativity in fun ways.
  2. Uber has built a massive data platform that handles over 100 petabytes of data quickly. This helps them manage and analyze huge amounts of information efficiently.
  3. There are new ways to learn data science, such as hands-on courses with mentoring and payment plans that let you pay after getting a job. This makes it easier for more people to get into the field.

[in case you missed it] Data Science Weekly - Issue 253

Data Science Weekly Newsletter • 0 implied HN points • 29 Sep 18
🕹 Technology Data science
  1. Uber uses machine learning and deep learning to make better forecasts for their products and services. They focus on combining traditional statistical methods with advanced techniques for accurate predictions.
  2. There's a shift in software development where deep learning is automating much of the coding process. Developers now create a basic outline, allowing the computer to generate the code from past examples.
  3. Tiny computers are increasingly replacing larger controllers in technology. This trend highlights the importance of smaller, more efficient computing solutions in the embedded world.

[in case you missed it] Data Science Weekly - Issue 252

Data Science Weekly Newsletter • 0 implied HN points • 22 Sep 18
🕹 Technology Data science
  1. Researchers found a pattern in prime numbers that resembles certain crystal patterns, which could change how we understand them. It's exciting because primes are usually seen as random and mysterious.
  2. DeepMind's AI is being used to improve Android battery life, showing how tech can help make our devices work better. It's important to see if these changes truly benefit users.
  3. Transfer learning allows using knowledge from similar problems to tackle new tasks more easily. This can save time and resources in machine learning projects.

[in case you missed it] Data Science Weekly - Issue 251

Data Science Weekly Newsletter • 0 implied HN points • 15 Sep 18
🕹 Technology Data science
  1. AI systems, like Amazon's Echo, rely on a mix of resources including technology, labor, and nature to work effectively.
  2. Fake news can influence people's voting choices, and there's a mathematical way to show how it happens.
  3. Machine learning tools are becoming easier to use, allowing people to explore and understand models without needing to write code.

[in case you missed it] Data Science Weekly - Issue 247

Data Science Weekly Newsletter • 0 implied HN points • 18 Aug 18
🕹 Technology Data science
  1. Coursera has a new AI tool that helps companies see what skills their employees are learning. This way, workers can find out what skills they might still need.
  2. A small team of student AI coders has achieved success that rivals Google's machine-learning code. This shows that innovative work in AI can come from anywhere, not just big companies.
  3. Facebook's chief AI scientist believes that better collaboration between industry and academia is essential for AI innovation. This partnership can lead to exciting developments in the field.

[in case you missed it] Data Science Weekly - Issue 246

Data Science Weekly Newsletter • 0 implied HN points • 11 Aug 18
🕹 Technology Data science
  1. Data science can balance fast experimentation with careful research. It's important for teams to adapt quickly while also planning for the long term.
  2. Understanding how land in the U.S. is used can highlight ways to create wealth. Different areas have various productive uses that affect the economy.
  3. Automated machine learning tools like Auto-Keras can help people without a data science background to easily access deep learning models.

[in case you missed it] Data Science Weekly - Issue 244

Data Science Weekly Newsletter • 0 implied HN points • 28 Jul 18
🕹 Technology Data science
  1. Companies need to define data science roles clearly, focusing on three areas: Analytics, Inference, and Algorithms. This helps businesses meet their specific needs effectively.
  2. Google's AutoML grabs attention for simplifying machine learning tasks, but understanding concepts like transfer learning is essential to grasp its true potential.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at complex challenges, similar to how humans learn.

[in case you missed it] Data Science Weekly - Issue 239

Data Science Weekly Newsletter • 0 implied HN points • 23 Jun 18
🕹 Technology Data science
  1. AI can argue like a human but it doesn't really understand what it's saying. This raises questions about the limits of AI in communication.
  2. Researchers are working hard to make algorithms fair to avoid biases in machine learning. This is important as technology becomes more involved in our lives.
  3. Experts are discussing how AI and robotics can change healthcare, pointing to a future where technology plays a big role in medicine.

[in case you missed it] Data Science Weekly - Issue 238

Data Science Weekly Newsletter • 0 implied HN points • 16 Jun 18
🕹 Technology Data science
  1. Neural networks can struggle with humor if they don't have enough examples to learn from. More data might help them learn to tell better jokes.
  2. Machine learning is expected to become more effective on smaller devices, like smartphones, thanks to energy-efficient technologies. This could solve many current problems.
  3. Data cleaning is a big part of data science, often taking up to 80% of a person's time. Using tools like Python and Pandas can help make this process easier.

[in case you missed it] Data Science Weekly - Issue 237

Data Science Weekly Newsletter • 0 implied HN points • 08 Jun 18
🕹 Technology Data science
  1. Understanding the brain has improved with maps that show how it processes information, which is helping scientists and neurologists.
  2. The future of work will involve more teamwork between humans and machines, requiring companies to adapt to this changing landscape.
  3. Deep learning methods for object detection have evolved and improved over time, demonstrating how small changes can enhance research and technology.

Data Science Weekly - Issue 210

Data Science Weekly Newsletter • 0 implied HN points • 30 Nov 17
🕹 Technology Data science
  1. Computer vision is making big strides, and it's important to keep track of these changes as they can impact society in various ways.
  2. The idea of an 'intelligence explosion' is challenged, suggesting that it's a misunderstanding of how intelligent systems and self-improving technologies function.
  3. Recent studies indicate that many comments about net neutrality may have been faked, highlighting issues with data integrity and trust in public opinions.

Data Science Weekly - Issue 209

Data Science Weekly Newsletter • 0 implied HN points • 23 Nov 17
🕹 Technology Data science
  1. Flies have a special way of categorizing smells, and researchers are using that idea to improve how computers find similar images.
  2. AI can detect art forgeries by examining just one brushstroke, making the process cheaper and quicker than traditional methods.
  3. Apple is still working on being more open in AI research despite promising to engage more with the academic community last year.

Data Science Weekly - Issue 208

Data Science Weekly Newsletter • 0 implied HN points • 17 Nov 17
🕹 Technology Data science
  1. Neural networks are changing how we develop software, not just being another tool in machine learning. They represent a big shift towards 'Software 2.0' which impacts many projects.
  2. Evolution strategies are a method in machine learning that can be explained visually, making it easier for people to understand how they work.
  3. There is a growing interest in how AI can be used in creative ways, such as in cooking or video games, showcasing its potential beyond traditional applications.

Data Science Resume Advice - Weekly Update #5

Data Science Weekly Newsletter • 0 implied HN points • 08 Jan 16
🚌 Education Data science
  1. Talking to hiring managers can give you great tips for your resume. They have insights on what they really want to see.
  2. You don't need a PhD to get an interview in data science. There are effective ways to stand out even without it.
  3. Feel free to ask questions while putting your resume together. Getting advice can boost your confidence and improve your chances.

Data Science Resume Advice - Weekly Update #1

Data Science Weekly Newsletter • 0 implied HN points • 11 Dec 15
🚌 Education Data science
  1. A resume's main goal is to showcase your skills and experiences effectively. Understanding its purpose can help you focus on what to include.
  2. Being an open-source contributor can be helpful, but it's not the only way to impress hiring managers. There are many paths to demonstrate your skills.
  3. The format of your resume matters a lot. A clean and organized layout makes it easier for employers to read and understand your qualifications.

GroupBy #8: Demystifying the Parquet File, the future of the data engineer, intro to data modeling.

VuTrinh. • 0 implied HN points • 06 Nov 23
🕹 Technology Data science
  1. The Parquet file format is becoming popular for data storage because it is efficient and works well with big data tools. Understanding how to use it can help data engineers be more effective.
  2. Data engineering is evolving, and new trends like data mesh are changing how data platforms are built. Keeping up with these changes is important for anyone in the field.
  3. Starting a small data engineering project can be a great way to learn new skills. Even a quick project can teach you important techniques, like web scraping and using cloud storage.

GroupBy #2

VuTrinh. • 0 implied HN points • 15 Sep 23
🕹 Technology Data science
  1. The Lakehouse concept combines the best features of data lakes and data warehouses. It's a new way to manage and analyze data effectively.
  2. Good data quality is essential for making AI work. If the data is bad, the results will also be poor.
  3. AI tools might help data teams work more efficiently, but they won't reduce the demand for data professionals. In fact, they might increase it.

Geometry-Aware Hashing of GeoJSON objects

Altay's Blog • 0 implied HN points • 26 May 24
🕹 Technology Data science
  1. Hashing can make comparing large lists of GeoJSON objects faster and easier. It lets you quickly find duplicates without much effort.
  2. GeoJSON shapes, like polygons and lines, can be represented by different sets of coordinates. This means we need a special way to hash them to recognize their shape, not just the order of points.
  3. To achieve this, we can create a consistent way to choose starting points or reorder coordinates. This way, the hash will stay the same, even if the coordinates are ordered differently.

How to Calculate Image Similarity Using Python

Curious Devs Corner • 0 implied HN points • 12 Jul 24
🕹 Technology Data science
  1. You can easily compare images using Python with the image-similarity-measures library. It has different ways to measure how similar two images are.
  2. The library supports eight different methods to evaluate image similarity, like RMSE and SSIM. You just need to pick one and pass your images to it.
  3. You can run comparisons quickly from your terminal or create a Python script. It's a straightforward way to find out how similar different images are.

#21 - Hannah Ritchie | Why We Aren't Doomed by Climate Change

Win-Win • 0 implied HN points • 04 May 24
🌞 Climate & Environment Data science
  1. We need to rethink what sustainability really means. It's not just about cutting back, but finding ways to improve our lives while being kind to the planet.
  2. Some climate problems that we worry about might not be as big as they seem, while others are more serious than we think. It's important to look at the facts.
  3. There are successful ideas and technologies out there that can help us tackle environmental issues. We can aim for solutions that benefit both the environment and our quality of life.

HN blogs - 31/10/24

HackerNews blogs newsletter • 0 implied HN points • 01 Nov 24
🕹 Technology Data science
  1. Women in tech face unique challenges, and it's important to support them in their careers. Encouraging diversity can lead to better teams and ideas.
  2. Understanding what makes a good problem is key to effective problem solving. It's not just about fixing issues, but knowing which problems to tackle.
  3. Typing speed isn't everything when it comes to being productive. Sometimes, taking your time can lead to better thinking and results.