The hottest Data science Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 387

Data Science Weekly Newsletter • 19 implied HN points • 22 Apr 21
🕹 Technology Data science
  1. Goodreads is a huge platform for readers where they discuss what makes a book a 'classic.' It shows how engaging with books online can shape opinions and communities.
  2. Scientists are using AI to decode whale language, which could help us understand more about these intelligent creatures and their communication.
  3. Neural networks are getting better at solving complex math problems quickly, making it easier to model complicated systems in science and engineering.

Indian AI Startups Raised $836.3 Million In 2020

Sector 6 | The Newsletter of AIM • 19 implied HN points • 28 Feb 21
🕹 Technology Data science
  1. In 2020, Indian AI startups were able to raise a significant amount of $836.3 million. This shows a strong interest and investment in artificial intelligence in India.
  2. Kubernetes is linked to the success of AI technologies like GPT-3. It helps in managing software applications efficiently, which is crucial for AI development.
  3. There is a growing focus on the hiring process for data scientists in India. Companies are sharing their recruitment strategies, making it easier for aspiring data scientists to understand what to expect.

Data Science Weekly - Issue 386

Data Science Weekly Newsletter • 19 implied HN points • 15 Apr 21
🕹 Technology Data science
  1. Accessibility in data visualization is important. Tools like Chartability help ensure that everyone can understand data, especially people with disabilities.
  2. Graph Neural Networks (GNNs) are a powerful tool for analyzing data, but their effectiveness can vary depending on how they use features and edges.
  3. There's a growing need for data observability. Companies must ensure data quality and avoid issues like missing or duplicate data as they handle more complex data pipelines.

More Than 1 In 5 Analytics Teams Grew In India Amid The Pandemic

Sector 6 | The Newsletter of AIM • 19 implied HN points • 21 Feb 21
🕹 Technology Data science
  1. More than 20% of analytics teams in India saw growth during the pandemic. This shows a rising interest in data analysis roles.
  2. Data science education is a huge market in India, nearing a billion dollars. But many people feel confused about which courses to take due to too many options.
  3. There are lots of different course names and structures, making it hard for learners to choose the best fit for their needs. A clearer platform for education could help.

Data Science Weekly - Issue 385

Data Science Weekly Newsletter • 19 implied HN points • 08 Apr 21
🕹 Technology Data science
  1. Building a machine learning rig can be a fun project. It involves planning and buying the right hardware, especially GPUs.
  2. Data observability is crucial for businesses using large data sets. It helps ensure data quality and reduces issues in complex data pipelines.
  3. Using deep learning and automation can simplify tasks like monitoring bird nests. This can save time and keep track of nature without constant watching.
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Data Science Weekly - Issue 384

Data Science Weekly Newsletter • 19 implied HN points • 01 Apr 21
🕹 Technology Data science
  1. Maps are getting smarter with AI, offering real-time updates for traffic and information. This makes navigation easier and more efficient than ever before.
  2. It's important to stop labeling everything as AI. We need to focus more on creating useful machine learning systems that actually help people.
  3. Using data effectively can be tricky. Numbers can greatly influence policy, but relying solely on them can lead to problems.

The Belamy is the weekly newsletter of Analytics India Magazine. We share a curated list of best stories on AI & machine learning

Sector 6 | The Newsletter of AIM • 19 implied HN points • 07 Feb 21
🕹 Technology Data science
  1. The Belamy newsletter shares top stories about AI and machine learning each week. It's a great way to stay updated in these fast-changing fields.
  2. Analytics India Magazine also highlights important technological advancements in analytics, data science, and big data. This helps readers understand new trends and innovations.
  3. You can sign up for a free trial to explore the newsletter's archives. This is a good chance to see if the content is a good fit for you.

Data Science Weekly - Issue 383

Data Science Weekly Newsletter • 19 implied HN points • 25 Mar 21
🕹 Technology Data science
  1. Artificial intelligence is making big strides in drug discovery, helping researchers tackle important problems more effectively. It's great to see technology playing a role in improving health outcomes.
  2. Jupyter notebooks are a popular tool among data scientists for data analysis and exploration, but some find them tricky to manage in production environments. It's a love/hate relationship for many users.
  3. Machine learning is becoming a key player in game development, helping to test and balance games more efficiently. This could lead to better gaming experiences for everyone.

AI Explained: Retrieval-Augmented Generation (RAG)

Year 2049 • 8 implied HN points • 26 Jan 24
🕹 Technology Data science
  1. RAG solves problems with AI like hallucinations, outdated knowledge, being too general, and privacy concerns
  2. RAG allows for retrieving specific knowledge, adding new updated documents easily, and not training the AI on your data
  3. RAG can be used to create assistants for tasks like onboarding new employees, customer service, coding, and design, improving productivity through knowledge access

Deep Learning Is Better Than Linear Regression

As Clay Awakens • 2 HN points • 19 Mar 23
🕹 Technology Data science
  1. Linear regression is a reliable, stable, and simple technique with a long history of successful applications.
  2. Deep learning, especially non-linear regression, has shown significant advancements over the past decade and can outperform linear regression in many real-world tasks.
  3. Deep learning models have the ability to automatically learn and discover complex features, making them advantageous over manually engineered features in linear regression.

Data Science Weekly - Issue 382

Data Science Weekly Newsletter • 19 implied HN points • 18 Mar 21
🕹 Technology Data science
  1. Computers will never truly understand or create good literature. They lack the ability to appreciate and express the complexities of human writing.
  2. Color scales are important in data visualization. Choosing the right color can make your data easier to understand and communicate.
  3. Data documentation and organization are crucial for effective data management. Having a clear framework helps teams work better and ensures everyone understands the data.

Data Science Weekly - Issue 381

Data Science Weekly Newsletter • 19 implied HN points • 11 Mar 21
🕹 Technology Data science
  1. COVID-19 skeptics use data and social media to promote their views. A study analyzed tweets and visual data to uncover their strategies.
  2. New reports on AI development show that the COVID-19 pandemic has impacted research and hiring in this field. It highlights how AI technology is being utilized in health-related areas.
  3. Machine learning can struggle with new data it wasn't trained on. Research is ongoing to improve its reliability and performance in real-world situations.

Data Science Weekly - Issue 380

Data Science Weekly Newsletter • 19 implied HN points • 04 Mar 21
🕹 Technology Data science
  1. Managing up is about sharing important facts with your manager to improve teamwork. It helps them understand what's slowing you down and what support you need.
  2. Data discovery platforms are evolving from traditional data catalogs, focusing on better ways to understand data context. This helps users find and utilize data more effectively.
  3. Generative adversarial transformers are a new kind of model that can produce high-quality visuals while being more efficient in computation. They could enhance creativity in visual content creation.

Data Science Weekly - Issue 379

Data Science Weekly Newsletter • 19 implied HN points • 25 Feb 21
🕹 Technology Data science
  1. Writing a book on data science can be a fun way to inspire others to use data in their lives. The process can feel challenging but is ultimately rewarding.
  2. Learning about Python concurrency can be tricky but understanding it is important for data scientists moving into software engineering roles. Engaging with live coding talks can clarify complex concepts.
  3. Feature stores are becoming essential for managing machine learning data and making it easier to deploy models. They help data scientists collaborate and quickly get their work into production.

Data Science Weekly - Issue 378

Data Science Weekly Newsletter • 19 implied HN points • 18 Feb 21
🕹 Technology Data science
  1. Creating morals in robots can be similar to parenting techniques, which raises interesting questions about how we teach values to machines.
  2. There is a growing collection of data science podcasts available, making it easy for enthusiasts to find quality content and stay updated in the field.
  3. Research is exploring better and more stable methods for training neural networks, which could improve how computers learn and function like human brains.

Data Science Weekly - Issue 377

Data Science Weekly Newsletter • 19 implied HN points • 11 Feb 21
🕹 Technology Data science
  1. Machine learning is being used in interesting ways, like tracking pets at home with Bluetooth and specialized detectors. It's cool to see technology helping us keep track of our furry friends.
  2. There's a shift from using Excel to Python in industries that need tech improvements. Companies are finding that Python can handle complex tasks and data much better than traditional methods.
  3. Active learning in machine learning helps reduce the amount of labeled data needed to train models. By letting the model ask questions about uncertain data, it learns more efficiently.

Data Science Weekly - Issue 376

Data Science Weekly Newsletter • 19 implied HN points • 04 Feb 21
🕹 Technology Data science
  1. Data quality is super important for AI, especially in high-stakes situations like medical diagnoses. Poor data can lead to serious mistakes in predictions.
  2. DanNet revolutionized deep learning by being the first successful deep CNN in competitions. Its success marked a turning point in computer vision.
  3. Cohort analysis is a powerful way to examine customer data over time, helping businesses improve their user engagement and marketing strategies.

Data Science Weekly - Issue 375

Data Science Weekly Newsletter • 19 implied HN points • 28 Jan 21
🕹 Technology Data science
  1. When building a machine learning team, it's important to adapt the team's structure as projects grow. Start small, but be ready to scale up as your needs change.
  2. Creating machine learning systems that can generalize well requires us to use observations to make inferences. This process, known as induction, helps build smarter algorithms.
  3. Machine learning is now being applied to modeling audio equipment, which could change the way we think about sound and effects in music production.

July Newsletter

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

Data Science Weekly - Issue 374

Data Science Weekly Newsletter • 19 implied HN points • 21 Jan 21
🕹 Technology Data science
  1. Controlled experiments are important for understanding the impact of new features in software. They help ensure that changes actually improve user experience and metrics.
  2. Deep learning is being used in various scientific fields, making tools like DeepChem important for democratizing access to advanced technologies. This helps researchers across disciplines like chemistry and bioinformatics.
  3. There are innovative methods for diagnosing diseases like prostate cancer using AI. These techniques can offer high accuracy and reduce the need for invasive procedures.

Data Science Weekly - Issue 373

Data Science Weekly Newsletter • 19 implied HN points • 14 Jan 21
🕹 Technology Data science
  1. Machine learning is being used a lot in developmental biology. It helps scientists work with big data from things like images and gene studies, making analysis easier.
  2. There's a growing need for data engineers, with many companies looking for these roles. Focusing on engineering skills can open up more job opportunities than traditional data scientist roles.
  3. The U.S. government has started an initiative to promote and oversee artificial intelligence. This shows how important AI is to the economy and security of the nation.

Data Science Weekly - Issue 372

Data Science Weekly Newsletter • 19 implied HN points • 07 Jan 21
🕹 Technology Data science
  1. DALL·E is a powerful AI that creates images from text descriptions, showcasing its ability to combine different ideas and concepts in creative ways.
  2. Machine learning is making significant strides in healthcare, but it also comes with risks that need careful consideration to ensure patient safety.
  3. Transformers have revolutionized natural language processing and are now being applied to various tasks in computer vision, improving how we manage data.

Data Science Weekly - Issue 371

Data Science Weekly Newsletter • 19 implied HN points • 31 Dec 20
🕹 Technology Data science
  1. Real-time machine learning is becoming important for many companies. Some have invested heavily in the right infrastructure and are seeing good results.
  2. There are many new tools for machine learning and MLOps. Keeping track of these tools can help in improving workflow and project success.
  3. Understanding concepts like Markov models can help in planning routines, such as workouts, based on previous choices. This helps in making smart decisions about what to do next.

Quantum ML Journey: A Fireside Chat with Amira Abbas

Deep-Tech Newsletter • 19 implied HN points • 20 Oct 20
🕹 Technology Data science
  1. The course aims to help reduce the mathematical barrier to Quantum Computation for software engineering and data science professionals interested in the field.
  2. Encouraging and nurturing professionals from diverse backgrounds, like Amira Abbas, in the deep-tech industry can lead to innovation and potential for emerging technologies.
  3. There is untapped talent in Africa, and providing education, support, and opportunities can unlock brain power for the deep-tech industry on the continent.

Data Science Weekly - Issue 370

Data Science Weekly Newsletter • 19 implied HN points • 24 Dec 20
🕹 Technology Data science
  1. NeRF technology made big waves in 2020, changing how we render 3D images with neural networks. It’s a cool new area in data science that’s just starting to grow.
  2. DeepMind's MuZero AI is impressive because it learns the rules of games by itself, improving how we analyze videos. This could lead to cost cuts for platforms like YouTube.
  3. If you're looking to start a career in data science, there are practical guides available. These can help you with everything from filling knowledge gaps to creating a strong portfolio.

Super Weights in LLMs - How Pruning Them Destroys a LLM's Ability to Generate Text ?

Machine Learning Diaries • 3 implied HN points • 18 Nov 24
🕹 Technology Data science
  1. Super weights are very important for how well large language models (LLMs) perform. Even though they're a tiny part of the model, they can greatly affect the results.
  2. If a super weight is removed, it can ruin the model's ability to generate clear text and make predictions. Just taking out one of these weights can cause a huge drop in performance.
  3. Removing regular outlier weights doesn't harm performance much, but losing just one super weight is much worse than taking out a lot of other weights combined.

Data Science Weekly - Issue 369

Data Science Weekly Newsletter • 19 implied HN points • 17 Dec 20
🕹 Technology Data science
  1. Companies are changing how they share information because of AI. They're making their reports easier for machines to read, which can influence market behavior.
  2. Monitoring machine learning models is essential for maintaining accuracy. It's important to detect issues like outliers and changes in data patterns in real-time.
  3. Deep learning research often helps engineers tackle real-world problems effectively. Insights from recent research can guide better practices in building and deploying models.

June Newsletter

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

Data Science Weekly - Issue 368

Data Science Weekly Newsletter • 19 implied HN points • 10 Dec 20
🕹 Technology Data science
  1. Machine learning needs systematic approaches to create strong systems for real-world use. This means looking beyond just algorithms to see the bigger picture.
  2. Deep neural networks are powerful, but understanding how they work can be tricky. Tools like network dissection can help us figure out what these networks are really doing.
  3. Feature stores are becoming important for machine learning. They allow teams to share and manage data better for creating and deploying models quickly.

Data Science Weekly - Issue 367

Data Science Weekly Newsletter • 19 implied HN points • 03 Dec 20
🕹 Technology Data science
  1. AlphaFold is a huge breakthrough in biology that helps solve the protein folding problem, which has puzzled scientists for 50 years. It shows how AI can speed up scientific discovery.
  2. Spotify needs good tools to make sense of its massive data from millions of users. Designing user-friendly data tools is key for them to understand and improve their services.
  3. Having high-quality data is essential for companies. New technologies can help businesses maintain data quality without spending huge amounts of money.

Data Science Weekly - Issue 366

Data Science Weekly Newsletter • 19 implied HN points • 26 Nov 20
🕹 Technology Data science
  1. Pinterest improved its machine learning signals by updating its data infrastructure. They moved from a Lambda architecture to a Kappa architecture for better real-time performance.
  2. DoorDash built a feature store to handle the massive amounts of data needed for its machine learning models. This helps them manage costs and maintain fast performance when retrieving data.
  3. When choosing between a data lake, warehouse, or lakehouse, it's important to consider the specific needs of your data platform. The right choice depends on the tools that best fit your project requirements.

How to implement a decision tree

The Palindrome • 3 implied HN points • 08 Nov 24
🕹 Technology Data science
  1. A decision tree splits data based on features and thresholds, which helps in making predictions by creating branches. Each split leads to two outcomes based on whether the condition is met or not.
  2. Gini impurity is a key measure for evaluating how 'pure' the labels are in each leaf of the tree. A lower Gini impurity means better predictability for a leaf's classification.
  3. You can create both classification and regression trees by changing how you score the splits and define the predictions in the leaves. This flexibility allows for various applications in data analysis.

Data Science Weekly - Issue 365

Data Science Weekly Newsletter • 19 implied HN points • 19 Nov 20
🕹 Technology Data science
  1. It's important to connect with AI researchers as people, not just through their work. Personal stories can give better insights into their lives and motivations.
  2. Dynamic data testing is crucial for effective data analysis. Unlike software testing, data needs flexible tests that can adjust as it changes.
  3. Creating open datasets for sound events helps improve research in machine learning. These datasets can provide valuable resources for training models.

Will LLMs Make NLP Scientists Jobless?

Pratik’s Pakodas 🍿 • 12 implied HN points • 21 Mar 23
🕹 Technology Data science
  1. Technological progress leads to job displacement but also creates new opportunities.
  2. Understanding when and where to use LLMs is crucial for NLP engineers to deliver value.
  3. NLP engineers may see a shift from the need for researchers to the demand for full-stack engineers due to advancements in LLM technology.

Data Science Weekly - Issue 364

Data Science Weekly Newsletter • 19 implied HN points • 12 Nov 20
🕹 Technology Data science
  1. Organizing data in spreadsheets can help prevent errors and make analysis easier. It's important to keep a consistent format and to avoid leaving any empty cells.
  2. AI is being used to create music that sounds like famous artists, which could change the music industry. This technology raises questions about copyright and authenticity.
  3. Monitoring tools are becoming essential for data scientists to track their models for performance and integrity. These tools help ensure that models are accurate and reliable over time.

Data Science Weekly - Issue 363

Data Science Weekly Newsletter • 19 implied HN points • 05 Nov 20
🕹 Technology Data science
  1. Synthetic biology has gained a lot of attention over the past decade, and it's been evolving to deliver real technologies and breakthroughs.
  2. Data poisoning is a serious concern in machine learning, as bad data can manipulate model predictions, especially with NLP models.
  3. Managing data for machine learning projects is challenging, but using version control tools can help keep things organized and prevent unexpected issues.

Data Science Weekly - Issue 362

Data Science Weekly Newsletter • 19 implied HN points • 29 Oct 20
🕹 Technology Data science
  1. Form extraction using AI can help important fields like journalism and medicine by accurately pulling data from documents. This can significantly improve research and decision-making.
  2. Data engineering is crucial and involves gathering, cleaning, and shaping data before it's analyzed. It's just as important as data science, which builds on that data to create insights and models.
  3. Dealing with data imbalance can be tricky, but using semi-supervised and self-supervised learning techniques can improve model performance. These methods help when some categories have much less data than others.