The hottest Software Development Substack posts right now

And their main takeaways
Category
Top Technology Topics

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

Data Science Weekly Newsletter • 0 implied HN points • 28 Mar 21
🕹 Technology Software Development
  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 378

Data Science Weekly Newsletter • 0 implied HN points • 21 Feb 21
🕹 Technology Software Development
  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 Software Development
  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 Software Development
  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 368

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[in case you missed it] Data Science Weekly - Issue 344

Data Science Weekly Newsletter • 0 implied HN points • 28 Jun 20
🕹 Technology Software Development
  1. As AI and autonomous systems grow, figuring out who is responsible for problems is important. We need to think about who is accountable when things go wrong.
  2. Scientists discovered that a long earthquake swarm was likely caused by natural fluids in the earth. This finding shows how detailed studies can help us understand complex natural events.
  3. The landscape of machine learning tools is extensive but still developing. A recent analysis of over 200 tools revealed both challenges and opportunities for those in the field.

[in case you missed it] Data Science Weekly - Issue 343

Data Science Weekly Newsletter • 0 implied HN points • 21 Jun 20
🕹 Technology Software Development
  1. Image GPT can create images just like large language models create text. This means we can now generate detailed images by understanding pixel patterns.
  2. MLOps helps data scientists work better together by automating tasks like testing and version control. This makes it easier to manage machine learning projects.
  3. There is no proper regulation for algorithms that affect our daily lives. A group of citizens should help oversee how these algorithms are used to ensure fairness and accountability.

[in case you missed it] Data Science Weekly - Issue 341

Data Science Weekly Newsletter • 0 implied HN points • 07 Jun 20
🕹 Technology Software Development
  1. Data scientists are in high demand, so it's important to know how to market yourself effectively. Building a strong project portfolio can make you an attractive candidate.
  2. Recent advancements in language models, like GPT-3, show that larger models can perform tasks with fewer examples. This could change how we approach natural language processing.
  3. Managing expectations in data science jobs is crucial, especially for newcomers. Many people feel disappointed because they might not understand the job's realities.

[in case you missed it] Data Science Weekly - Issue 340

Data Science Weekly Newsletter • 0 implied HN points • 31 May 20
🕹 Technology Software Development
  1. AI has some issues that limit its usefulness in businesses. By understanding these problems, businesses can find ways to effectively use AI and even save money.
  2. Human and machine cooperation is essential, and fully automating processes might not be the best approach. We should find ways for machines and people to work better together.
  3. Learning about basic machine learning models is still very important. Many companies don't need advanced techniques, so knowing the basics can help you in real-world jobs.

[in case you missed it] Data Science Weekly - Issue 338

Data Science Weekly Newsletter • 0 implied HN points • 17 May 20
🕹 Technology Software Development
  1. AR and AI can merge to create tools for editing images by cutting and pasting elements from our surroundings. This could revolutionize how we visually manipulate content.
  2. Researchers are working on mapping the human brain's connections to better understand how it functions and what happens when it gets sick. This could lead to major breakthroughs in neuroscience.
  3. Active learning techniques in AI can make label management easier by tracking what data has been labeled and what still needs attention. This saves time and reduces errors during data annotation.

[in case you missed it] Data Science Weekly - Issue 336

Data Science Weekly Newsletter • 0 implied HN points • 02 May 20
🕹 Technology Software Development
  1. Tornado plots are a unique way to display time series data, showing how values change over time in a more dynamic way. They help visualize trends in a more engaging format.
  2. An open-source chatbot named Blender, developed by Facebook, is designed to be more human-like in conversations. It is the largest chatbot model available and can be used by other researchers.
  3. The use of machine learning (ML) for optimizing chip design is becoming important as hardware needs to keep up with advancing technology. It could help speed up the design process significantly.

[in case you missed it] Data Science Weekly - Issue 335

Data Science Weekly Newsletter • 0 implied HN points • 26 Apr 20
🕹 Technology Software Development
  1. Specification gaming can happen in AI when systems find shortcuts to achieve goals without actually completing the intended task. This is a problem we need to address in AI design.
  2. There’s a lot of gender bias in machine translation, which reflects societal issues. Companies like Google are trying to reduce these biases in their systems.
  3. Training large NLP models is expensive and requires careful budgeting. Understanding these costs can help developers plan better for their projects.

[in case you missed it] Data Science Weekly - Issue 332

Data Science Weekly Newsletter • 0 implied HN points • 04 Apr 20
🕹 Technology Software Development
  1. Agent57 is a new AI that can play all 57 Atari games better than humans, showing how deep reinforcement learning can achieve high performance in gaming tasks.
  2. During the COVID-19 crisis, it's important for everyone to approach discussions about data and health with curiosity and honesty, especially if they aren't experts.
  3. ACM is offering free access to their digital library for three months to support researchers and learners during the pandemic, promoting resource sharing in the computing community.

[in case you missed it] Data Science Weekly - Issue 329

Data Science Weekly Newsletter • 0 implied HN points • 14 Mar 20
🕹 Technology Software Development
  1. Human-in-the-Loop Machine Learning helps reduce bias and improve accuracy by involving people in the decision-making process.
  2. Google’s wearable technology analyzes sports performance in real-time, showing how AI can enhance athletic training.
  3. Reinforcement learning can be applied to complex tasks like trading, learning strategies to maximize rewards in dynamic environments.

[in case you missed it] Data Science Weekly - Issue 327

Data Science Weekly Newsletter • 0 implied HN points • 01 Mar 20
🕹 Technology Software Development
  1. Deep learning can help discover new antibiotics, which is really important as antibiotic resistance grows. By using neural networks, scientists found a new molecule that fights a variety of pathogens.
  2. Ethics in AI is becoming essential, especially for technologies that can operate without human intervention. It's important to think about the potential consequences and applications of AI, like in self-driving cars.
  3. Data science skills are in high demand, and there are growing opportunities in various sectors, especially in eCommerce and healthcare. Companies are looking for talent to drive innovations and improve operations.

[in case you missed it] Data Science Weekly - Issue 326

Data Science Weekly Newsletter • 0 implied HN points • 22 Feb 20
🕹 Technology Software Development
  1. AI businesses are different from traditional software companies. They often have different costs and profit structures, resembling more of a service model.
  2. Spotify's Wrapped campaign is a major marketing success that reflects user listening habits over the decade. It was challenging for the engineering team to accomplish this unique data display.
  3. Algorithmic bias is being actively addressed through explainable AI, aiming to make AI decisions fairer and less prejudiced against certain groups.

[in case you missed it] Data Science Weekly - Issue 325

Data Science Weekly Newsletter • 0 implied HN points • 15 Feb 20
🕹 Technology Software Development
  1. AI is changing many industries, showing potential in areas like healthcare and self-driving cars. Economists are studying how this technology will affect jobs and the economy.
  2. There are guides available for anyone looking to get into data science. These resources can help you decide what skills you need, build a strong portfolio, and create a standout resume.
  3. Research in machine learning is advancing rapidly, with new models for tasks like seeing transparent objects and improving supply chains. These innovations could lead to smarter, more flexible systems in our daily lives.

[in case you missed it] Data Science Weekly - Issue 322

Data Science Weekly Newsletter • 0 implied HN points • 25 Jan 20
🕹 Technology Software Development
  1. The Smulemates project suggests a feature for the karaoke app Smule to help users find singing partners that match their style. This could make karaoke more social and enjoyable.
  2. Facebook AI introduced a new method for teaching machines to navigate in real environments without maps. This could lead to better robots that understand complex spaces, helping them perform tasks with ease.
  3. A tool called Manifold was released as open source to help find problems in machine learning models. It allows users to visually debug and improve their models more efficiently.

[in case you missed it] Data Science Weekly - Issue 311

Data Science Weekly Newsletter • 0 implied HN points • 09 Nov 19
🕹 Technology Software Development
  1. Neural networks are becoming smarter by mimicking biological strategies. This could be key to creating truly intelligent machines.
  2. When regulating AI, we should focus on its real-world effects rather than just on fears surrounding the technology.
  3. There are significant challenges in applying AI to healthcare, and we need more examples of successful technology implementation in clinical practice.

[in case you missed it] Data Science Weekly - Issue 308

Data Science Weekly Newsletter • 0 implied HN points • 20 Oct 19
🕹 Technology Software Development
  1. Neural networks can solve real-world problems like a robot hand solving a Rubik's Cube. This shows they can learn and adapt in unpredictable situations.
  2. There's a shift happening in machine learning tools, with more researchers choosing PyTorch over TensorFlow. While TensorFlow is still popular in the industry, this could change soon.
  3. Companies can use a smart model to find the best regions for hiring offshore talent. This helps them build stronger remote teams by targeting specific skills.

[in case you missed it] Data Science Weekly - Issue 307

Data Science Weekly Newsletter • 0 implied HN points • 12 Oct 19
🕹 Technology Software Development
  1. AI needs to learn how to explain its decisions. A leading expert believes understanding the reasons behind AI's choices is important.
  2. Data science is increasingly used in different fields, even fashion. Scientists are applying their skills to help with style choices and personal recommendations.
  3. Small AI models can make everyday technology, like autocorrect and voice assistants, faster and more efficient.

[in case you missed it] Data Science Weekly - Issue 306

Data Science Weekly Newsletter • 0 implied HN points • 06 Oct 19
🕹 Technology Software Development
  1. Data scientists have many job opportunities, and the demand for their skills is increasing in various industries.
  2. AI is being used in innovative ways, like helping people choose outfits or teach machines to plan actions using natural language.
  3. Stabilizing techniques for training Generative Adversarial Networks (GANs) are important because they help prevent issues that can arise during the training process.

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

Data Science Weekly Newsletter • 0 implied HN points • 01 Sep 19
🕹 Technology Software Development
  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 Software Development
  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 Software Development
  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.

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

Data Science Weekly Newsletter • 0 implied HN points • 13 Jul 19
🕹 Technology Software Development
  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 280

Data Science Weekly Newsletter • 0 implied HN points • 06 Apr 19
🕹 Technology Software Development
  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 Software Development
  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 Software Development
  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 260

Data Science Weekly Newsletter • 0 implied HN points • 16 Nov 18
🕹 Technology Software Development
  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 Software Development
  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 Software Development
  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.