The hottest Deep Learning Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 372

Data Science Weekly Newsletter • 19 implied HN points • 07 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence NLP Deep Learning
  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 368

Data Science Weekly Newsletter • 19 implied HN points • 10 Dec 20
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Data Tools
  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.

Deep Learning Platform, TinyML, Privacy ↔ Contact Tracing

Gradient Flow • 19 implied HN points • 07 May 20
🕹 Technology Deep Learning Machine Learning COVID-19 Virtual Conferences Work
  1. Deep learning models are being implemented in tiny devices with tools like TinyML for ultra-low-power systems.
  2. Distributed training for deep learning models is made simpler and cheaper with libraries like RaySGD.
  3. Technology like facial recognition for contact tracing can also raise concerns about privacy and mass surveillance.

Data Science Weekly - Issue 349

Data Science Weekly Newsletter • 19 implied HN points • 30 Jul 20
🕹 Technology Data science Machine Learning Artificial Intelligence Neuroscience Deep Learning
  1. Deep learning has important ideas that have been around for a while. If you're new to it, learning these basics can really help you understand current research.
  2. GPT-3 is creating a lot of buzz, and it's important to think critically about the hype. Understanding the difference between hype and reality helps us navigate new technologies better.
  3. Evaluating machine learning models is similar to testing software. New methods can help us better assess how well these models work, which is key to making them reliable.

Data Science Weekly - Issue 348

Data Science Weekly Newsletter • 19 implied HN points • 23 Jul 20
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Software Development
  1. Deep Learning papers can be confusing for beginners, but there's a roadmap to help you choose where to start. It's a good way to navigate through the vast amount of research out there.
  2. Machine Learning is creating a lot of value for businesses, and it's important to understand how this value can be captured. Different companies are finding unique ways to apply ML for their needs.
  3. New techniques in AI, like using neural networks for soundscapes, are not just tech innovations but can also help protect the environment. It shows how technology can contribute to nature conservation.
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Data Science Weekly - Issue 332

Data Science Weekly Newsletter • 19 implied HN points • 02 Apr 20
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Computing
  1. Agent57 is a new deep learning agent that can beat human scores in all Atari games. It's a big step forward in how we measure AI performance.
  2. During the COVID-19 crisis, it's important to approach data honestly and with curiosity. This helps individuals responsibly discuss topics outside their expertise.
  3. ACM is offering free access to their digital library to support research and learning during the pandemic. This allows more people to access valuable computing resources.

Data Science Weekly - Issue 323

Data Science Weekly Newsletter • 19 implied HN points • 30 Jan 20
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Programming
  1. Data cleaning is a big part of a data scientist's job. Many great ideas can get stuck because people can't access or use the right data.
  2. Choosing the right settings, called hyperparameters, greatly impacts a machine learning project's success. There are smarter ways to find these settings than just guessing.
  3. Learning is easier when it's structured step by step. Using a curriculum helps models learn complex tasks bit by bit, just like how people learn.

How is LLaMa.cpp possible?

Artificial Fintelligence • 4 HN points • 16 Mar 23
🕹 Technology Deep Learning Inference Quantization Performance analysis
  1. Large deep learning models like LLaMa can run locally on a variety of hardware with optimizations and weight quantization.
  2. Memory bandwidth is crucial for deep learning GPUs, with memory being the bottleneck for inference performance.
  3. Quantization can significantly reduce memory requirements for models, making them more manageable to serve, especially on GPUs.

Data Science Weekly - Issue 300

Data Science Weekly Newsletter • 19 implied HN points • 22 Aug 19
🕹 Technology AI Data science Machine Learning Deep Learning Big Data
  1. Adversarial Fashion aims to confuse surveillance cameras by using items like license plates. This shows how fashion can be used to challenge technology.
  2. A new AI optimizer called RAdam can improve accuracy for various AI models. It's a helpful update for anyone working with machine learning.
  3. Deep learning is making waves in genetics, showing that it can help explore DNA. This opens new possibilities for understanding and working with genetic data.

Data Science Weekly - Issue 295

Data Science Weekly Newsletter • 19 implied HN points • 18 Jul 19
🕹 Technology Data science Machine Learning Artificial Intelligence Natural Language Processing Deep Learning
  1. Netflix is moving away from traditional collaborative filtering methods to improve its recommendation system.
  2. Using AI and natural language processing (NLP) can help companies better understand and meet customer requests.
  3. It's important to audit AI systems to check for bias, especially when making significant decisions like loans or legal verdicts.

Data Science Weekly - Issue 288

Data Science Weekly Newsletter • 19 implied HN points • 30 May 19
🕹 Technology AI Machine Learning Data science Deep Learning Cloud Computing Computer Vision
  1. Creating general artificial intelligence might be possible through AI-generating algorithms, which could be a better approach than manually piecing together intelligence components.
  2. Generative adversarial networks (GANs) could greatly change the fashion industry by allowing realistic digital models to replace human models in online shopping.
  3. Recent advances in AI technology are enabling more efficient processing on devices, reducing the need for powerful cloud machines and making AI applications more accessible.

Data Science Weekly - Issue 268

Data Science Weekly Newsletter • 19 implied HN points • 10 Jan 19
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Software Development
  1. Being a specialist is important in data science. It's better to focus on a specific area rather than trying to know a little about everything.
  2. Machine learning research often takes a long time to reach actual industries. Many cutting-edge advancements are not quickly applied in real-world scenarios.
  3. Understanding practical skills is crucial for success in machine learning jobs. Many candidates lack essential skills that aren't taught in standard courses.

Data Science Weekly - Issue 265

Data Science Weekly Newsletter • 19 implied HN points • 20 Dec 18
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Computer Vision
  1. AlphaZero is a powerful AI that learns board games like chess and Go from scratch, showing how quickly it can master complex games without prior knowledge.
  2. Building a deep learning system requires careful choice of hardware, and it's important to avoid overspending on unnecessary components.
  3. Collaboration between data science and engineering has challenges, but understanding these tension points can improve teamwork and model deployment.

Data Science Weekly - Issue 263

Data Science Weekly Newsletter • 19 implied HN points • 06 Dec 18
🕹 Technology AI Data science Machine Learning Analytics Deep Learning
  1. Deep learning is rapidly evolving, and it's important to track these changes to stay updated in the field.
  2. AI is changing jobs; while some roles may vanish, there is a growing demand for skilled professionals who can work with AI.
  3. Machine learning is being used in creative ways, like predicting grocery item availability and generating addresses from satellite images.

Data Science Weekly - Issue 262

Data Science Weekly Newsletter • 19 implied HN points • 29 Nov 18
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Healthcare Tech
  1. GANPaint allows you to create art by controlling specific objects in a scene using AI. It's an innovative way to draw, making it easier to express complex ideas visually.
  2. Uber AI has made significant progress in teaching AI to play challenging video games like Montezuma's Revenge. This shows how AI can learn and improve in tough scenarios without much human help.
  3. Amazon Comprehend Medical uses AI to understand medical language, which can help healthcare professionals work better. It's designed to help with everything from medical terms to complex procedures.

Data Science Weekly - Issue 254

Data Science Weekly Newsletter • 19 implied HN points • 04 Oct 18
🕹 Technology Data science Machine Learning AI Data Visualization Deep Learning
  1. You can calculate the age of the universe using SQL to analyze data from various databases. It's easier than it sounds and can lead to interesting insights.
  2. Training deep learning models on phones and other small devices is now possible but still challenging. There are teams making it work, but the tools available aren't very user-friendly yet.
  3. Big data is starting to change genetic research a lot. New techniques are creating huge amounts of data, which helps scientists discover new things but also keeps them busy trying to catch up.

Data Science Weekly - Issue 237

Data Science Weekly Newsletter • 19 implied HN points • 07 Jun 18
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Deep Learning
  1. Understanding how the human brain works can improve our grasp of complex environments. This knowledge helps in both neuroscience and technology applications.
  2. The future job landscape will involve more collaboration between humans and machines. Companies need to prepare for a mix of human and automated roles.
  3. Deep learning techniques are evolving, especially in object detection. Innovations in this field show how minor adjustments can lead to significant improvements in performance.

Data Science Weekly - Issue 232

Data Science Weekly Newsletter • 19 implied HN points • 03 May 18
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Programming
  1. Using machine learning can be made easier and more accessible through tools like Google's Teachable Machine, which provides useful UX insights.
  2. Deep learning techniques are being adapted for different types of data, including enhancing performance in models working with tabular data.
  3. Focusing on good data practices and proper processes is key for startups looking to build a strong data science platform.

Data Science Weekly - Issue 229

Data Science Weekly Newsletter • 19 implied HN points • 12 Apr 18
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Software Engineering
  1. Using mathematical methods like Markov Decision Processes can help find the best strategies to play games like 2048.
  2. Uber AI Labs has introduced a technique called differentiable plasticity, which allows AI to adapt and learn better over time.
  3. Automating canary analysis, as done by Netflix with their Kayenta platform, can improve testing of new software changes quickly and efficiently.

Data Science Weekly - Issue 222

Data Science Weekly Newsletter • 19 implied HN points • 22 Feb 18
🕹 Technology Data science Artificial Intelligence Machine Learning Neural Networks Deep Learning
  1. A moth's brain can learn to recognize odors faster than AI can, showing a fascinating aspect of how natural intelligence works.
  2. There's a shortage of AI talent, with only around 22,000 people worldwide having the necessary skills, which is a big challenge for the industry.
  3. New AI technologies are learning to be creative by understanding rules and then finding ways to break them, which could lead to innovative solutions.

Data Science Weekly - Issue 219

Data Science Weekly Newsletter • 19 implied HN points • 01 Feb 18
🕹 Technology Data science Machine Learning AI Deep Learning Big Data
  1. Deep learning education needs a common way to explain why different layers exist. Right now, it’s taught differently than other technical fields.
  2. You can create autonomous driving models using simulation environments like AirSim. This lets you train a model to steer a car just with camera input.
  3. Learning matrix calculus helps in understanding deep learning better. This knowledge is crucial for mastering the training of deep neural networks.

Data Science Weekly - Issue 217

Data Science Weekly Newsletter • 19 implied HN points • 18 Jan 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Deep Learning
  1. Deep learning can help automate front-end design by turning design mockups into code. This could make web development faster and easier for developers.
  2. Cloud AutoML is making AI technology more available to businesses that don't have a lot of machine learning experts. This tool can help them create their own high-quality models.
  3. A new recommendation method using a tree-based model can learn user preferences better than traditional methods. This could lead to smarter and more personalized recommendations for users.

Data Science Weekly - Issue 216

Data Science Weekly Newsletter • 19 implied HN points • 11 Jan 18
🕹 Technology AI Data science Machine Learning Deep Learning Computer Vision
  1. A cat named Oscar is surprisingly good at predicting when terminally ill patients are going to die, showing that sometimes animals can have abilities we don't understand yet.
  2. Researchers are making AI systems that can recognize when they are uncertain about something. This could help them make better decisions and avoid mistakes.
  3. There are new tricks used in AI, like AlphaGo Zero, that show how deep learning can improve by learning from its own experiences and using fewer resources.

Data Science Weekly - Issue 214

Data Science Weekly Newsletter • 19 implied HN points • 28 Dec 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Analytics
  1. There was a lot of cool stuff happening in data science in 2017. It's a good idea to look back and see what others accomplished that year.
  2. NVIDIA is facing competition in deep learning hardware with new products coming from AMD and Intel. It might be wise to hold off on buying new hardware until the market settles.
  3. Machine learning is getting more attention in fields like physics, showing its importance in making big discoveries. Using tools like Python is becoming essential in modern science.

Data Science Weekly - Issue 211

Data Science Weekly Newsletter • 19 implied HN points • 07 Dec 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning
  1. A new library of 3-D images can help robots better navigate in homes by recognizing different furniture. This means robots could become more helpful around the house.
  2. Deep learning continues to evolve, and some algorithms are now as good as expert doctors in diagnosing diseases. This could greatly impact healthcare and how we approach medical diagnoses.
  3. Effective data science management is crucial for the success of organizations. Understanding how to scale and manage data science teams can lead to more valuable outcomes.

Data Science Weekly - Issue 210

Data Science Weekly Newsletter • 19 implied HN points • 30 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Computer Vision
  1. Computer Vision has seen many advancements recently, making a big impact on society. It's important to keep a balance when discussing potential future outcomes.
  2. The idea of an intelligence explosion is challenged by claims that it misunderstands how intelligence and self-improving systems work. Concrete examples support this perspective.
  3. A study showed that many comments about net neutrality might have been faked using natural language processing, raising concerns about online authenticity.

Data Science Weekly - Issue 204

Data Science Weekly Newsletter • 19 implied HN points • 19 Oct 17
🕹 Technology AI Machine Learning Data science Deep Learning Neural Networks
  1. Google is working on smart software that can create other software, making tech easier and more efficient.
  2. Our brains limit us to having meaningful relationships with only about five close friends, which is interesting for understanding social networks.
  3. There are many resources available, like open-source tools and training, that can help anyone learn data science and AI skills easily.

Data Science Weekly - Issue 196

Data Science Weekly Newsletter • 19 implied HN points • 24 Aug 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Programming
  1. Using machine learning models, like recurrent neural networks, can enhance text editing by making it smarter and more responsive. It allows for cool features like inline autocomplete that feels very natural.
  2. When choosing between deep learning frameworks like PyTorch and TensorFlow, think about how easy they are to use and their flexibility for your specific project needs.
  3. Building a strong data science resume and portfolio is crucial to getting hired; make sure they highlight your skills and tailor them to each job you apply for.

Data Science Weekly - Issue 189

Data Science Weekly Newsletter • 19 implied HN points • 06 Jul 17
🕹 Technology AI Data science Machine Learning Deep Learning Analytics
  1. Machines are starting to create art that can compete with human artists. This raises interesting questions about creativity and technology.
  2. New tools are helping to improve both music and audio quality using advanced deep learning techniques. This could change how we experience sound.
  3. Companies like General Electric are using AI to enhance their operations and adapt to modern tech trends. This shows how traditional industries are evolving with technology.

Data Science Weekly - Issue 179

Data Science Weekly Newsletter • 19 implied HN points • 27 Apr 17
🕹 Technology Data science Artificial Intelligence Machine Learning Computer Vision Deep Learning
  1. Robots are getting smarter and might make their own choices, raising questions about their moral decisions. We need to think about what it means for a machine to behave morally.
  2. Creating effective Optical Character Recognition involves advanced technologies like deep learning and computer vision, showcasing how complex tech solutions can be in modern projects.
  3. Machines can analyze data in ways we may not fully understand, challenging our long-held beliefs about knowledge and order. This raises interesting points about how we trust these systems.

Data Science Weekly - Issue 167

Data Science Weekly Newsletter • 19 implied HN points • 02 Feb 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Programming
  1. There are better ways to summarize data instead of just using averages, like means and standard deviations. These alternatives are easier to understand and work better with tough data.
  2. Deep learning can create cool projects, like a neural network that rewrites rap lyrics or generates sentences in dead languages. It's amazing how machines can learn and create in new ways.
  3. Data science needs to be a core part of business for it to truly succeed. When integrated well, it can change the game, but it’s important to avoid half-hearted efforts.

Data Science Weekly - Issue 113

Data Science Weekly Newsletter • 19 implied HN points • 21 Jan 16
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Software Development
  1. Analyzing different State of the Union addresses can reveal changes in language and topics over time. It's interesting to see how leaders communicate their ideas.
  2. Video games can be very useful for developing artificial intelligence. They provide specific challenges that help researchers create better AI solutions.
  3. There's a growing interest in Bayesian methods among R users, thanks to new tools that make these techniques easier to adopt. This could change how many people approach data analysis.

Data Science Weekly - Issue 112

Data Science Weekly Newsletter • 19 implied HN points • 14 Jan 16
🕹 Technology Data science AI Machine Learning Neural Networks Deep Learning Statistical Analysis
  1. The value of information is important in decision-making. Knowing how much to pay for good information can help you make better choices.
  2. AI is getting better at understanding humor. It was thought machines couldn't grasp humor, but advancements are changing that view.
  3. Participating in hackathons can fast-track your learning. Working with others on projects can teach you more than studying alone for months.

Data Science Weekly - Issue 84

Data Science Weekly Newsletter • 19 implied HN points • 02 Jul 15
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Predictive Analytics
  1. Neural networks are being used to create things like text, music, and images. They're learning from examples and getting better at generating content.
  2. Machine learning can help predict crime in cities by analyzing data from various sources. This approach aims to enhance safety and efficiency in crime prevention.
  3. Getting good at machine learning requires practice and understanding. There are many resources available, like cheat sheets and books, to help beginners learn the basics.

Data Science Weekly - Issue 20

Data Science Weekly Newsletter • 19 implied HN points • 10 Apr 14
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Statistics
  1. Understanding neural networks can be easier with low-dimensional models, where we can use visualizations to see how they behave and learn.
  2. Building a data-driven organization involves encouraging team members to make decisions based on data rather than gut feelings.
  3. Machine Learning has its challenges, for example in self-driving car research, there are many expectations that might not be fulfilled as quickly as we hope.

Hot Topics #20 (Mar. 1, 2023)

The Merge • 0 implied HN points • 01 Mar 23
🔬 Science Deep Learning Neural Networks
  1. Protein design using deep learning techniques to create custom biocatalysts
  2. Efficient de novo protein design through relaxed sequence space for better computational efficiency
  3. Improving robotic learning with corrective augmentation through NeRF for better manipulation policies