Decoding Coding

Decoding Coding deconstructs computer science and technology concepts, with focus on AI, machine learning, system design, language models, data handling, and cryptography. It discusses technological applications, tools, frameworks, and the importance of structured data for model efficiency.

AI Machine Learning Language Models System Design Data Handling Cryptography Technology Applications Tools and Frameworks

The hottest Substack posts of Decoding Coding

And their main takeaways

Newsletter #13: StructGPT

19 implied HN points • 25 May 23
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. StructGPT helps large language models (LLMs) work better with structured data like graphs and databases. It converts this complex data into a simpler format that LLMs can understand.
  2. There are three key tasks that StructGPT can do: answer questions based on knowledge graphs, process data tables, and perform text-to-SQL queries. Each task has its own specific steps.
  3. The method focuses on linearizing raw data so that LLMs can process it more effectively. This allows LLMs to handle a wider variety of tasks more efficiently.

Newsletter 21: To keepdims or not to keepdims!

1 HN point • 19 Jul 24
🕹 Technology Machine Learning Deep Learning Software Development Data science Artificial Intelligence
  1. Understanding the 'keepdims' parameter in tensor operations is important for getting correct results in PyTorch. If you set 'keepdims' to True, the dimensions are preserved, which helps with broadcasting correctly.
  2. When summing tensors, if 'keepdims' is False, it can lead to incorrect calculations because the tensor's shape changes. This can result in dividing values incorrectly, leading to unexpected outputs.
  3. It's crucial to be careful with tensor shapes and broadcasting rules in machine learning models. Even a small oversight can cause models to produce wrong predictions, so always double-check these details.

Newsletter #12: System Design for Machine Learning - Part II

19 implied HN points • 18 May 23
🕹 Technology Machine Learning Data science System Design Case Studies Software Development
  1. Airbnb uses a special tool called Zipline for feature engineering in their Customer Lifetime Value model, which helps them pick and create over 150 features needed for predictions.
  2. Chicisimo built a recommendation system based on user data, which includes both objective and subjective features, to give personalized fashion advice using their Social Fashion Graph.
  3. Case studies provide valuable lessons in applying frameworks to real-world projects, showing that you need both a good framework and experience from past projects to succeed.

Newsletter #8: HuggingGPT

19 implied HN points • 06 Apr 23
🕹 Technology Artificial Intelligence Machine Learning Software Development Programming Automation
  1. HuggingGPT helps solve complex tasks by breaking them down into smaller steps. It uses different AI models to handle each part, making the whole process easier and more organized.
  2. Current AI models struggle with processing various types of data and managing multiple tasks at once. HuggingGPT aims to improve this by using LLMs to plan and execute tasks more efficiently.
  3. The model operates in four main stages: planning tasks, selecting the right model for each task, executing them, and generating a final response. This structured approach makes coding more productive.

Newsletter #7: Advanced Prompt Engineering

19 implied HN points • 30 Mar 23
🕹 Technology AI Machine Learning Programming Natural Language Data science
  1. Zero-shot prompting lets a model answer questions without examples. It's useful when there's no data to guide the model.
  2. Few-shot prompting gives the model a few examples to improve its answers. This helps the model understand the context better.
  3. Chain-of-thought prompting breaks down complex problems into steps. It helps the model reason through tasks more effectively.
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Newsletter #3: MusicLM

19 implied HN points • 23 Feb 23
🕹 Technology AI Music Generative Data Creatives
  1. MusicLM is a new tool by Google that generates music from text descriptions. It builds on previous models for sound and keeps improving the quality of the audio it creates.
  2. The technology behind MusicLM uses a combination of audio and text representations to produce music that matches the style described in the input. This allows for detailed and longer audio clips.
  3. While MusicLM could help make music production faster and more creative, there are concerns about biases in training data and potential plagiarism risks, leading to no plans for public release.

Newsletter #2: Certain Randomness

19 implied HN points • 09 Feb 23
🕹 Technology Computer Science Algorithms Cryptography Machine Learning Randomness
  1. Random numbers are important in computer science for things like cryptography, simulations, and game mechanics. They help create unpredictability and realism in these applications.
  2. There are two main types of random number generators: True Random Number Generators (TRNGs) that use real-world entropy, and Pseudo Random Number Generators (PRNGs) that produce predictable outcomes based on a starting value.
  3. Algorithms like Linear Congruential Generators (LCGs) and Mersenne Twister are commonly used for generating pseudo-random numbers in various applications due to their efficiency and quality.

Newsletter #1: Detecting AI with AI

19 implied HN points • 02 Feb 23
🕹 Technology AI Machine Learning Ethics Text generation
  1. Detecting AI-generated text can be done by analyzing how likely the text is based on minor changes. If a text keeps showing a low probability, it probably came from an AI.
  2. Watermarking is another method, where certain words are purposely biased to make AI writing unique. If those specific words show up often, it's a sign that the text was generated by an AI.
  3. As AI tools become more popular, it's important to develop better detection methods to prevent cheating and ensure fair use in writing and academics.

Newsletter #0: Zero-Knowledge Proofs

19 implied HN points • 26 Jan 23
🔮 Crypto Blockchain Security Privacy Web3
  1. Zero-knowledge proofs let someone prove they know something without giving away the actual information. It's like showing you can perform a magic trick without revealing how it’s done.
  2. These proofs have been around since the 1980s and have evolved into important applications in areas like finance and identity verification, especially in Web3 technologies.
  3. ZKPs have key properties like completeness and soundness, but they also come with challenges like being complex to implement and vulnerable to quantum computing attacks.

Newsletter #11: System Design for Machine Learning - Part I

0 implied HN points • 04 May 23
🕹 Technology Machine Learning System Design Data science Software Engineering Artificial Intelligence
  1. Before starting on a machine learning project, it's important to define clear goals and understand how ML can help achieve them.
  2. Setting up a data pipeline is crucial; it involves collecting, preparing, and analyzing data to see what features are useful for your model.
  3. When deploying machine learning models, you need to consider both hardware and software needs, including how to handle real-time data for ongoing training.

Newsletter #10: Generative Disco

0 implied HN points • 27 Apr 23
🕹 Technology AI Media Video Music Design
  1. Generative Disco is an AI tool that uses language models and image generation to create videos from music. It combines different AI technologies to visualize songs.
  2. Users can define specific time intervals in the music for the video generation. They also provide a description of the scene they want to depict.
  3. This new method makes video creation easier for everyone, even those who don't have expertise in complex editing software. It's a fresh look at how we might edit videos in the future.

Newsletter #9: Building a Brain with LLM

0 implied HN points • 20 Apr 23
🕹 Technology AI Robotics Programming Machine Learning Automation
  1. Robots can use language models to understand and navigate their environments better. This setup includes a visual model that acts like an 'eye' to see the world.
  2. The robot has a 'nerve' system that asks questions and plans actions based on what it sees. It makes sense of information and decides what the robot should do next.
  3. Eventually, as language models improve, robots could act more autonomously and make decisions on their own. This could change how we interact with machines in exciting ways.

Newsletter #6: Prompt Engineering

0 implied HN points • 23 Mar 23
🕹 Technology Artificial Intelligence Natural Language Software Development Programming Data processing
  1. When using language models, the way you ask or prompt them affects the answers you get. More context often leads to better responses.
  2. You can use specific prompts to generate summaries, create text in different styles, or even test your ideas by simulating expert responses.
  3. Language models can greatly assist in coding tasks by generating templates and examples quickly, but it's important to double-check the versions of any libraries they suggest.

[Help Page] Joining the subscriber chat

0 implied HN points • 21 Mar 23
🕹 Technology Apps Social media Communication Digital Tools User Experience
  1. There's a special chat space just for subscribers, kind of like a group chat. You can share thoughts and updates with others.
  2. To join the chat, you need to download the Substack app which works on both iOS and Android. Don't forget to turn on notifications so you can stay updated.
  3. Once you have the app, just click on the chat icon to get started. Say hi and join the conversation!

Newsletter #5: Backprop from scratch

0 implied HN points • 09 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Programming Data science Neural Networks
  1. Derivatives show how small changes in inputs affect the output of a function. This is important for understanding how neural networks adjust to improve their predictions.
  2. In neural networks, understanding how changes in weights and inputs influence the output helps us optimize performance. By adjusting weights based on calculated gradients, we can make the network learn better.
  3. The chain rule is key when calculating how different layers of a neural network affect the final output. It allows us to connect changes in inputs through to the overall output, helping us to fine-tune the model.

Newsletter #4: Probabilities with Python

0 implied HN points • 02 Mar 23
🕹 Technology Programming Data science Machine Learning Statistics Software Tools
  1. NumPy is a powerful tool for working with probability distributions in Python. You can easily generate data and calculate probabilities using its features.
  2. Common probability distributions like Normal, Binomial, and Poisson can be modeled using NumPy. Each distribution has its own formula to calculate probabilities.
  3. De Morgan's Laws help in calculating probabilities of complements in events. They show how to relate the union and intersection of events, which can be useful in probability theory.

Newsletter #15: ViperGPT

0 implied HN points • 15 Jun 23
🕹 Technology Artificial Intelligence Machine Learning Software Development Computer Vision Programming
  1. ViperGPT is a new AI model that can answer questions about images and videos. It combines powerful text and vision models to understand visual inputs better.
  2. The model generates Python code based on user questions, allowing it to be flexible and efficient. It uses all available online Python code for improvement.
  3. ViperGPT's execution engine runs the generated code and provides results based on the visual content. This helps users make sense of raw data in a more meaningful way.

Newsletter #20: PDFTraige

0 implied HN points • 08 Nov 23
🕹 Technology AI NLP Software Data science Machine Learning
  1. PDFTriage helps AI understand the structure of documents, like research papers. By using this structure, it can give better answers to specific questions about the document.
  2. It has three stages: first, it creates a detailed structure of the document; next, it queries data based on this structure; and finally, it answers user questions using the gathered information.
  3. This approach shows how thinking about how humans write and organize information can improve how AI systems work. It allows the AI to pull relevant details effectively.

Newsletter #16: PEARL — A LLM brain for large texts

0 implied HN points • 22 Jun 23
🕹 Technology AI Machine Learning Software Development Data science Research
  1. LLMs can act like a 'brain' for processing and understanding large texts. They help plan and execute tasks by breaking them down into smaller steps.
  2. The process consists of three main parts: discovering the necessary actions, creating a plan using those actions, and finally executing the plan carefully to avoid mistakes.
  3. Though this method shows promise, it still has limitations, like generating incorrect plans and being restricted by the size of information it can handle. Improvements are expected as technology advances.

Newsletter #19: CM3Leon

0 implied HN points • 20 Jul 23
🕹 Technology AI Machine Learning Image Generation Text generation Data processing
  1. CM3Leon is a new type of language model that can generate and fill in both images and text. It uses advanced techniques to combine these two forms of media.
  2. The model tokenizes images and text separately to understand them better, improving how it creates content. It also applies a method to ensure the documents it uses are relevant and diverse.
  3. CM3Leon aims to deliver quality results that are as good as current image generation models. Future posts will dive deeper into research and technical details about such technologies.

Newsletter #14: Adding Memory to LLMs

0 implied HN points • 01 Jun 23
🕹 Technology AI Machine Learning Data science Neural Networks Innovation
  1. LLMs can forget information when they get too big, which makes their performance worse. Adding an internal memory can help them remember better and adapt to new tasks.
  2. The new framework, Decision Transformers with Memory (DT-Mem), uses a special memory module to identify and store important information effectively. This helps the model improve its decision-making.
  3. By using techniques like content-based addressing, DT-Mem can selectively add or erase information in its memory, making it smarter and more efficient in handling tasks.

Newletter #17: Textbooks are all you need!

0 implied HN points • 29 Jun 23
🕹 Technology Machine Learning Artificial Intelligence Software Development Data science Education
  1. Using online code for training LLMs can cause problems because that code often needs extra info to be useful and includes repetition. It's not always high-quality or useful code.
  2. The phi-1 model improves training by using a specific set of high-quality code from textbooks and exercises, making it better for learning how to code.
  3. This approach shows that just changing the training data can lead to better results, highlighting the importance of using good resources for teaching coding.

Newsletter #18: Vision via language

0 implied HN points • 13 Jul 23
🕹 Technology AI Machine Learning Computer Vision Natural Language Data Analysis
  1. LENS uses large language models combined with computer vision to help computers understand images. This means computers can answer questions about visuals using language.
  2. The system has multiple components that analyze images and generate feedback. These include tagging images, describing their attributes, and creating detailed captions.
  3. This approach makes it easier for language models to handle not just images, but potentially videos and other visual inputs in the future, expanding their usefulness.