Category: Neural Network

  • Which Home Maintenance App Fits Your House? 6 Platforms Compared

    Which Home Maintenance App Fits Your House? 6 Platforms Compared

    Choosing a home maintenance app isn’t really about choosing software. It’s about choosing how you want to manage your home over the next five or ten years.

    Some homeowners want a simple reminder before winter arrives. Others want every receipt, warranty, inspection report, and contractor invoice stored in one place. Some are trying to understand how their house actually works, while others already know the basics and simply want a better way to stay organized.

    That’s why comparing homeowner apps can feel surprisingly difficult. Most of them solve the same broad problem, but they solve it from completely different angles.

    Rather than asking which platform has the most features, it’s usually more useful to ask a different question:

    Which one fits the way you actually own your home?

    Every House Creates Different Priorities

    A newly built townhouse doesn’t ask the same things of its owner as a century-old family home.

    Some properties require very little beyond seasonal maintenance and appliance servicing. Others come with mature landscaping, aging mechanical systems, ongoing renovations, and years of paperwork that need to stay organized. Even two neighboring homes can have completely different maintenance priorities depending on their age, location, previous upgrades, and the people living inside them.

    Because of that, the best homeowner app often depends less on the house itself and more on the homeowner’s routine. Someone who enjoys planning projects will naturally use software differently from someone who simply wants reminders and reliable answers when questions come up.

    1. I am Home Homeownership Platform

    I am Home is designed for homeowners who want one place to understand, maintain, and manage their property rather than assembling several separate tools.

    Instead of concentrating only on maintenance reminders, the platform combines homeowner education, personalized maintenance planning, seasonal checklists, AI guidance, and home expense tracking into one connected experience. The different features aren’t isolated modules – they support one another throughout the ownership journey.

    The educational side is particularly valuable for homeowners who want to become more confident over time. Know Your Home explains home systems in practical language, helping users understand why maintenance matters instead of simply reminding them to complete another task. Personalized maintenance plans adapt recommendations according to the home’s characteristics and local climate, while Hank, the platform’s AI assistant, provides answers using context about the specific property instead of relying on generic online advice.

    Highlights include:

    • Know Your Home educational library
    • Personalized maintenance plans
    • Seasonal checklists for every US state and Canadian province
    • Hank AI homeowner assistant
    • Home expense tracking
    • Recurring maintenance reminders
    • First-home guidance

    Rather than acting as a digital checklist, I am Home gradually becomes a complete operating system for homeownership. Every maintenance task, expense, document, and question contributes to a broader understanding of the property, making the platform increasingly valuable the longer someone owns their home.

    2. HomeZada

    HomeZada appeals to homeowners who naturally think in terms of long-term projects. Owning a home often means balancing maintenance with renovations, budgets, inventories, warranties, and future improvements. Keeping those responsibilities connected can make planning much easier than treating every project as a separate event.

    The platform combines maintenance scheduling with home inventories, financial planning, remodeling management, and property documentation, creating a workspace that’s well suited to homeowners who regularly invest in improving their property.

    Core features include:

    • Maintenance scheduling
    • Home inventory
    • Budget management
    • Remodeling projects
    • Warranty storage
    • Property documentation
    • Financial planning

    Instead of focusing only on what needs attention today, HomeZada helps homeowners build a longer-term picture of how their property changes and improves over time.

    3. HomeBinder

    Some homeowner apps focus on the future. HomeBinder focuses just as much on the past. Every repair, inspection, renovation, warranty, and contractor visit becomes part of a growing digital record that stays connected to the home. Instead of relying on memory when questions come up years later, homeowners can quickly review the property’s history and understand exactly what work has already been completed.

    Key features include:

    • Digital home records
    • Maintenance history
    • Contractor directory
    • Warranty management
    • Home improvement documentation
    • Secure document storage
    • Property information

    That historical perspective becomes increasingly valuable with every passing year. The more work that’s completed on the property, the more useful it becomes to have everything organized inside one searchable record.

    4. Dwellin

    Some homeowners measure organization by how many reminders they receive. Others measure it by how quickly they can find what they’re looking for.

    Whether it’s a paint color from three years ago, the warranty for a garage door opener, or the installation date of a new water heater, having reliable access to information often saves more time than another notification on the calendar.

    Dwellin focuses on creating a digital home profile where maintenance plans, appliance information, warranties, and household records stay connected to the property. Instead of organizing information by folders or file names, everything revolves around the house itself.

    Key features include:

    • Home profile management
    • Maintenance planning
    • Digital document storage
    • Appliance records
    • Warranty organization
    • Property history
    • Household information

    As more information is added, the platform becomes increasingly useful. Instead of creating another archive, it builds a practical reference point that homeowners can return to whenever questions arise.

    5. HomeKeepr

    Maintaining a house isn’t always about doing the work yourself. Knowing who to call and remembering who did excellent work last time can be equally important. Reliable professionals become part of homeownership just as much as maintenance schedules and household records.

    HomeKeepr combines maintenance organization with homeowner resources and service-provider management. Homeowners can keep recurring tasks, contractor information, and previous service history connected inside one platform, making future repairs and improvement projects much easier to coordinate.

    Features include:

    • Home maintenance reminders
    • Service provider organization
    • Contractor directory
    • Property records
    • Homeowner resources
    • Contact management
    • Maintenance tracking

    That balance between home organization and professional contacts makes the platform especially useful for homeowners who regularly work with local service providers.

    6. Upkept

    Every homeowner has good intentions. The challenge is turning those intentions into routines that actually last.

    Many maintenance jobs are small enough to postpone, but important enough that delaying them repeatedly eventually leads to larger repairs. Building consistent habits often matters more than having the perfect maintenance schedule.

    Upkept was designed around that idea. It combines recurring reminders with educational resources, helping homeowners understand not only what should be done, but also why those tasks deserve attention throughout the year.

    Highlights include:

    • Maintenance schedules
    • Recurring reminders
    • Seasonal planning
    • Home care education
    • Task management
    • Progress tracking
    • Maintenance history

    Rather than overwhelming users with long annual checklists, the platform encourages steady, manageable maintenance that fits naturally into everyday life.

    There Isn’t A Perfect Home Maintenance App

    The right platform depends on the kind of homeowner you are. Some people enjoy understanding every system inside the house before something breaks. Others care more about staying organized, preserving years of home records, or making sure maintenance simply doesn’t get forgotten. Those priorities naturally lead to different software choices.

    Instead of comparing feature counts alone, it’s often more useful to think about which parts of homeownership currently take the most time or create the most frustration. That’s usually where the biggest improvement will come from.

    Your House Will Keep Changing

    A home isn’t a finished project. Every year introduces new maintenance tasks, repairs, upgrades, documents, warranties, and decisions. The information surrounding the property keeps growing long after moving day, which means the system you choose should be able to grow alongside it rather than becoming another thing that eventually needs replacing.

    The best home maintenance platforms aren’t simply reminders with extra features. They become long-term companions that help homeowners understand their property, organize its history, and make better decisions as the home evolves. Choosing one that matches both your home and your habits is often more valuable than choosing the one with the longest list of capabilities.

  • ChatGPT – an assistant for a programmer? An example of a real-world task: Neural network square recognition

    ChatGPT – an assistant for a programmer? An example of a real-world task: Neural network square recognition

    No matter how you look at it, the ChatGPT language model can never completely replace a programmer, because only about 1/10 of the total development time is spent writing code. However, ChatGPT is great for helping with various aspects of programming. The more skills and experience a programmer has, the more useful an “assistant” can be:

    • Perform code optimization and improve performance.
    • Find and fix bugs in the code.
    • Explain complex concepts and algorithms.
    • Assist in developing ideas and choosing the right architecture.
    • Create prototypes and demos of programs.
    • Give advice on programming style and best practices.
    • Automate repetitive tasks.
    • Generate code based on specifications or specified parameters.
    • Extend functionality with plugins and tools.
    • Write documentation and comments to the code.

    Today it’s incredibly stupid not to use the features of ChatGPT. It really is a universal assistant, which greatly simplifies the life of a programmer and increases the efficiency of development. This programming becomes a much more pleasant and efficient business than ever before.

  • How a neural network recognized landmarks on photo cards

    How a neural network recognized landmarks on photo cards

    The goal of the project was to recognize landmarks in photographs using machine learning, namely convolutional neural networks. This topic was chosen for the following reasons:

    The author already had some experience with computer vision tasks

    the task sounded as if it could be done very quickly without much effort and, what is important, without a lot of computing resources (all nets were trained in colab or in kagle)

    the problem could have some practical application (well, in theory…)

    At first it was planned as a purely educational project, but then I got into the idea of it and decided to refine it to the state that I can.

    In what follows, I will talk about how I approached this task, and in doing so I will try to follow the code from the notebook where all the magic happened, while also trying to explain some of my actions. Maybe this will help someone get over their fear of the “blank slate” and see that this kind of thing is really easy to do!

    Tools
    First things first, let me tell you about the tools which were used for this project.

    Colab/Kaggle: used to train networks on GPUs.

    Weights And Biases: a service where I was saving models, their descriptions, adding losses, metrics values, training parameters, preprocessing. In general, I kept complete records. You can read the data here. The metadata section was slightly changed while writing the code – it actually contains the parameters of training and preprocessing. In the files section you can find a description of the network (how its layers are arranged), download the trained weights of the network and look at the values of losses and metrics.

    Training data
    Well, I should probably start by choosing the data to train the neural network. For this I searched data sets on Cagle (see here) and this site caught my eye.

    Actually, as it turned out, there is a competition from Google, related just to the recognition of landmarks. Here was the first problem: dataset weighs \approx100gb. Realizing that the grids in the future I will learn not on my bakery, I had to give up this option. After some more research, I settled on this dataset. It contains 210 classes and about 50 pictures per class. The pictures are all different sizes, taken from different angles, from different distances. In general, the dataset is not refined at all, and so far I’ve only worked with these.

  • OpenAI studied GPT-2 with GPT-4 and tried to explain the behavior of neurons

    OpenAI studied GPT-2 with GPT-4 and tried to explain the behavior of neurons

    Experts from OpenAI published a study in which they described how they tried to explain the work of neurons of its predecessor, GPT-2, using the GPT-4 language model. Now the company’s developers seek to advance in the “interpretability” of neural networks and understand why they create the content that we receive.

    In the first sentence of their article, the authors from OpenAI admit: “Language models have become more functional and more pervasive, but we don’t understand how they work.” This “ignorance” of exactly how individual neurons in a neural network behave to produce output data is referred to as the “black box.” According to Ars Technica, trying to look inside the “black box,” researchers from OpenAI used their GPT-4 language model to create and evaluate natural-language explanations of neuronal behavior in a simpler language model, GPT-2. Ideally, having an interpretable AI model would help achieve a more global goal called “AI matching.” In this case, we would have assurances that AI systems would behave as intended and reflect human values.

    OpenAI wanted to figure out which patterns in the text cause neuron activation, and moved in stages. The first step was to explain neuron activation using GPT-4. The second was to simulate neuronal activation with GPT-4, given the explanation from the first step. The third was to evaluate the explanation by comparing simulated and real activations. GPT-4 identified specific neurons, neural circuits, and attention heads, and generated readable explanations of the roles of these components. The large language model also generated an explanation score, which OpenAI calls “a measure of the ability of the language model to compress and reconstruct neuronal activations using natural language.”

    During the study, OpenAI offered to duplicate the work of GPT-4 in humans and compared their results. As the authors of the article admitted, both the neural network and the human “performed poorly in absolute terms.”

    One explanation for this failure, suggested by OpenAI, is that neurons can be “polysemantic,” meaning that a typical neuron in the context of a study can have multiple meanings or be associated with multiple concepts. In addition, language patterns may contain “alien concepts” for which people simply do not have words. This paradox could arise for various reasons: for example, because language models care about the statistical constructs used to predict the next token; or because the model has discovered natural abstractions that people have yet to discover, such as a family of similar concepts in non-comparable domains.

    The bottom line at OpenAI is that not all neurons can be explained in natural language; and so far, researchers can only see correlations between input data and the interpreted neuron at a fixed distribution, with past scientific work showing that this may not reflect a causal relationship between the two. Despite this, the researchers are quite optimistic and confident that they have succeeded in laying the groundwork for machine interpretability. They have now posted on GitHub the code for the automatic interpretation system, the GPT-2 XL neurons and the explanation data sets.

  • How to structure machine learning projects using GitHub and VS Code: complete instructions with settings and templates

    How to structure machine learning projects using GitHub and VS Code: complete instructions with settings and templates

    A well-designed process for structuring machine learning projects can help you create new GitHub repositories quickly and navigate an elegant software architecture from the start. The VS Cloud team has translated an article on how to organize files in machine learning projects using VS Code. A template for creating machine learning projects can be downloaded on GitHub.

    Note

    To create a new machine learning project from the GitHub template, go to the GitHub repository and click “Use this template”. GitHub template repositories are a very handy thing: they allow me and other users to generate new repositories with the same structure, branches, and files as the template.

    The next page opens up project settings, such as repository name and privacy settings:

    Having created the repository, click “Actions” on the top menu and wait a bit:

    If a green checkmark appears, the project is ready – you can write code!
    Next I’ll tell you why a particular file is added to the project and how the GitHub template was created.

    Basic files

    First, let’s look at the main files of the project, created on the basis of the template:

    .gitignore

    From the .gitignore file, GitHub draws information about which files to ignore when you commit a project to the GitHub repository. If you are creating a new repository from scratch, you can specify a pre-configured .gitignore file.