标签：#learn

With more software engineers entering into data science and AI, I feel it's equally important for a person with data and AI background to dive into software development to survive, thrive in industry. I Know it's a very broad question, so suggestions with broad subjects, topics are welcome , like I often wonder how DSA is relevant. I totally understand the needs of the skills are deeply coupled with domain, industry and specific problems but unfortunately the industry doesn't understand this, it judges you, rewards you based on what you already know or pretend rather than your ability to learn or adapt. submitted by /u/Dapper_Chance_2484 [link] [留言]

Hi folks, Deciding between these two Mac options has been a challenge for me, so pls help. I know mac is not even necessary for this but just help me to decide between these two options. For the reference, Im a swe student and looking forward to go deep into ml and data science in the near future… EDIT: mac book pro m5 ( base chip) that I’m referring here. submitted by /u/Both-Hovercraft3161 [link] [留言]

Hi everyone, I'm an undergraduate student and ML researcher at UC Berkeley. My colleagues and I are working on a project that hopes to fix some of the problems users face with Colab. What are the features you wish it had as an ML professional, researcher, or enthusiast? What're the biggest problems you've faced while using it? Some of the issues that everyone feels (including us) is environment management and kernel persistence. But we would love to hear more from the community. submitted by /u/myplstn [link] [留言]

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

May 25, 2026. This is not just another date on my calendar. This marks the beginning of one of the biggest professional transitions of my life. After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time: I am transitioning into Data Analytics. And this is where I document that journey—publicly, honestly, and in real time. Not when I become an expert. Not when I feel “ready.” Not when everything looks polished. I’m starting now. Because real growth is rarely clean, predictable, or perfectly planned. Sometimes it starts with one uncomfortable decision: To leave what you already know… and step into what your future requires. Where My Journey Started Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT. For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations. My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity. Over the years, I’ve worked deeply in: Computer troubleshooting and hardware diagnostics Printer setup, configuration, and enterprise support Wi-Fi deployment and hotspot installations LAN design and structured network deployment Fiber optic installations and network termination Data cabling and structured cabling systems CCTV surveillance installation and maintenance Alarm systems and electronic security integration Intelligent security systems Electric fence installations and perimeter protection systems Router, switch, and access point configuration End-user support and enterprise technical troubleshooting Systems maintenance and operational support I’ve spent years on ladders, in server rooms, inside offices, on construction sites, insi

In the previous article, we explored concepts such as total loss and epochs. Now, we will continue...

I read and collected Arxiv whitepapers starting after the launch of ChatGPT. I copied and pasted excerpts into Word to track them. Then migrated to Obsidian. That vault of some 1700 papers is now online. I figured it was time to see if others would find the collection useful. My whitepapers were organized into some 90 categories, all of which emerged from paper topics. New categories became necessary with the discussion of new methods, techniques, models etc. If I wanted to write about a topic, I'd upload an md file containing research excerpts on that topic to ChatGPT. This worked to a degree but maxxed out context pretty quickly. And I always had related research in multiple categories, according to how the research was framed. (Personas research in Aligment, Psychology, HCI, etc). So I used a plugin to create topic notes that built in and outbound wikilinks across the papers centered on shared concepts. When I ported this all online I added another layer of synthesis: Inquiring Lines as I call them. These cover cross-cutting, tension-surfacing, synthesizing, and frontier-opening research frames. There's 6,000 of them in my collection. Each is a page to itself that's a useful description of a research line of inquiry. These now also have prompts you can run yourself that will find related (and more recent) research - (I can't adequately maintain each topic with new research). It's all at https://inquiringlines.com/inquiring-lines/ if you want to poke around. As is everything in the age of AI, it's a work in progress. But there's a lot of rich material in there. Have a look. submitted by /u/Barton5877 [link] [留言]

Hi, I am and PhD student and trying to run a ML reading group focused on interpretability and robustness every weekend. Its always nice to hear different takes and opinions on a paper and this discussion group could serve the purpose. If you are a fellow PhD student or a ML researcher interested in reading recent papers in depth then please fill this google form to be added in the group for receiving further updates on when we can meet and discuss: https://docs.google.com/forms/d/e/1FAIpQLSdNg4x60lUHV7YW_kKPFlpPR3Rom_rOovbryD8YtOGQR8x0Kw/viewform submitted by /u/Ok_Access_9159 [link] [留言]

Hi everyone, I’m an amateur student who has been experimenting with neural networks mostly out of curiosity. Over the past few weeks, I ended up going fairly deep into a specific architecture I designed, which I call a Directional Neural Network (DirNN) . This isn’t meant as a polished or formal contribution — it’s something I’ve been tinkering with, iterating on, and testing in my spare time. That said, the architecture does impose real structural constraints and uses a custom backward pass. In my own experiments on simple tasks (including some using GloVe embeddings), the DirNN has repeatedly performed better than standard MLP baselines. This result has been consistent enough that I don’t think it’s pure luck — but I’m very aware that I might be fooling myself. What I’m unsure about is whether I’ve been unfair in my comparisons . I don’t know if: the DirNN is effectively a special or degenerate case of an MLP my training procedure, initialization, or optimizer choices favor it in subtle ways the tasks or datasets I’m using make the comparison misleading I’ve put together a small repository with a README describing the architecture, the custom backward pass, and a minimal script to reproduce what I’m seeing. I’m posting here because I could really use a sanity check from people more experienced than me . If this is obviously flawed, I’d much rather learn that now. Blunt technical criticism, references, or “you’re missing X” comments are all very welcome. Repository: DirNNs Thanks for reading — I’m genuinely here to learn. submitted by /u/jos_lucas73 [link] [留言]

I need a break from social media and all the bots.. Aside from Arxiv are there any sources that do a good job of aggregating the good stuff and filtering out all the junk? submitted by /u/Tiny_Arugula_5648 [link] [留言]

Hi all, I have been working on this method based on a hunch along with many llm for quite some time. Though first it was being engineered by me but I was learning in supervised ml area but this hunch took to semi-supervised ml and that to too deep. I then became llm orchestrator of sort while 4 llm's tried to figure it out. I put up a live demo on Hugging Face Spaces where you can try it yourself — set the number of labels, click run, see the accuracy. No installation, no code required. Brief about method Optimus — Graph SSL under Extreme Label Scarcity Key Results (PathMNIST, N=2000, 9 classes) Labels Total Optimus GCN 9(1 per class) 73.9 60.6 27(3 per class) 77.3 68.5 45(5 per class) 79.8 77.1 https://huggingface.co/spaces/Keshu007/optimus-graph-ssl Edit : You can can even run the code on your own dataset submitted by /u/Loner_Indian [link] [留言]

From TF's website: Quantization aware training emulates inference-time quantization, creating a model that downstream tools will use to produce actually quantized models. So is it designed to work with a very specific quantization method (for Gemma-4, presumably, Google's own)? Or would it make sense to use alternative quantization methods? According to the benchmarks unsloth released, its (alternative) quantizations of Gemma-4-QAT are closer to the QAT fine-tunes, but is it a good thing, or does it defeat the purpose of QAT? submitted by /u/we_are_mammals [link] [留言]

I'm planning to submit a paper to either NMI, but this will be my first paper to a nature-like venue. Would love a quick chat with anyone that has experience. My paper's specifically more geared towards signal processing with ML for a specific subfield of engineering. But can be interdisciplinary. submitted by /u/PlateLive8645 [link] [留言]

If you're a networking instructor — at a university, technical college, boot camp, or corporate training program — you know the frustration of teaching the OSI Model. Static PowerPoint slides can only do so much. Students nod along in class, but when exam time arrives, the layers blur together. The PDU names become a confusing jumble. The OSI Model Simulator by Roboticela was built with educators in mind. It transforms a passive lecture into an interactive demonstration that students engage with, remember, and take home to explore on their own. Classroom Use Cases Live Demonstration Project the simulator on a classroom screen. Have students suggest messages to send and protocols to use. Step through each layer together as a class, stopping to ask questions: "What's happening here? What header was added? What device would operate at this layer?" The interactive format maintains attention far better than any lecture. Lab Assignments Assign students to run specific simulations and document their findings: "Run HTTP and HTTPS simulations. Screenshot the Presentation Layer for each. Explain in writing what differs and why." This assignment tests both tool usage and conceptual understanding. Flipped Classroom Send students to app.osi-model-simulator.roboticela.com before class. Ask them to run three simulations and come prepared to discuss what they observed. Class time becomes richer discussion rather than basic concept delivery. Protocol Comparison Exercise Have students run simulations for all five protocols — HTTP, HTTPS, SMTP, DNS, FTP — and create a comparison chart noting the differences at each OSI layer. This develops deep protocol literacy that traditional instruction rarely achieves. Why It Works: The Science of Active Learning Research in educational psychology consistently shows that active learning produces dramatically better retention than passive instruction. The "Learning Pyramid" (Edgar Dale's Cone of Experience) suggests: Lecture: ~5% retention after 2

Networking certifications like CompTIA Network+ and Cisco's CCNA are career-defining credentials. They validate your understanding of networking fundamentals — and both exams test OSI Model knowledge extensively. In fact, the OSI Model is arguably the single most tested conceptual framework in entry-level and intermediate networking certifications. Why OSI Is So Critical for Certification Exams Exam questions on OSI take many forms: "At which layer of the OSI model does a router operate?" (Layer 3) "What PDU is used at the Transport Layer?" (Segment) "Which protocol operates at the Application Layer?" (HTTP, DNS, SMTP...) "A user cannot connect to a website. Troubleshooting should begin at which OSI layer?" (Layer 1, then up) "Which device operates at Layer 2?" (Switch) "What is the function of the Presentation Layer?" (Translation, encryption, compression) These questions seem straightforward on paper but are notoriously confusing under exam pressure without deep conceptual understanding. How the OSI Simulator Accelerates Your Studies Visual Memory Formation Research in cognitive science consistently shows that visual and kinesthetic learning creates stronger memories than text-only reading. When you watch the OSI Simulator animate your message through all seven layers, you're forming episodic memories — vivid, experience-based memories that are far more durable than rote memorization. Protocol-to-Layer Association One of the most commonly missed exam categories is protocol-to-layer mapping. The OSI Simulator makes this automatic: when you select HTTP, the Application Layer is highlighted. When you watch TCP headers form, you associate TCP with Layer 4 viscerally, not just verbally. PDU Name Mastery Data, Segment, Packet, Frame, Bits — the five PDU names are shown explicitly at each layer in the simulator. After running 10 simulations, these names become second nature. No flashcard can match this experiential learning. Troubleshooting Framework Practice Network+ an

The Physical Layer's choice of transmission medium profoundly affects the performance, cost, security, and reliability of a network. The OSI Model Simulator supports all five major media types — making it a powerful tool for understanding how physical choices ripple up through all seven OSI layers. Medium Speed Max Distance Security Cost Ethernet Up to 10 Gbps+ 100m (Cat6a) High (physical access) Low Wi-Fi Up to ~9.6 Gbps (Wi-Fi 6) ~100m indoor Medium (WPA3) Low Fiber Optic Terabits/s 100s of km Very High High Coaxial Up to 1 Gbps 500m (RG-8) Medium Medium Radio Variable (5G: Gbps) km to global (satellite) Low–Medium Variable Ethernet: The Reliable Standard Ethernet is the dominant wired networking standard in homes, offices, and data centers. Using twisted-pair copper cables (Cat5e, Cat6, Cat6a), it provides reliable, high-speed connectivity with predictable latency. The IEEE 802.3 standard governs Ethernet, and modern variants include 1GbE, 10GbE, 25GbE, 40GbE, and 100GbE. Wi-Fi: Wireless Freedom Wi-Fi (IEEE 802.11) eliminated the need for physical cables in most consumer settings. Wi-Fi 6 (802.11ax) and Wi-Fi 6E deliver impressive speeds, but shared medium access, interference, and radio propagation challenges mean it will never fully replace wired Ethernet for critical applications. Fiber Optic: The Internet's Backbone Fiber optic cables carry data as pulses of light through glass or plastic strands. They're immune to electromagnetic interference, support enormous bandwidth, and can span continents — literally. Every major internet exchange, submarine cable, and data center interconnect uses fiber. Coaxial Cable: The Cable TV Legacy Coaxial cable — familiar from cable TV connections — consists of a central conductor surrounded by insulating layers and a braided metal shield. DOCSIS-based cable internet connections (common from ISPs like Comcast) use coaxial as the last-mile medium. Radio: Wireless at Scale From the cellular 5G network in your pocket to satellite

When I started learning Data Science, I expected to spend my first week writing Python code, exploring machine learning models, and working with advanced tools. Instead, I spent most of my time in Excel. At first, it felt underwhelming—just rows, columns, and simple spreadsheets. But within a few days, I realized something important: Excel is not a basic tool at all. It is one of the most widely used tools in data analysis, business decision-making, and reporting. 📊 Real-World Uses of Excel Excel is widely used across industries for handling and analyzing data. Some of the most common uses include: Business Analysis - Tracking sales and identifying trend Accounting and Budgeting - Managing Expenses, Profits and Financial reports Marketing Analysis - Measuring campaigns performance and customer behavior Data Entry and Management - organizing large datasets efficiently Businesses rely on Excel because it helps turn raw data into meaningful insights for decision making. 🛠️ Key Excel Features I Learned In my first week, I explored several important Excel Features that help with data organization and analysis: Excel Interface Overview - I first explored how Excel is organized, including Ribbon, Worksheets, Cell, Row, Columns, and formula bar. this helped me understand how to navigate the tool before working with data Data Sorting - Organizing data by numbers, Text and Dates Filtering - Showing only relevant data based on condition Data Validation - Ensuring accurate and consistent data entry Freeze Panes - Keeping header Visible while scrolling through large datasets. These features make working with data much easier, faster and more structured. 🧮 Basic Excel Functions I learned I was also introduced to some basic Excel functions used in Data Analysis. Aggregate Functions - SUM - Add all values in a range - AVERAGE - Calculate the mean of a dataset - COUNT - Counts numerical entries in a dataset Conditional Functions - SUMIF () and SUMIFS()** - Add values that meets one

Optimizing Laravel Performance: Conquering the N+1 Query Problem with Eager Loading As full-stack developers, building performant applications is a continuous challenge. One of the most insidious yet common performance bottlenecks encountered in Laravel applications is the "N+1 query problem." This issue can significantly degrade response times, inflate database load, and ultimately lead to a poor user experience. Fortunately, Laravel provides a powerful and elegant solution: eager loading using the with() method. This tutorial will walk you through understanding the N+1 problem and effectively using eager loading to keep your applications fast and efficient. Understanding the N+1 Query Problem Imagine a scenario where you need to display a list of blog posts, and for each post, you also want to show the name of its author. In a typical Laravel application, your Post model would likely have a belongsTo relationship with a User model. Let's look at a common, yet inefficient, way this might be implemented: 1. The Inefficient N+1 Approach Consider a controller fetching all posts and a view attempting to display the author's name: app/Http/Controllers/PostController.php (N+1 Example): namespace App\Http\Controllers ; use App\Models\Post ; use Illuminate\Http\Request ; class PostController extends Controller { public function index () { $posts = Post :: all (); // Fetches all posts return view ( 'posts.index' , compact ( 'posts' )); } } resources/views/posts/index.blade.php (N+1 Example): <h1>Blog Posts</h1> @foreach ($posts as $post) <div class="post-item"> <h2>{{ $post->title }}</h2> <p>Author: {{ $post->user->name }}</p> <!-- Accessing related user inside loop --> <p>{{ Str::limit($post->body, 150) }}</p> </div> @endforeach Why this is N+1: 1 Query: SELECT * FROM posts; – This initial query fetches all your posts. N Queries: For each $post in the loop, when you access $post->user->name , Laravel lazy-loads the associated User model. If you have 10 posts, this will exe

maybe this should be asked in the Fc26 game subreddit but not sure. Anyway I just saw a video of someone predicting the winner of the world cup using the simulate match feature in the game but he only did it once. Would running this feature 100-1000 times give a significant result ? or is that feature only based on luck ? submitted by /u/Stillane [link] [留言]

Most developers learn CSS specificity once. They remember: #id > .class > div Then move on. Until one day… Everything looks correct. The CSS is present. The selector is correct. The z-index looks higher. And yet the UI is broken. That’s when CSS specificity stops being a beginner topic and becomes a production debugging problem. The Production Incident That Started This Recently, I was working on a microfrontend application. Everything worked fine initially. I opened a page, launched a modal and the UI looked correct. Then I navigated to another microfrontend. Its CSS got loaded. After returning to the original microfrontend, suddenly: ❌ Modal appeared behind page content ❌ Overlay behaved incorrectly ❌ z-index looked correct but wasn't working DevTools showed my CSS rule still existed. Yet another CSS rule was winning. The culprit? CSS Specificity. 🧠 What is CSS Specificity? CSS specificity is the algorithm browsers use to decide: Which CSS rule wins when multiple rules target the same element. Browsers don't simply apply: "The last CSS rule." That's one of the biggest misconceptions. Specificity is calculated first. Only when specificity is equal does source order become important. ⚔️ Example .modal { z-index : 9999 ; } .some-library .modal { z-index : 100 ; } HTML: <div class= "some-library" > <div class= "modal" ></div> </div> Many developers expect: .modal to win because the value is larger. But CSS doesn't compare values first. It compares selectors. 🧮 How Specificity Works Specificity is usually represented as: ID - CLASS - TYPE Specificity Table Selector Specificity * 0-0-0 div 0-0-1 .modal 0-1-0 [type="text"] 0-1-0 :hover 0-1-0 #dialog 1-0-0 Inline Style Highest MDN defines specificity as the weight browsers calculate to determine which declaration gets applied when multiple selectors match the same element. Example Calculation Selector: button .primary Contains: button → 0 -0-1 .primary → 0 -1-0 Total: 0-1-1 Another selector: #header button .primary Contai