Post and delete by Camilla talking about Rollie’s son😬

2025.01.22 22:19 DrizzyDayy Post and delete by Camilla talking about Rollie’s son😬

Post and delete by Camilla talking about Rollie’s son😬 submitted by DrizzyDayy to BaddiesSouth [link] [comments]


2025.01.22 22:19 GameProfessional 🏆 Game Professional | 🔴🆕 YouTube | Battlefield 2042 Gameplay 30 PS5

🏆 Game Professional | 🔴🆕 YouTube | Battlefield 2042 Gameplay 30 PS5 submitted by GameProfessional to GameProfessional [link] [comments]


2025.01.22 22:19 gulshan_jakhon_ What’s the Outlier Marketplace? Here’s Why It’s a Game-Changer! 🚀

What’s the Outlier Marketplace? Here’s Why It’s a Game-Changer! 🚀 If you’re using OutlierAi (or thinking about it), let me tell you about something that makes the platform even better: the Outlier Marketplace! 🎉
https://preview.redd.it/1okvcikbemee1.png?width=1414&format=png&auto=webp&s=86007ee2150d6e9ae330adbcd7f45624418da85c
This feature puts YOU in control of your work. 🙌 Here’s the deal: you’ll get access to key info like pay rates 💰 and task details 📝, so you can make smarter choices about the projects you take on. Plus, you can even switch between gigs or pick up projects outside your usual area of expertise—if you’re qualified, of course! 🔄
Here’s Why You’ll Love the Outlier Marketplace ❤️: 1️⃣ Find work easily when your project ends. No more downtime or scrambling—just hop into the marketplace and find your next opportunity!
2️⃣ Pick projects that excite you. Bored of the same old thing? Choose tasks that match your interests or explore something new! 🌟
3️⃣ Transparency all the way. Know exactly what to expect with clear pay rates and task details upfront—no surprises, just clarity.🕶️
It’s all about giving YOU the freedom and flexibility to work on your terms. 💪
Have you tried the Marketplace yet? What’s been your experience? Let’s chat below! 👇
submitted by gulshan_jakhon_ to outlier_ai [link] [comments]


2025.01.22 22:19 GameProfessional 🌐 24/7 Video Game | 🔴🆕 YouTube Video | Battlefield 2042 Gameplay 30 PS5

🌐 24/7 Video Game | 🔴🆕 YouTube Video | Battlefield 2042 Gameplay 30 PS5 submitted by GameProfessional to 247videogame [link] [comments]


2025.01.22 22:19 Basshead404 Looking for a good budget press, recommendations? Other beginner questions too :P

Hey guys! I’m looking to finally get my own rosin press, although I’m trying not to overspend so I have budget for everything else. What are some good options that’ll have hydraulics and other small niceties? The 6 ton dabpress seems like a good option, but I’d love to hear if the community has found any other hidden gems.
Additionally, the more I’m thinking this out, the more elaborate the process seems. For example, how would you go about cleaning the bags? If there’s any other less covered parts of the process or tips you have, please share!
submitted by Basshead404 to rosin [link] [comments]


2025.01.22 22:19 Fm8722 Irko defo involved in ts 💀🙏

Irko defo involved in ts 💀🙏 submitted by Fm8722 to GoodAssSub [link] [comments]


2025.01.22 22:19 Cheap_Ad8739 Calculating Limits Using the Limit Laws question

Calculating Limits Using the Limit Laws question https://preview.redd.it/m3cbnf80emee1.png?width=834&format=png&auto=webp&s=77c20e0cb8cee32d5c987ff1e71db260c04d8519
Hi guys! I'm trying to do this problem, but it does not seem like this polynomial can be factored in order to cancel out x-2. Let me know if I'm missing any steps!
submitted by Cheap_Ad8739 to calculus [link] [comments]


2025.01.22 22:19 Background-Zombie689 Exploring how football strategy and AI/ML development go hand in hand

Introduction One of the most challenging aspects of Artificial Intelligence (AI) and Machine Learning (ML) is explaining their many moving parts in a way that both newcomers and experts can intuitively understand. Imagine, for a moment, that you’re not just building a model—you’re assembling an entire football organization. From scouting high-potential players (collecting data and crafting features) to adjusting strategies at halftime (incremental retraining), every component of AI/ML development has a parallel on the gridiron.
Below is a fully integrated analogy, rooted in advanced (PhD-level) concepts but presented in a way that resonates with practitioners and novices alike. By the end, you’ll see how the entire lifecycle of an AI/ML solution—from data collection to production deployment—can be reframed as a high-stakes football season.
@Sora
A. Preparation: Building the Foundation

  1. Owner → Business Stakeholder
    • Football: The owner defines long-term vision, invests capital, and tracks the team’s market value.
    • AI/ML: The business stakeholder sets the project’s objectives, allocates resources (budget, staff, computing power), and specifies performance expectations (KPIs, ROI targets).
  2. General Manager (GM) → Data Scientist
    • Football: The GM constructs the roster, balances the salary cap, and scouts future talent to maintain the team’s competitiveness.
    • AI/ML: The data scientist assembles datasets, manages resource constraints (compute budgets, data availability), and develops a sustainable plan for the model’s continuous improvement—much like shaping a balanced team over multiple seasons.
  3. Head Coach → Training Algorithm
    • Football: The head coach designs practices, sets the overarching strategy, and adjusts the team’s style of play as new challenges arise.
    • AI/ML: The training algorithm (e.g., gradient descent, genetic algorithms) iteratively updates model parameters, refining how the model “learns” from data. Like a coach, it establishes the direction and pace of the learning process.
  4. Assistant Coaches → Specialized Training Modules
    • Football: Offensive, defensive, and special teams coaches hone specific skills, align players to positions, and tailor techniques for different scenarios.
    • AI/ML: Specialized trainers or sub-processes (e.g., autoencoders for dimensionality reduction, adversarial training modules for robustness) each optimize a different aspect of the overall model’s performance.
  5. Scouts → Data Collection & Feature Engineering
    • Football: Scouts identify promising athletes, gather stats, and look for hidden gems in overlooked leagues or colleges.
    • AI/ML: Data collectors and feature engineers explore diverse data sources, clean and label datasets, and identify critical features. Like perpetual scouting, data gathering is never a one-and-done task; new data often reveals new opportunities for improving performance.
  6. Scouting Combine → Benchmarking & Validation
    • Football: Players perform under standardized conditions, showcasing measurable skills (40-yard dash, vertical jump, agility drills).
    • AI/ML: Potential models are tested on standard benchmarks (ImageNet, COCO, GLUE) or hold-out sets to compare architectures, hyperparameters, or new approaches. This ensures fairness and consistency in evaluation before “signing” the final model.
B. Execution: The Game Plan in Action
  1. Offensive Coordinator → Model Architecture & Hyperparameter Tuning
    • Football: Crafts the offensive strategy (run-heavy, pass-heavy, trick plays), adapting to an opponent’s weaknesses.
    • AI/ML: Selects and fine-tunes architectures (CNNs, RNNs, Transformers), deciding on learning rates, batch sizes, and other hyperparameters to optimize performance for the task at hand.
  2. Defensive Coordinator → Validation & Testing Strategies
    • Football: Focuses on stopping the opposing offense by anticipating play calls and adjusting defensive formations in real time.
    • AI/ML: Oversees validation, stress tests, or cross-validation routines to safeguard against overfitting. By spotting where the model fails, the coordinator (validation) refines the overall system.
  3. Playbook → Algorithm Design
    • Football: A repository of plays—everything from power running schemes to elaborate pass routes—that can be deployed based on the situation.
    • AI/ML: A repertoire of algorithms (supervised, unsupervised, reinforcement learning) and model variations, ready for different data types and business requirements.
  4. Quarterback → Machine Learning Model
    • Football: The on-field leader who translates the coach’s strategy into tangible action, making split-second decisions under pressure.
    • AI/ML: The core model that ingests input data (features) and outputs predictions or classifications. Just like a quarterback is heavily reliant on the team around him, the model’s performance is contingent upon data quality, preprocessing, and robust architecture design.
  5. Offensive Line → Data Preprocessing
    • Football: Linemen protect the quarterback, giving him time to execute plays and shielding him from sacks or hurried throws.
    • AI/ML: Preprocessing pipelines (cleaning, normalization, augmentation) shield the model from “noise” in raw data, thereby ensuring stability and accuracy in predictions.
  6. Wide Receivers & Running Backs → Specialized Sub-Models / Key Features
    • Football: Receivers handle complex routes and big-yardage gains; running backs manage consistent ground play.
    • AI/ML: Sub-models or feature sets tailored for specific tasks—e.g., a dedicated vision pipeline, an NLP module, or time-series forecasting. Each can provide either explosive insights or reliable, steady performance, depending on the situation.
  7. Tight Ends → Multitask Models
    • Football: Tight ends block like linemen yet catch like receivers, bridging two essential functions.
    • AI/ML: Multitask learning setups that handle more than one objective simultaneously (e.g., predicting both sentiment and topic in text data), balancing versatility with training complexity.
  8. Kicker → Fine-Tuning & Final Adjustments
    • Football: Specialists who deliver crucial points via field goals, sometimes deciding the outcome in the final seconds.
    • AI/ML: Fine-tuning or hyperparameter “nudges” that can significantly impact the final model performance (for instance, last-mile domain adaptation or calibration to handle imbalanced classes).
  9. Special Teams → Specialized Pipelines
    • Football: Unique scenarios—kickoffs, punts, returns—require highly specialized roles and tactics.
    • AI/ML: Separate pipelines or processes for edge cases like anomaly detection, one-shot learning, or extremely low-latency inferences.
  10. Team Captain → The Optimizer
C. Support & Maintenance: Staying Game-Ready
  1. Medical Staff → Debugging & Error Analysis
    • Football: Diagnose player injuries, recommend treatments, and coordinate recovery programs to ensure peak health.
    • AI/ML: Identify code bugs or data anomalies, troubleshoot performance drops, and devise patches or new data collection strategies to keep the model healthy and operational.
  2. Strength and Conditioning Coach → Regularization & Model Health
    • Football: Prevent overtraining, monitor fatigue levels, and ensure players maintain peak fitness throughout the season.
    • AI/ML: Techniques like dropout, weight decay, or data augmentation that guard against overfitting, ensuring the model remains robust and generalizable under various conditions.
  3. Film Analysts → Performance Metrics & Evaluation
    • Football: Examine game footage to dissect successes, failures, and opponent tendencies, providing tactical insights for improvement.
    • AI/ML: Continuous monitoring of precision, recall, F1-score, confusion matrices, and real-time dashboards to understand exactly where the model excels or falls short, fueling iterative refinement.
  4. Practice Squad → Experimental Sandbox / Shadow Mode
    • Football: Unrostered players or rookies who practice with the main team but don’t typically appear in official games.
    • AI/ML: Running experimental models in parallel—“shadow mode”—to gather performance stats without affecting production, allowing safe trials of new algorithms or features.
  5. Fans & Fan Communities → End Users / Developer Communities
    • Football: The supportive (and sometimes critical) audience that follows games, purchases tickets, and gives feedback on the team’s performance.
    • AI/ML: The user base or open-source developer community that directly interacts with the model’s outputs, shares feedback, and highlights both successes and pain points.
  6. Injury Reserve → Downtime for Model Debugging or Maintenance
    • Football: Injured players are temporarily sidelined for rehabilitation, opening a roster spot for alternates.
    • AI/ML: Models found to have serious bugs or vulnerabilities are taken offline for intensive debugging or retraining, possibly reverting to a prior stable version in the meantime.
D. Governance & Adaptation: Playing by the Rules, Staying Ahead
  1. Referees → Regulatory Compliance / Ethical Oversight
    • Football: Enforce fair play, penalize infractions, and ensure the game follows established rules.
    • AI/ML: Compliance teams and ethics boards ensure that the model adheres to regulations (GDPR, HIPAA) and responsible AI guidelines (bias mitigation, fairness checks).
  2. League Officials → AI Governance & Standards Bodies
    • Football: Oversee the entire league, create schedules, and revise official rules to maintain fairness and safety.
    • AI/ML: International or industry organizations (ISO, IEEE, NIST) and legislative bodies define standards, best practices, and frameworks (e.g., EU AI Act) that guide responsible innovation.
  3. Media Coverage → Public Perception & Market Influence
    • Football: Sports journalists and talk shows can sway public opinion, highlight controversies, or celebrate key victories.
    • AI/ML: Tech media and influencers spotlight breakthroughs (like GPT innovations) or raise alarm over data breaches and bias, shaping the public narrative around AI solutions.
  4. Rivalries → Adversarial Attacks
    • Football: Rival teams exploit patterns or weaknesses, forcing constant vigilance and adaptation.
    • AI/ML: Adversarial examples or malicious attacks (e.g., data poisoning, model inversion) push AI teams to build robust defenses, refine threat models, and continuously update detection strategies.
  5. Salary Cap → Resource Constraints
    • Football: Roster talent is limited by fixed budget caps, requiring strategic allocation of funds.
    • AI/ML: Training time, computational power, and data collection budgets are finite. Balancing these constraints is critical for delivering a performant, maintainable solution.
  6. Player Trades & Waivers → Transfer Learning & Model Updates
    • Football: Teams trade players to fix weaknesses or waive underperformers when better talent is found.
    • AI/ML: Transfer learning leverages pre-trained models (like BERT for NLP or ResNet for vision), and poorly performing models or architectures are “cut” in favor of improved approaches.
  7. Halftime Adjustments → Active Learning or Incremental Retraining
    • Football: Coaches regroup at halftime, analyze first-half gameplay, and modify tactics to exploit new insights or correct mistakes.
    • AI/ML: Dynamic or real-time systems that adapt to shifting data distributions (concept drift) by incrementally retraining or fine-tuning the model without waiting for a complete new release cycle.
E. Deployment & Impact: Where the Game is Won or Lost
  1. Stadium → Production Environment
    • Football: The arena where real fans watch in real time under high-pressure conditions (weather, crowd noise).
    • AI/ML: The live production environment that may face unpredictable user behavior, latency spikes, or data shifts. The model either stands up to real-world stressors or falters.
  2. Game Plan → Inference Pipeline
    • Football: The detailed strategy for the day’s opponent—coordinating offensive and defensive plays, contingency plans, and time management.
    • AI/ML: The end-to-end pipeline handling real-time predictions (data ingress, feature transformations, model inference, and output generation). Must be designed to handle scale, latency requirements, and failover scenarios.
  3. Play Clock → Latency Constraints
    • Football: Offenses must snap the ball before the play clock expires, or incur a penalty.
    • AI/ML: Hard deadlines for inference. If the system fails to respond within milliseconds for high-frequency trading, or seconds for a user-facing application, the results can be catastrophic (lost revenue, poor user experience).
  4. Scoreboard → Real-Time Dashboards / Monitoring
    • Football: Reflects the evolving game score and important stats.
    • AI/ML: Observability platforms that track CPU/GPU usage, throughput, error rates, and key model metrics (accuracy, recall, business KPIs). These dashboards guide immediate interventions and longer-term improvements.
Conclusion Like a well-run football franchise, a successful AI/ML initiative demands synergy across multiple roles and responsibilities. The “owner” (business stakeholder) sets the overarching objective; the “general manager” (data scientist) assembles the data and steers the project strategy; the “coaches” (training algorithms and specialized modules) shape how the model learns; the “players” (preprocessing pipelines, sub-models, and the core model itself) execute, adapt, and perform on the field of real-world data; and the “referees” (compliance bodies) ensure everything adheres to regulations and ethical principles.
By drawing on this analogy, even advanced concepts—like adversarial defenses, incremental retraining, or hyperparameter optimization—become relatable and memorable. Whether you’re explaining AI/ML to an executive team or to fellow researchers at a conference, framing the lifecycle as a high-stakes football season transforms abstract technicalities into a vivid narrative. Ultimately, the goal is the same as on any football Sunday: win on the field of production deployment—touchdown guaranteed.
If you found this analogy helpful or know other creative ways to bridge AI/ML and everyday life, feel free to share your thoughts below. Let’s keep pushing the boundaries of how we communicate technology!
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2025.01.22 22:19 Iamthesvlfvr Alaska May - Paper Pilots

Alaska May - Paper Pilots submitted by Iamthesvlfvr to PostHardcore [link] [comments]


2025.01.22 22:19 Infamous_Secret_1573 ONE PERSON NEEDED HAT TRICK

hi i just need one more person to accept!! my code is 78431620, let me know urs and ill do it as well. thank you in advance a bunch!!!!!:)
submitted by Infamous_Secret_1573 to TemuThings [link] [comments]


2025.01.22 22:19 mvxrco Newbie here, recommend me some mods!

HI! I recently dowloaded Skyrim Special Edition for PC and i’m looking for some mods like better textures, fluent animations, dragon riding and other quality of life mods. Could you recommend some that are easy to install and are actually good? Thanks in advance to who will help me!
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2025.01.22 22:19 luckychaingan Not sure if I’m welcomed here but I’d like to ask something here.

I assume a lot of people here are experienced when making cloth goods for their dolls and I wanted to ask about how I could go about making cloth goods for my figures.
Should I follow regular tutorials of how to make real life sized ones and then try downsizing for the figure? Or is there a specific way that it should be made for smaller things like dolls and figures?
Genuine question, and if this question ain’t welcomed here then I’ll delete the post.
submitted by luckychaingan to Dolls [link] [comments]


2025.01.22 22:19 GameProfessional Battlefield 2042 Gameplay 30 PS5

Battlefield 2042 Gameplay 30 PS5 submitted by GameProfessional to Youtubeviews [link] [comments]


2025.01.22 22:19 jonpeeji Why Order Management is the Next Frontier in the MACH and Composable Revolution

Why Order Management is the Next Frontier in the MACH and Composable Revolution submitted by jonpeeji to composable_commerce [link] [comments]


2025.01.22 22:19 Suitable_Ad4114 What's the dumbest thing you've ever said to another person?

submitted by Suitable_Ad4114 to AskReddit [link] [comments]


2025.01.22 22:19 GameProfessional Battlefield 2042 Gameplay 30 PS5

Battlefield 2042 Gameplay 30 PS5 submitted by GameProfessional to YouTubeSubscribeBoost [link] [comments]


2025.01.22 22:19 EffectiveAd8760 Zrób mi joi na kamerce wykonam każde polecenie bądź moją slythi

Zrób mi joi na kamerce wykonam każde polecenie bądź moją slythi submitted by EffectiveAd8760 to waleniepolska [link] [comments]


2025.01.22 22:19 sudomakeitrain Reminds me a bit of Trade Gothic, but definitely isn't. Can you identify this?

Reminds me a bit of Trade Gothic, but definitely isn't. Can you identify this? submitted by sudomakeitrain to identifythisfont [link] [comments]


2025.01.22 22:19 steffi8 What made you choose voyager?

What made you choose Voyager over say Lily58 low profile variants?
Since the later has more switches.
submitted by steffi8 to zsaVoyager [link] [comments]


2025.01.22 22:19 Immediate-Constant24 Peter Dills Dining in Los Angeles

Peter Dills Dining in Los Angeles submitted by Immediate-Constant24 to FoodLosAngeles [link] [comments]


2025.01.22 22:19 Relative_Glass1817 edgy non circular top surface

edgy non circular top surface https://preview.redd.it/61zs92m4emee1.png?width=708&format=png&auto=webp&s=4be5ee384370e00724cf34b7fbd5bdae0f89e99b
I am trying to print a shield and it has a dome, I have laid it on the side and using adeptive layer height but for some reason the top surface is a polygon with corners rather than a circle, do yall know how to fix this?
submitted by Relative_Glass1817 to crealityk2 [link] [comments]


2025.01.22 22:19 GameProfessional Battlefield 2042 Gameplay 30 PS5

Battlefield 2042 Gameplay 30 PS5 submitted by GameProfessional to YoutubeSelfPromotion [link] [comments]


2025.01.22 22:19 the_repgod Anyone getting lots of duplicates

i open 5 nominee packs and got 4 adam fox???
submitted by the_repgod to NHLHUT [link] [comments]


2025.01.22 22:19 BritishRCMNG Stolen from the main Formula One page, but it looks like Angela Cullen is back with Lewis

Stolen from the main Formula One page, but it looks like Angela Cullen is back with Lewis submitted by BritishRCMNG to CincinnatiF1 [link] [comments]


2025.01.22 22:19 ObscuredString Charmander the Stray, art by Pixeladdy on IG

Charmander the Stray, art by Pixeladdy on IG This scene is from Episode 11 - Hitokage, the Stray Pokemon.
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https://google.com/