Roadmap for building Scalable AI Agents in 2026!! https://t.co/fA2lqEwxKL
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Python Roadmap to try in 2026..
A practical path from beginner to advanced.
• Learn fundamentals: syntax, loops, functions, OOP
• Practice logic with real problems, not just tutorials
• Master core libraries: Pandas, NumPy, Matplotlib
• Build web apps using Flask or https://t.co/7irpMUcLOb
A practical path from beginner to advanced.
• Learn fundamentals: syntax, loops, functions, OOP
• Practice logic with real problems, not just tutorials
• Master core libraries: Pandas, NumPy, Matplotlib
• Build web apps using Flask or https://t.co/7irpMUcLOb
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Look! 🤖This Lynx M20 robot dog has passed performance testing in the extremely cold snowy region of Gulunbuir. ❄️It can not only walk in the snow, but also perform high-difficulty maneuvers such as running in complex snowy terrain.🏃 Let's give a thumbs up to Chinese https://t.co/z89AAl4evk
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Beijing Studio to Boost Drone Maintenance Training Amid Industry Boom
Yang Long’s Drone Assembly and Maintenance Master Studio, recognized by Beijing’s Dongcheng District, is developing accessible training tools for drone technicians. Using a “Beijing R&D, Jiangsu testing”model, https://t.co/LZBFbRuUMY
Yang Long’s Drone Assembly and Maintenance Master Studio, recognized by Beijing’s Dongcheng District, is developing accessible training tools for drone technicians. Using a “Beijing R&D, Jiangsu testing”model, https://t.co/LZBFbRuUMY
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gradient descent: this is the primary way models learn by taking the error at the end and calculating which way to nudge billions of weights to make that error smaller it is like trying to find the bottom of a dark valley by feeling the slope of the ground with your feet [32/100]
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backpropagation: to update the weights at the very beginning of the model we have to pass the error backward through every single layer using the chain rule from calculus it is a massive game of pass the blame until every parameter knows how it contributed to the mistake [31/100]
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weight initialization: before a model learns anything its billions of connections are just random numbers if you start with numbers that are too big the math explodes and if they are too small the model never wakes up so we have to carefully pick a starting point [30/100]
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weights and parameters: the billions of parameters in an ai are just the knobs that we turn during training each parameter is a tiny number that determines how much influence one neuron has over another in the final prediction [29/100]
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9 AI Skills That Will Make you More Employable in 2026!! https://t.co/rFCQYYjxF0
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layer normalization: training a giant model is a constant battle against numbers getting too large or too small layer normalization acts like a thermostat for the math by constantly rescaling the values so they stay in a healthy range for learning [19/100]
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residual connections: as models get deeper the signal can get lost or distorted so we add shortcuts that let the original input skip ahead and mix with the output of a layer this keeps the gradients healthy and allows us to train networks with hundreds of layers [18/100]
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dataset quality: we used to just scrape everything but now we realize that garbage in garbage out is the law of the land a few thousand pages of high-quality textbooks are worth more to a model than millions of pages of low-quality social media arguments [11/100]
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AI Engineering Roadmap for 2026!! https://t.co/bhfE5YoJbn
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scaling laws: researchers found a beautiful mathematical relationship where if you double the data and double the compute the model error drops in a predictable line it suggests that we haven't hit the ceiling of intelligence yet as long as we can keep feeding the machines
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context window: this is the short-term memory of the model and it has a hard limit once you reach the end of the window the model literally starts to forget the beginning of the conversation because it can only hold so many tokens in its immediate focus at once [9/100]
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17 Rules for writing Chatgpt Prompts to learn in 2026!!
#chatgpt #ai #tech #Productivity https://t.co/O1Mwbkmdoi
#chatgpt #ai #tech #Productivity https://t.co/O1Mwbkmdoi
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high-dimensional space: human brains can't really visualize 4096 dimensions but that is where tokens live in a massive mathematical coordinate system that allows the model to map out extremely subtle relationships between every concept known to man [6/100]
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AI Engineering Learning Roadmap to learn in 2026!! https://t.co/CPj30frrT6
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byte pair encoding: bpe is the algorithm that decides where to cut these tokens it looks at millions of documents and finds the most frequent character combinations to merge into a single unit which helps the model handle millions of different words without needing a massive
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tokens: tokens are the atoms of language for an ai we don't feed it raw words because language is too messy and complex instead we break text down into chunks that the model can turn into math which is why it sometimes struggles with things like counting letters or spelling
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I create content that makes AI easy to understand. I share practical ideas for everyday creative work.
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