[{"data":1,"prerenderedAt":77},["ShallowReactive",2],{"term-b\u002Fbatch":3,"related-b\u002Fbatch":60},{"id":4,"title":5,"acronym":6,"body":7,"category":40,"description":41,"difficulty":42,"extension":43,"letter":44,"meta":45,"navigation":46,"path":47,"related":48,"seo":54,"sitemap":55,"stem":58,"subcategory":6,"__hash__":59},"terms\u002Fterms\u002Fb\u002Fbatch.md","Batch",null,{"type":8,"value":9,"toc":33},"minimark",[10,15,19,23,26,30],[11,12,14],"h2",{"id":13},"eli5-the-vibe-check","ELI5 — The Vibe Check",[16,17,18],"p",{},"A batch is a small group of training examples that the model processes at once before updating its weights. Instead of learning from examples one at a time (slow) or all at once (doesn't fit in memory), you feed it in batches. Batch size is a hyperparameter — bigger batches are faster but sometimes learn worse. AI loves batches.",[11,20,22],{"id":21},"real-talk","Real Talk",[16,24,25],{},"A batch is a subset of training examples processed together in a single forward and backward pass. Batch size is a key hyperparameter: larger batches provide more stable gradient estimates but require more memory and can hurt generalization; smaller batches introduce noise that can be regularizing. Mini-batch gradient descent is the standard training approach.",[11,27,29],{"id":28},"when-youll-hear-this","When You'll Hear This",[16,31,32],{},"\"Set batch size to 32.\" \u002F \"Out of memory — reduce the batch size.\"",{"title":34,"searchDepth":35,"depth":35,"links":36},"",2,[37,38,39],{"id":13,"depth":35,"text":14},{"id":21,"depth":35,"text":22},{"id":28,"depth":35,"text":29},"ai","A batch is a small group of training examples that the model processes at once before updating its weights.","intermediate","md","b",{},true,"\u002Fterms\u002Fb\u002Fbatch",[49,50,51,52,53],"Epoch","Training","Gradient Descent","Hyperparameter","Loss Function",{"title":5,"description":41},{"changefreq":56,"priority":57},"weekly",0.7,"terms\u002Fb\u002Fbatch","IYRJMle8ZugJFyew9nkk4YI9IV6nEQMFMK8z_m_8L6A",[61,64,68,71,74],{"title":49,"path":62,"acronym":6,"category":40,"difficulty":42,"description":63},"\u002Fterms\u002Fe\u002Fepoch","An epoch is one complete pass through your entire training dataset. If you have 100,000 examples, one epoch means the model has seen all 100,000 once.",{"title":51,"path":65,"acronym":6,"category":40,"difficulty":66,"description":67},"\u002Fterms\u002Fg\u002Fgradient-descent","advanced","Gradient Descent is how an AI learns — it's the algorithm that nudges the model's weights in the right direction after each mistake.",{"title":52,"path":69,"acronym":6,"category":40,"difficulty":42,"description":70},"\u002Fterms\u002Fh\u002Fhyperparameter","Hyperparameters are the settings you configure BEFORE training starts — as opposed to parameters (weights) which the model learns ON ITS OWN.",{"title":53,"path":72,"acronym":6,"category":40,"difficulty":42,"description":73},"\u002Fterms\u002Fl\u002Floss-function","The loss function is the AI's score of how badly it's doing.",{"title":50,"path":75,"acronym":6,"category":40,"difficulty":42,"description":76},"\u002Fterms\u002Ft\u002Ftraining","Training is the long, expensive process where an AI learns from data.",1776518259692]