00:00Your model gets 100% on training data but fails on new images.
00:03Classic overfitting, happens to every beginner.
00:06My day 80 model memorized the training set, looked perfect, was useless.
00:12Train accuracy 99%, validation 60%, red flag.
00:17You accidentally let test data leak into training, model cheats and you don't notice until production.
00:22I once got 99.9% accuracy because the same images were in both sets.
00:28The most dangerous because it looks like success.
00:32I'll create leakage live, then show how to detect it.
00:35Your deep network stops learning after layer 3, gradients become zero, common with sigmoid.
00:40My 10-layer model learned nothing, spent 3 days crying.
00:45ReLU and proper initialization fix this.
00:48I'll show a broken sigmoid model versus working real you.
00:51Ethan, bring my gradients back.
00:5399% of your sentiment data is neutral.
00:56Model just predicts neutral and gets 99% accuracy but is useless.
01:01My fraud detection model said no fraud every time.
01:0499.9% accurate, completely broken.
01:07Accuracy is meaningless.
01:09Look at F1's score.
01:10I'll create a 99.1 dataset and break the model live.
01:14Anastasia, save the minority class.
01:17Your loss suddenly stops moving or explodes to NAN, classic gradient nightmare.
01:21I once trained for 6 hours and got NAN at Epoch 3, cried real tears.
01:27Too high learning rate, exploding.
01:29Sigmoid plus deep nets, vanishing.
01:32I'll change the learning rate live, watch it explode, then fix with 0.0001.
01:37Anastasia, bring my gradients back to life.
01:39You split by random but same patient appears in train and test.
01:43Instant leakage.
01:44I did this with medical data, 99% accuracy, completely fake.
01:48Always split by patient ID, timestamp, or stratified.
01:52I'll break the day 85 app live with bad split fix with stratified shuffle split.
01:56Anastasia, save me from fake accuracy.
01:5810% of your training data has wrong labels, model learns garbage.
02:04I was trained on CIFAR 10 with swapped cat-dog labels, hilarious disaster.
02:08Real datasets are 5 to 15% noisy.
02:12You must handle it.
02:14I'll flip 10% of labels live, watch accuracy drop from 75% to 60%.
02:1899% accuracy sounds amazing, until you realize it predicts no fraud every time.
02:24My fraud model had 99.9% accuracy and caught zero frauds.
02:30Accuracy lies when classes are imbalanced.
02:31Always use F1, AUC, or precision slash recall.
02:35Your data is accidentally sorted by label.
02:37First 90% of every batch is negative.
02:40I did this for three days.
02:41Model only learned the first class.
02:43Always shuffle with shuffle equals true, or shuffle dataset.
02:47I'll sort the day 85 data live.
02:49Watch it learn only one sentiment.
02:51You forgot to normalize pixel values.
02:53Some features 0255, others 01.
02:56Optimizer goes crazy.
02:58My model wouldn't train at all, until I scaled.
03:01Four hours wasted.
03:02Always normalized to similar ranges.
03:04Standard scaler, or slash 255.
03:07I'll remove scaling from day 85 image model.
03:09Watch training die.
03:11Anastasia, scale me properly.
03:13Your model was perfect in January.
03:15By June it's useless because the world changed.
03:18My sentiment model hated new slang.
03:21Accuracy dropped 20% in three months.
03:24Monitor predictions and retrain regularly.
03:26Concept drift is inevitable.
03:28I'll simulate six months of drift live.
03:30Watch accuracy collapse.
03:31Anastasia, keep my model young forever.
03:34Your beautiful model is 300 membros.
03:36Crashes on phones and costs 100 month to serve.
03:39My first mobile app took eight seconds to load.
03:42Users deleted it.
03:43Quantization, pruning, distillation, reduce size 10 times with less than 1% accuracy loss.
03:49I'll quantize day 85 model from 120 megabytes to 12 megabytes live.
03:54Ethan, make me lightweight and fast.
03:56First user after deploy waits 45 seconds while model loads.
03:59They leave forever.
04:00My streamlit app was perfect.
04:02Except the first person always bounced.
04:05Pre-warm the model or use lazy loading with spinner.
04:07I'll show cold start right pointing arrow add street spinner and pre-load trick.
04:12Anastasia, warm me up instantly.
04:14User uploads 100 mem by image.
04:16Streamlit eats all memory and crashes for everyone.
04:19I killed my shared app with one big photo.
04:21Felt terrible.
04:23Resize images early.
04:24Use ST cache wisely.
04:25Limit upload size.
04:27I'll upload a 50 megabytes image live.
04:29Watch it die.
04:30Then fix with resize.
04:31Ethan, don't let me crash the party.
04:33You used binary cross entropy instead of categorical.
04:37Model becomes arrogantly overconfident.
04:40My sentiment model said, I'm 100% sure on everything.
04:43Total clown.
04:44Always match lost output.
04:46Binary versus categorical versus focal.
04:49I'll swap the loss live.
04:50Watch confidence go from 70% to 99.999%.
04:54Ethan, teach my model some humility.
04:57Your model learns fast at first, then stops dead.
04:59No learning rate decay.
05:00I trained for 50 epochs and wasted the last 40.
05:04Classic.
05:05Reduce LR on Plateau or Cosine Decay, essential for deep nets.
05:10I'll add Reduce LR on Plateau Live.
05:12Watch it suddenly start learning again.
05:13Anastasia, make my model keep improving forever.
05:17Your validation loss jumps up and down like crazy
05:19because validation data isn't shuffled.
05:21I thought my model was drunk.
05:23Turns out it was just the validation order.
05:26Always shuffle both train and validation every epoch.
05:28I'll turn off validation shuffle.
05:30Watch the chaos, then fix it.
05:32Ethan, sober up my validation.
05:35Your app is perfect locally.
05:36Deploy and it's 500 error city.
05:38The ultimate betrayal.
05:39I've cried at 2 a.m.
05:41Because of this exact curse.
05:42Every developer's nightmare.
05:44Requirements.txt, Docker, or exact environment replication.
05:47No excuses.
05:48I'll show local success, right-pointing arrow production,
05:52fail, right-pointing arrow fix with exact requirements.txt plus Docker.
05:56Anastasia, make it work everywhere.
05:58I'm tired of this curse.
05:59You thought your test set was clean,
06:01but 5% of images appear in training with different labels.
06:04I got 98% accuracy.
06:07Felt like a genius until I discovered the overlap.
06:10Use hashing or image similarity checks before training.
06:14I'll inject 5% duplicates live.
06:16Watch accuracy lie through its teeth.
06:18Sophia, clean my dirty test set.
06:21Your sentiment model cuts reviews at 200 words,
06:23loses the punchline every time.
06:25My 500-word movie review became I loved, suddenly negative.
06:30Truncate from the end or use sliding windows.
06:33I'll truncate from start versus end, watch sentiment flip.
06:36Your predictions are different every time,
06:38because batch norm is still in training mode.
06:41My app was literally random, terrifying.
06:44Model.eval in PyTorch, or compile with correct mode in TF.
06:48I'll forget to freeze batch norm.
06:50Watch predictions dance.
06:52You upgrade one package, suddenly five others break.
06:54Welcome to dependency hell.
06:56I once spent 8 hours fixing Numpy plus TensorFlow version war.
07:01Pin exact versions, TensorFlow equals equals 2.15.0, Numpy a 1.24.
07:06I'll show a working app, upgrade Numpy, total crash, fix with exact pins.
07:12Your app works fine for 10 users.
07:14By user 100, it crashes from GPU memory leak.
07:17My hugging face space died after two hours.
07:19So embarrassing.
07:21Clear session, delete variables.
07:23Use tf.keras.backend.clear underscore session.
07:25I'll run 200 predictions.
07:28Watch memory explode, then fix with clear session.
07:31You run the same code twice, different accuracy.
07:34No one can reproduce your work.
07:36My boss asked for reproducible results.
07:38I had nothing.
07:40Set all seeds, Python, number py, TensorFlow, and tf.deterministic ops.
07:45I'll run without seeds, different results.
07:47Add four lines, identical every time.
07:50Your images are float64 instead of float32, 10x slower and eats all RAM.
07:55My app was dog slow.
07:57Turns out it was data type.
07:59Always use float32 for images, and eight for quantized models.
08:03I'll change to float64.
08:05Watch it crawl, then fix with S type, float32.
08:09Your app is perfect locally.
08:10Deploy in its 500 error city, the ultimate betrayal.
08:13I've cried at 2 a.m. because of this exact curse.
08:16Requirements.txt, docker, or exact environment replication.
08:20No excuses.
08:22I'll show local success.
08:23Production fail.
08:24Fix with exact requirements.
08:26Txt.
08:27You now have the complete survival kit.
08:30No more blind failures.
08:32These 12 fixes saved my career multiple times.
08:36Professional AI engineers master these exact issues.
08:40You're no longer a beginner.
08:42You're battle tested.
08:43Next time something breaks, run through our 12-question checklist.
08:4995% of issues are covered.
08:52I printed this flowchart.
08:54It's above my desk.
08:57Tomorrow, day 87.
08:59Full debugging master class with this exact system.
09:0365% of not parfait.
09:0561% of knowledge.
09:05Do!
09:0695% ofowanie.
09:06diu.
09:0786% ofias.
09:08The time.
09:08I printed this weather.
09:09From 787.
09:09The time.
09:10By the.
09:10ə.
09:12The Air Erik.
09:14The Air mortality.
09:14Yeah!
09:15The Big Four.
09:16Is wrong!
09:1683% of upright.
09:16It's the helfen.
09:16I printed this bug.
09:17ously well switchedisonfully.
09:18Number seven.
09:19We didn't.
09:19Look.
09:20They daddy's bored.
09:20About 6-6 ia.
09:21Yong caughtamento.
09:22Enemy Bettler.
09:23Batters.
09:23In direct.
09:23Night.
09:24하기悪at.
09:24bow al Stuff Marshall.
09:25App 있습니다 breeze.
09:25deeper in down.
09:26ch counselor.
09:26ичесcincks t quest.
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