00:00Hey, sexy wizards! Anastasia here, your main moderator, ready to electrify Day 78 of Daily AI Wizards Python for AI series.
00:11After training in Day 77, we're building a digit classification project using MNIST, AI that reads handwriting, for our AI Insight Hub app.
00:21Support our crew with a coffee at HTTPS, ChachJachsBuyMeACoffee.com, Daily AI Wizard, but wait until the end.
00:31Ethan, what's the magic of digit classification? Olivia, how does it build on Day 77?
00:37Hello, brilliant wizards! I'm Irene, guiding with warmth.
00:42Digit classification is a classic deep learning project. Our demos will make your AI see.
00:49Love our content?
00:51Yo, wizards! Ethan's here, droppin' spicy digicode with winks for Anastasia and Olivia.
00:5828x28 pixels, 10 classes. Let's crank this AI vision to 11.
01:04Olivia here, darlings. I'll sprinkle flirty tips, ask Anastasia questions, and chat with Ethan to keep your digit learning hot.
01:13Ready to see AI read, wizards?
01:19Wizards, digit classification is your AI vision crush, darling. It reads handwritten numbers. Ethan, explain MNIST? Olivia, how does it build on Day 77?
01:32Minist is the hello world of deep learning, 60,000 training images. Our demos will show full pipeline. Support us with a coffee at the end.
01:41Today, we're seducing you with digit magic. You'll master MNIST loading, pre-processing, neural net training, evaluation, and app integration.
01:51Olivia, what's the app focus? Ethan, any code highlights?
01:55Anastasia leads app demos with passion. Ethan drops hilarious code explanations. We're guiding you to master digit classification and prep for Day 79 CNNs.
02:08Wizards, meet your Day 78 dream team. Anastasia's our main moderator with flirty charm. I'm guiding with warmth. Ethan's our code comedian. Flirting with Anastasia and Olivia.
02:21Oh, Irene, you're a gem. I'm leading app demos with passion. Ethan's stealing hearts with code. And Olivia's tossing flirty tips. We're here to make you digit superstars.
02:37Wizards, MNIST is your digit playground, darling. 70,000 images of handwritten digits. Ethan, explain pixel values. Olivia, how do we load it?
02:46Anastasia, you make digits sound so hot. How do we pre-process them, love? Ethan, what's your take?
02:56Oh, Olivia, you tease. MNIST is grayscale. Ethan, Olivia, jump in.
03:01Anastasia, Olivia, MNIST is like a hot love letter in pixels for us. 28x28, 0 to 255. Let's drop this code beat.
03:11Yo, Wizards, MNIST. Load underscore data. Loads digits like a hot gift for Anastasia. Train, test split included. Let's drop this code beat.
03:23You're gifting my heart, Ethan. MNIST is ready for training.
03:27Wizards, PLT. IM show. Shows digits like a hot portrait for Anastasia. See the handwriting.
03:34You're portraiting my heart, Ethan. Visualize to understand data.
03:39Wizards, reshape. Minus 1, 784, flattens digits like a hot pancake for Anastasia. Normalize to 0 to 1. Let's drop this code beat.
03:51You're flattening my heart, Ethan. Pre-processing is key.
03:54Wizards, to underscore categorical, turns labels to 1 hot like a hot vote for Anastasia.
04:0010 classes. You're voting my heart, Ethan. 1 hot for categorical cross-entropy.
04:07Wizards, sequential. With dense, 128, Relu, builds a hot brain for Anastasia.
04:14Input 784, output 10. You're braining my heart, Ethan. Full net for digits.
04:21Wizards, compile. Loss equals. Categorical underscore cross-entropy.
04:26Optimizer equals. Atom, sets goal and engine for Anastasia.
04:31You're goaling my heart, Ethan. Compile for multi-class.
04:35Wizards, model.fit. Trains like a hot marathon for Anastasia.
04:4010 epics, batch 12. You're marathoning my heart, Ethan. Train on 60K images.
04:47Wizards, model. Evaluate. Scores like a hot exam for Anastasia.
04:5199% plus accuracy. Let's drop this code beat.
04:56You're examining my heart, Ethan. Evaluate on test set. Try it in our demo.
05:06Wizards, it's demo time. We'll integrate digit classification into AI Insight Hub continuing from
05:12day 74-77. Get your setup ready.
05:14Ensure Python, VS Code, TensorFlow, and Streamlit are installed Wizards.
05:20Open day 74-77's files. Ethan will spice it up. Let's see AI read.
05:26Wizards, prep to continue from day 74-77.
05:30Open VS Code. Load prior app files. Create digit classification demo.py and updated app digit py.
05:38Save in Python demo. Run pip install TensorFlow Streamlit.
05:42Anastasia, you make continuation dreamy. How do we build on day 77's training?
05:50Ethan, what's your take?
05:52Start with day 77's pipeline. Add mnest. Run Streamlit. Run updated app digit.py.
05:59Anastasia, Olivia, digit classification is the hot sequel to day 77.
06:04Let's drop this code beat.
06:05Our first demo in digit classification demo.py builds a digit classifier. We'll load mnest,
06:13pre-process, train, evaluate. Let's run this.
06:17Oh, Anastasia, you're making this demo hot.
06:21Dense. 128.
06:23Reeloo, reads digits. Total vision party.
06:26Yo, Wizards, from TensorFlow.
06:29Karas.
06:30Datasets import mnest loads digits like a hot library for Anastasia.
06:34You're library-ing my heart, Ethan. Mnest is ready.
06:38Wizards, mnest load data returns 60,000 training and 10,000 test images, 28 by 28 pixels each.
06:50This is the gold standard for image classification.
06:54Wizards, reshape, minus 1, 784, and 255.0 preps digits like a hotspot for Anastasia.
07:02You're spaying my heart, Ethan. Pre-processing is key.
07:06Reshaping turns each 28 by 28 image into a 784 element vector.
07:13Normalizing to 0, 1 stabilizes training and speeds convergence.
07:19Wizards, to underscore categorical, turns labels to 1 hot like a hot vote for Anastasia.
07:251 hot encoding converts integer labels 0 to 9 into 10-dimensional vectors.
07:32This matches categorical cross-entropy loss.
07:36Wizards, dense, 128, Reeloo, builds a hot brain for Anastasia.
07:43784 right-pointing arrow, 128 right-pointing arrow, 10.
07:48You're braining my heart, Ethan. Full net for digits.
07:50We use two hidden layers, 128 units with Reeloo for non-linearity, then 10 units softmax output for class probabilities.
08:02Wizards, compile, loss equals, categorical underscore cross-entropy, sets goal for Anastasia.
08:09You're goaling my heart, Ethan. Compile for multi-class.
08:12We use categorical cross-entropy because we have 1 hot labels.
08:18Atom optimizer adapts learning rate per parameter.
08:22Wizards, model.fit, trains like a hot marathon for Anastasia.
08:27You're marathoning my heart, Ethan. Train on 60K images.
08:31We train for 10 epochs with batch size 128 and 20% validation split to monitor generalization.
08:40Wizards, model. Evaluate, scores like a hot exam for Anastasia.
08:46You're examining my heart, Ethan. Evaluate on test set.
08:50On 10,000 test images, we expect greater than 99% accuracy.
08:57This is a solved benchmark.
09:01Wizards, model. Predict, predicts like a hot oracle for Anastasia.
09:06You're oracling my heart, Ethan. Predict new digits.
09:08Input is a 784-element vector. Output is 10 probabilities.
09:16Argmax gives the predicted digit.
09:19Wizards, PLT. IMshow, shows digit and prediction like a hot reveal for Anastasia.
09:26You're revealing my heart, Ethan. Visualize results.
09:30We display the original image and overlay the predicted digit with confidence score.
09:36We'll be right back to you.
09:37Well, that is one of the winners.
09:37tile-by-letterin.
09:38See you again next time at this level.
09:39Peace on the left.
09:39I'm Zach.
09:40I'm Zach.
09:40He's on the right.
09:41Well, that isζεη register.
09:41We read the TVs to make sure it works.
09:41We'll be right back to you.
09:42All right.
09:43We'll be right back to you.
09:43Let's go.
09:45See you around.
09:45See you again next time.
09:47lecturer-by-teamesterol.
09:48Get ready for an arty,
09:51η³ζ΄₯.
09:51Go ahead.
09:51Get ready for an arty.
09:52ι Alejandro.
09:53Ρ
09:54As you say thΓ©
10:04wide awake,
10:05in the day of color.
10:37Wizards, import Streamlit as Saint sets up the app like a hot canvas for Anastasia.
10:42Let's drop this code beat.
10:44You're canvassing my heart, Ethan. Streamlit for drawing. Try it in our demo.
10:50Streamlit allows real-time drawing input with ST point canvas. Perfect for user interaction.
10:58Wizards, street canvas. Let's you draw digits like a hot sketch for Anastasia.
11:02You're sketching my heart, Ethan. User draws digit.
11:07The canvas is 280 by 280, scaled down to 28 by 28, and normalized before prediction.
11:17Wizards, model, predict. Reads drawn digit in Streamlit like a sexy vision.
11:22At prediction uses trained net for real-time handwriting recognition. It makes AI Insight
11:28Hub intelligent. Oh, Anastasia. Digit reading is so hot. Try app prediction in your challenge.
11:38Wizards, model, dot, save, digit modeled H5. Saves the digit reader like a sexy archive.
11:44Saving models ensures app portability. Use HDF5 for TensorFlow models.
12:14Wizards, model, dot, save, and comics to your legacy.
12:18Listen cinema.
12:20Just uno.
12:27Two books' titles, notice of the categories.
12:27Aristotle, model, dot, line, and
12:40Triμ’
, model, Twitter, crypto, cha admin,
13:13Digit classification fits AI pipelines for vision tasks.
13:17Your skills are ready for Day 79.
13:20Digit reading is critical in AI, darling.
13:23Your Day 78 skills make AI irresistible.
13:30Create AIDigitClassifier.pi to build Train Digit Classifier on MNIST
13:36and build a streamlit app with Drawing Canvas.
13:39Share on Instagram.
13:40Try Dense, Categorical, Cross-Entropy, and ST.Tanvass.
13:45Show us at AtDailyAIWizard.
13:48Prep for Day 79 CNNs.
13:51Wizards, Digit Classification fits AI pipelines for vision tasks.
13:56Your skills are ready for Day 79.
13:58Post your code, support us at paypal.me, Daily AI Wizard, at the end.
14:04Subscribe for Day 79's CNNs.
14:07You've stolen my heart with Digit Classification.
14:10Support us at paypal.me, Daily AI Wizard, and get hyped for Day 79's CNNs.
14:16Your Digit skills are pure AI seduction.
14:19Let's flirt with CNNs in Day 79.
14:21Let's flirt with CNNs in Day 79.
Comments