Day 77: Training a Neural Network – From Data to Intelligence | #DailyAIWizard - video Dailymotion

DailyAIWizard

Kick off with 1970s jazz, coffee, and neural training! Join Anastasia, Irene, Ethan, Sophia, and Olivia as we master epochs, validation, early stopping for Iris. Sophia leads two demos, Ethan drops flirty code. Support at PayPal.me/DailyAIWizard! Get ready for Day 78: Digit Classification! Subscribe, like, share your ai_iris_training.py!  pay a coffee:  https://buymeacoffee.com/dailyaiwizard  #PythonForAI #LearnPython #AICoding #DailyAIWizard Hashtags:  #Python #LearnPython #PythonForAI #AICoding #PythonTutorial #CodingForBeginners #ScikitLearn #Datasets #AIProgramming #TechTutorial #MachineLearning #DailyAIWizard #CodeTheFuture Tags:  Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Scikit-learn, Datasets, AI Programming, Tech Tutorial, Python 3, Coding Journey, VS Code, Beginner Programming, Machine Learning, Data Science, DailyAIWizard, Code the Future 1970s jazz, retro jazz, morning coffee, ocean views, vintage vibe, jazz playlist, positive music, coffee time, 70s music, smooth jazz, beachside jazz, funky jazz, relaxation music, morning vibes, instrumental jazz, Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, TensorFlow, Neural Networks, AI Programming, Tech Tutorial, Python 3, Coding Journey, VS Code, Beginner Programming, Machine Learning, Data Science, DailyAIWizard, Code the Future  Timestamps: 00:00 Training Neural Network 01:27 Why Training Neural Network? 02:46 What is Training Neural Networks? 05:50 Demo 13:25 Best Practice 13:59 Challenge

Transcript

00:00Hey, sexy wizards. Anastasia here, your main moderator, ready to ignite Day 77 of Daily AI Wizards Python for AI series.

00:10After optimizers in Day 76, we're diving into training a neural network, the art of turning data into intelligence, for our AI Insight Hub app.

00:19Support our crew with a coffee at paypal.me Daily AI Wizard.

00:24Ethan, what's the magic of training?

00:26Sophia, how does it build on Day 76?

00:28Hello, brilliant wizards. I'm Irene, guiding with warmth.

00:34Training is where models learn from data.

00:37Our demos will show you how to avoid overfitting and converge fast.

00:42Love our vibe?

00:44Yo, wizards.

00:46Ethan's here, dropping spicy training code with winks for Sophia and Olivia.

00:52Epochs, batches, validation. Let's crank this AI brain to 11.

00:56Sophia here, Ethan, and your charms got me blushing.

01:02I'm pumped to lead our app component demos and make training sizzle.

01:07Let's turn data into wisdom, wizards.

01:11Olivia here, darlings.

01:12I'll sprinkle flirty tips, ask Anastasia questions, and chat with Ethan to keep your training learning hot.

01:20Ready to learn, wizards.

01:26Wizards, training is your neural nets gym session, darling.

01:30It adjusts weights to minimize loss.

01:32Ethan, explain epochs versus batches.

01:35Sophia, how does it build on Day 76?

01:37Training turns raw data into predictions.

01:42Our demos will show how to monitor and improve learning.

01:47Today, we're seducing you with training magic.

01:49You'll master epics, batch size, validation, overfitting, early stopping, and app integration.

01:56Sophia, what's the app focus?

01:59Irene, any code highlights?

02:01Sophia leads app demos with passion.

02:04Ethan drops hilarious code explanations.

02:09We're guiding you to master training and prep for Day 78's digit classification.

02:15Wizards, meet your Day 77 dream team.

02:20Anastasia's our main moderator with flirty charm.

02:24I'm guiding with warmth.

02:27Ethan's our code comedian, flirting with Sophia and Olivia.

02:32Oh, Irene, you're a gem.

02:33Sophia's leading app demos with passion, Ethan's stealing hearts with code, and Olivia's tossing flirty tips.

02:40We're here to make you training superstars.

02:47Wizards, training is your neural nets learning loop, darling.

02:50Forward loss, backward update, repeat.

02:54Ethan, explain the loop.

02:55Sophia, how does it use Day 76's optimizer?

03:00Anastasia, you make training sound so hot.

03:03How does it avoid overfitting, love?

03:06Ethan, what's your take?

03:08Oh, Olivia, you tease.

03:10Training refines weights.

03:12Ethan, Sophia, jump in.

03:14Anastasia, Olivia, training is like a hot dance for Sophia.

03:19Forward, loss, backward, update.

03:22Let's drop this code beat.

03:25Yo, wizards.

03:26Epics equals 50 means 50 full data passes, like a hot marathon for Sophia.

03:32More epics, more learning.

03:34Let's drop this code beat.

03:37You're marathoning my heart, Ethan.

03:40Too many epics overfit.

03:44Wizards, batch underscore size equals 32 trains on mini-batches like a hot snack for Sophia.

03:49Faster, noisier.

03:52Let's drop this code beat.

03:55You're snacking my heart, Ethan.

03:58Batch size affects speed and stability.

04:02Wizards, validation underscore split equals 0.2 holds out 20% like a hot test for Sophia.

04:09Watch for overfitting.

04:11Let's drop this code beat.

04:13You're testing my heart, Ethan.

04:15Validation, validation prevents memorization.

04:20Wizards, overfitting is like a hot student cramming for Sophia.

04:25Perfect on train, fails on test.

04:28Let's drop this code beat.

04:30You're cramming my heart, Ethan.

04:33Use validation to stop it.

04:37Wizards, early stopping, stops training like a hot referee for Sophia.

04:41When ValLoss stops improving, let's drop this code beat.

04:47You're refereeing my heart, Ethan.

04:50Early stopping saves time.

04:54Wizards, full pipeline is like a hot factory for Sophia.

04:58Data model train validates stop.

05:00Let's drop this code beat.

05:03You're factoring my heart, Ethan.

05:06Full pipeline for robust training.

05:08Wizards, model checkpoint, saves the best model like a hot trophy for Sophia.

05:15Only when ValLoss improves, let's drop this code beat.

05:20You're trophying my heart, Ethan.

05:23Save best weights.

05:26Wizards, tf.karas.models.load underscore model, loads the best model like a hot comeback for Sophia.

05:34Reuse in app, let's drop this code beat.

05:37You're comebacking my heart, Ethan.

05:42Load for inference.

05:44Try it in our demo.

05:51Wizards, it's demo time.

05:54We'll integrate full training pipeline into the neural net classifier for AI Insight Hub,

05:58continuing from days 72 to 76.

06:02Get your setup ready.

06:04Ensure Python, VS Code, TensorFlow, and Streamlit are installed.

06:09Wizards, open days 72, 76 as files.

06:12Ethan will spice it up.

06:13Let's train.

06:14Wizards, prep to continue from days 72 to 76.

06:18Open VS Code, load prior app files, create iris-training-demo.py and updated-app-iris-training.py, save in a Python demo.

06:31Run pip install TensorFlow Streamlit.

06:33Sophia, you make continuation dreamy.

06:38How do we build on day 76's optimizer?

06:41Ethan, what's your take?

06:43Start with day 70 SIXS model, add training pipeline, run streamlit run updated underscore app underscore iris underscore training dot py.

06:51Sophia, Olivia, training is the hot SQL to day 76.

06:58Let's drop this code beat.

07:02Our first demo in iris underscore training underscore demo dot py shows full training pipeline.

07:08We'll build, train with validation, early stopping, and save best model.

07:13Let's run this.

07:15Oh, Sophia, you're making this demo hot.

07:19Early stopping, plus model checkpoint, total training mastery.

07:25Wizards, from TensorFlow dot keras dot callbacks import early stopping, model checkpoint loads smart training tools for Sophia.

07:33Let's drop this code beat.

07:37You're tooling my heart, Ethan.

07:39Callbacks automate training.

07:42Try it in our demo.

07:43Wizards, load underscore iris, loads data like a love letter to Sophia, prepping for AI training.

07:52Let's drop this code beat.

07:55You're loading my heart, Ethan.

07:58Iris data is ready for training.

08:02Wizards, standard scaler, scales data like a hot transformation for Sophia.

08:08Neural nets love normalized input.

08:10Let's drop this code beat.

08:11Wizards, sequential, stacks layers like a hot tower for Sophia.

08:25Input, hidden, output.

08:27Let's drop this code beat.

08:30You're stacking my heart, Ethan.

08:33Sequential builds the net.

08:36Wizards, model dot compile, sets loss and optimizer like a hot goal for Sophia.

08:42Let's drop this code beat.

08:45You're goaling my heart, Ethan.

08:48Compile defines learning.

08:52Wizards, early stopping, patience equals 5, and model checkpoint, are smart coaches for Sophia.

08:59Let's drop this code beat.

09:02You're coaching my heart, Ethan.

09:05Callbacks automate training.

09:07Wizards, model dot fit, callbacks equals callbacks, trains smart like a hot pro for Sophia.

09:15Let's drop this code beat.

09:18You're proing my heart, Ethan.

09:21Callbacks make training robust.

09:25Wizards, load underscore model, best underscore model dot h5, loads the champion like a hot comeback for Sophia.

09:32You're comebacking my heart, Ethan.

09:37Load for inference.

09:40Wizards, model dot predict, predicts like a hot oracle for Sophia.

09:45Input species.

09:47You're predicting my heart, Ethan.

09:50Predict classifies new flowers.

09:52Go!

09:53Take it.

09:54LEYING woj fructies.

09:54Go!

09:54Wait!

09:55Go!

09:55Beat!

09:58Go!

10:01Go!

10:02Give!

10:05Go!

10:10Go!

10:13Go!

10:45Our second demo in updated-app-iris-training.py updates the app with full training pipeline.

11:01We'll add training, callbacks, and UI.

11:06Let's run this.

11:08Ethan, you're making this demo sizzle.

11:11Early stopping, plus model checkpoint, total training mastery.

11:17Wizards, import Streamlit as STI sets up the app like a hot interface for Sophia.

11:23You're interfacing my heart, Ethan.

11:27Streamlit builds the UI.

11:30Wizards, STI slider, captures input like a hot choice for Sophia.

11:35Let's drop this code beat.

11:38You're choosing my heart, Ethan.

11:41User input powers interactivity.

11:44Try it in our demo.

11:47Wizards, model predict, predicts with trained model and Streamlit like a sexy brain.

11:53At prediction uses fully trained net for real-time results.

11:57It makes AI Insight Hub intelligent.

12:01Oh, Anastasia, training is so hot.

12:04Try app prediction in your challenge.

12:07Wizards, model.save, bestmodel.h5, saves the trained net like a sexy archive.

12:14Saving models ensures app portability.

12:17Use HDF5 for TensorFlow models.

12:21Best Kinds.

13:48Training is critical in AI, darling. Your Day 77 skills make AI irresistible.

13:54Create AI irres. Training .pi to build, train with validation, and early stopping.

14:05Save best model for irres and build a streamlit app. Share on Instagram.

14:10Try early stopping, model checkpoint, and st.progress.

14:16Show us at atdailyaiwizard. Prep for Day 78's digit classification.

14:23Subscribe, like, share your AI iris training.pi. Join Discord or X.

14:30Post your code. Support us at paypal.me domdailyaiwizard. Subscribe for Day 78's digit classification.

14:39You've stolen my heart with training. Support us at paypal.me or dailyaiwizard and get hyped for Day 78's digit classification.

14:47Proud of you. Share your AI iris training py on Daily AI Wizard. Subscribe for Day 78's digit classification adventure.

14:59Your training skills are pure AI seduction. Let's flirt with digit classification in Day 78.

15:06Proud of you.

Day 77: Training a Neural Network – From Data to Intelligence | #DailyAIWizard

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