Day 76: Optimizers – The Engine of Neural Learning | #DailyAIWizard - video Dailymotion

DailyAIWizard

Kick off with 1970s jazz, coffee, and optimizers! Join Anastasia, Irene, Ethan, Sophia, and Olivia as we compare SGD, Adam, RMSprop for Iris classification. Sophia leads two demos, Ethan drops flirty code. Support at PayPal.me/DailyAIWizard! Get ready for Day 77: Training a Neural Network! Subscribe, like, share your ai_iris_optimizers.py!  pay a coffee:  https://www.paypal.com/pool/9j2tp7IvP...  #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 Optimizers 01:19 Why Optimizers? 02:40 What are Optimizers? 05:29 Demo 12:56 Libraries 13:30 Challenge

Transcript

00:00Hey, sexy wizards. Anastasia here, your main moderator, ready to accelerate Day 76 of Daily AI Wizards Python for AI series.

00:09After loss functions in Day 75, we're diving into Optimizers, the heartbeat of neural nets, for our AI Insight Hub app.

00:18Ethan, what's the power of Optimizers? Sophia, how do they build on Day 75?

00:24Hello, brilliant wizards. I'm Irene, guiding with warmth.

00:29Optimizers minimize loss faster and smarter. Our demos will make your AI fly.

00:36Love our content? Buy us a coffee.

00:39Yo, wizards, Ethan's here, dropping spicy optimizer code with winks for Sophia and Olivia.

00:45Adam, RMS prop, SGD. Let's crank this AI engine to 11.

00:51Sophia here, Ethan, and your charms got me blushing.

00:54I'm pumped to lead our app component demos and make optimizers sizzle.

01:00Let's accelerate learning, wizards.

01:05Olivia here, darlings.

01:06I'll sprinkle flirty tips, ask Anastasia questions, and chat with Ethan to keep your optimizer learning hot.

01:13Ready to speed up, wizards?

01:14Wizards, optimizers are your neural net coach, darling.

01:22They update weights to minimize loss.

01:25Ethan, explain SGD versus Adam.

01:28Sophia, how do they speed up Day 75?

01:32Optimizers find the fastest path down the loss landscape, crucial for iris classification.

01:38Our demos will show real speed gains.

01:43Today we're seducing you with optimizer magic.

01:45You'll master SGD, Adam, RMS prop, compare convergence, and integrate into the app.

01:52Sophia, what's the app focus?

01:54Ethan, any code highlights?

01:56Sophia leads app demos with passion.

01:59Ethan drops hilarious code explanations.

02:02We're guiding you to master optimizers and prep for Day 77's Training a Neural Network.

02:11Wizards, meet your Day 76 dream team.

02:14Anastasia's our main moderator with Flirty Charm.

02:17I'm guiding with warmth.

02:19Ethan's our code comedian, flirting with Sophia, and Olivia.

02:24Oh, Irene, you're a gem.

02:27Sophia's leading app demos with passion.

02:29Ethan's stealing hearts with code, and Olivia's tossing flirty tips.

02:33We're here to make you optimizer superstars.

02:40Wizards, optimizers are your gradient descent coach, darling.

02:44They use momentum, adaptivity, and more.

02:47Ethan, explain Adam's magic.

02:49Sophia, how do they beat Day 75?

02:52Anastasia, you make optimizers sound so hot.

02:55How do they improve training, love?

02:57Ethan, what's your take?

03:00Oh, Olivia, you tease.

03:01Optimizers make neural nets learn faster.

03:04Ethan, Sophia, jump in.

03:07Anastasia, Olivia, optimizers are like hot engines for Sophia.

03:11Adam adapts, RMS prop remembers.

03:14Let's drop this code beat.

03:15Yo, Wizards, SGD, is the OGG optimizer, like a hot sprinter for Sophia.

03:21Fast but noisy.

03:22Let's drop this code beat.

03:24You're sprinting my heart, Ethan.

03:27SGD is simple but needs tuning.

03:32Wizards, SGD, momentum equals 0.9.

03:35Adds velocity like a hot roller coaster for Sophia.

03:39Smoother, faster.

03:40Let's drop this code beat.

03:42You're rolling my heart, Ethan.

03:45Momentum accelerates in ravines.

03:49Wizards, Adam, adapts like a hot chameleon for Sophia.

03:53Combines momentum and RMS prop.

03:55Let's drop this code beat.

03:57You're adapting my heart, Ethan.

04:00Adam is default for a reason.

04:02Wizards, RMS prop.

04:06Normalizes gradients like a hot equalizer for Sophia.

04:09Great for RNNs.

04:11Let's drop this code beat.

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

04:16RMS prop handles non-stationary objectives.

04:21Wizards, compare optimizers like a hot race for Sophia.

04:24Adam wins, but SGD is lightweight.

04:27Let's drop this code beat.

04:28You're racing my heart, Ethan.

04:30Comparison shows best for Iris.

04:36Wizards, build nets with optimizer equals, Adam, like a hot experiment for Sophia.

04:41Test each.

04:42Let's drop this code beat.

04:44You're experimenting my heart, Ethan.

04:48Different optimizers for different tasks.

04:52Wizards, model.fit, trains with different optimizers like a hot race for Sophia.

04:57See which converges best.

04:59Let's drop this code beat.

05:00You're racing my heart, Ethan.

05:04Training shows optimizer effects.

05:08Wizards, model.

05:09Evaluate, scores optimizers like a hot judge for Sophia.

05:13Accuracy, loss.

05:15Let's drop this code beat.

05:17You're judging my heart, Ethan.

05:20Evaluation shows best optimizer.

05:23Wizards, it's demo time.

05:30We'll integrate optimizers into the neural net classifier for AI Insight Hub, continuing from days 72 to 75.

05:39Get your setup ready.

05:43Ensure Python, VS Code, TensorFlow, and Streamlit are installed.

05:48Wizards, open days, 7275's files.

05:52Ethan will spice it up.

05:53Let's accelerate.

05:54Wizards, prep to continue from days 72 to 75.

05:59Open VS Code, load prior app files, create iris-optimizer-demo.py and updated-app-iris-optimizer.py.

06:11Save in a Python demo.

06:14Run pip install TensorFlow Streamlit.

06:18Sophia, you make continuation dreamy.

06:21How do we build on day 75's loss functions?

06:24Ethan, what's your take?

06:26Start with day 75's model.

06:28Add optimizer comparison.

06:30Run Streamlit run updated-app-iris-optimizer.py.

06:36Anastasia, Olivia, optimizers are the hot sequel to day 75.

06:42Let's drop this code beat.

06:44Our first demo in iris-optimizer-demo.py compares optimizers on iris.

06:50We'll build four neural nets, train, evaluate, and visualize.

06:54Let's run this.

06:56Oh, Ethan, you're making this demo hot.

07:00Optimizer equals Atom VS SGD, total optimizer showdown.

07:07Wizards, import TensorFlow as TF loads the deep learning engine like a hot startup for Sophia.

07:12Let's drop this code beat.

07:14You're starting my engine, Ethan.

07:17TensorFlow powers neural nets.

07:21Wizards, load underscore iris, loads data like a love letter to Sophia, prepping for AI classification.

07:27Let's drop this code beat.

07:29You're loading my heart, Ethan.

07:33Iris data is ready for optimizers.

07:36Wizards, standard scaler, scales data like a hot transformation for Sophia.

07:42Neural nets love normalized input.

07:45Let's drop this code beat.

07:46You're transforming my heart, Ethan.

07:50Scaling ensures stable training.

07:54Wizards, sequential, stacks layers like a hot tower for Sophia.

07:58Input, hidden, output.

08:00Let's drop this code beat.

08:01Wizards, dense, 16, activation equals, relu.

08:13Adds neurons like a hot web for Sophia.

08:15Let's drop this code beat.

08:18You're connecting my heart, Ethan.

08:21Dense layers learn patterns.

08:24Wizards, model.

08:25Compile, optimizer equals, atom, sets optimizer like a hot trainer for Sophia.

08:31Let's drop this code beat.

08:33You're training my heart, Ethan.

08:36Compile defines learning.

08:40Wizards, model.fit, trains like a hot workout for Sophia.

08:44Epics, batch size.

08:46Let's drop this code beat.

08:48You're working out my heart, Ethan.

08:51Fit trains the net.

08:53Wizards, model.

08:56Evaluate, scores like a hot test for Sophia.

08:59Accuracy, loss.

09:00Let's drop this code beat.

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

09:06Evaluate measures performance.

09:09Wizards, model.

09:11Predict, predicts like a hot oracle for Sophia.

09:14Input right-pointing arrow species.

09:16Let's drop this code beat.

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

09:20Predict classifies new flowers.

09:50You're predicting your windcast.

09:52Give me your love, Ethan.

09:53Next time you're wins.

09:55I'll be right back.

09:56With a feeling.

09:57Push suns ik overseas.

10:00I'll be right back.

10:01With a feeling.

10:01You're Swiss.

10:02Falls away.

10:04Thanks.

10:08Well, I'll use a feeling.

10:11красив, man.

10:12I'll use screen name me, though.

10:13Titles type.

10:18Oh, happiness.

10:19I love this day.

10:20Our second demo in updated-app-iris-optimizer.py updates the app with optimizer comparison.

10:34We'll add multiple nets, evaluation, and UI.

10:39Let's run this.

10:42Sophia, you're making this demo sizzle.

10:45Optimizer equals.

10:47Atom, VSSGD, Total Optimizer Showdown.

10:50Wizards, import Streamlit as Saint sets up the app like a hot interface for Sophia.

10:55Let's drop this code beat.

10:57You're interfacing my heart, Ethan.

11:00Streamlit builds the UI.

11:04Wizards, Street Slider, captures input like a hot choice for Sophia.

11:09Let's drop this code beat.

11:10You're choosing my heart, Ethan.

11:13User input powers interactivity.

11:16Wizards, Model, Predict, predicts with optimizers in Streamlit like a sexy engine.

11:24App Prediction uses neural net with optimizers for real-time results.

11:29It makes AI Insight Hub intelligent.

11:32Oh, Anastasia, optimizers are so hot.

11:36Try App Prediction in your challenge.

11:38Wizards, Model.save, Model.h5 saves the optimized net like a sexy archive.

11:45Saving models ensures app portability.

11:48Use HDF5 for TensorFlow models.

11:51Vision models.

11:52Both catalogs...

11:56...

11:57...

11:58...

11:59...

12:01...

12:08...

12:09It makes it so hot.

12:10It's a fun one.

12:11It makes it so hot.

13:13Optimizers fit AI pipelines for efficient training.

13:17Your skills are ready for Day 77.

13:20Optimizers are critical in AI, darling.

13:23Your Day 76 skills make AI irresistible.

13:25Create AI irisoptimizers.py to build, train, compare four neural nets with different optimizers for iris and build a streamlit app.

13:40Share on Instagram.

13:41Try optimizer equals atom, SGD, RMS prop, and ST select box.

13:49Show us at at Daily AI Wizard.

13:52Prep for Day 77's training and neural network.

13:56Subscribe, like, share your AI irisoptimizers.py.

14:00Join Discord or X.

14:03Post your code.

14:04Support us at paypal.me, John, Daily AI Wizard.

14:08Subscribe for Day 77's training and neural network.

14:11You've stolen my heart with optimizers.

14:14Support us at paypal.me, Daily AI Wizard.

14:17And get hyped for Day 77's training and neural network.

14:21Proud of you.

14:22Share your AI irisoptimizers.py on at Daily AI Wizard.

14:29Subscribe for Day 77's training and neural network adventure.

14:34Your optimizer skills are pure AI seduction.

14:37Let's flirt with training and neural network in Day 77.

Day 76: Optimizers – The Engine of Neural Learning | #DailyAIWizard

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