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Deep Learning powers most of the technologies we rely on every day. From Machine Translation that can translate web pages in seconds to recommendations of shows to watch or even face recognition that lets us log in to our phones many times a day.
Even though we use it every day, it might still not be clear to you how deep learning works. Let's dive into the behind-the-scenes of deep learning in this video.
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In this video, we will take a closer look at deep learning. We will learn what deep learning is, where in the world of Artificial Intelligence it stands, why it has been very successful in becoming part of our lives, and how it compares to more traditional machine learning algorithms.
Artificial Intelligence, Machine Learning, and Deep Learning have become the most talked-about technologies in today’s commercial world as companies are using these innovations to build intelligent machines and applications. And although these terms are dominating business dialogues all over the world, many people have difficulty differentiating between them.
In this video, Dr. Sheraz Naseer, a cyber security and deep learning expert, will be explaining:
- What are AI, ML, and Deep Learning?
- What's the difference between AI, ML & DL?
- How can you make a career in this field?
Playlist - Data Science Series: https://www.youtube.com/playli....st?list=PLxf3-FrL8Gz
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Machine Learning is one of those things that is chock full of hype and confusion terminology. In this StatQuest, we cut through all of that to get at the most basic ideas that make a foundation for the whole thing. These ideas are simple and easy to understand. After watching this StatQuest, you'll be ready to learn all kinds of new and exciting things about Machine Learning.
If you're interested in learning more about SoSA, here's the link: http://thesosa.org/
Here's the link to the video about the bias/variance tradeoff:
https://youtu.be/EuBBz3bI-aA
Here's the link to the video about cross-validation, aka the way to determine which samples go into your training set and which samples go into your testing set: https://youtu.be/fSytzGwwBVw
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying The StatQuest Illustrated Guide to Machine Learning!!!
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channe....l/UCtYLUTtgS3k1Fg4y5
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https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
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Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
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0:00 Awesome song and introduction
0:35 A silly example of classification
2:24 A silly example of regression
3:37 The Bias/Variance Tradeoff
8:15 Fancy machine learning
8:56 Evaluating the performances of a decision tree
11:12 Summary of concepts and main ideas
#statquest #ML
Learn the essentials of working with AI in the cloud from @ExamProChannel. This comprehensive course covers the complete generative AI development lifecycle - from fundamental concepts through practical implementation, including prompt engineering, development tools, deployment, optimization, and advanced topics like RAGs and AI agents.
More course info: https://www.exampro.co/exp-genai-001
❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba)
⭐️ Contents ⭐️
- 00:00:00 Introduction
- 00:54:16 AI and ML Fundamentals
- 03:02:21 Gen AI Primer
- 03:32:55 Data and ML
- 03:47:56 LLM Basics
- 04:12:22 AI Powered Assistants
- 04:24:42 Env Setup
- 06:12:17 Prompt Engineering
- 07:00:25 WorkBenches and Playgrounds
- 07:44:09 Model as a Service
- 08:36:26 LLM DevTools and Workflow
- 11:52:07 AI Code Assistants
- 14:04:37 App Prototyping
- 17:21:06 Containers
- 18:12:43 Serving
- 18:19:51 AI Delivery Platform
- 19:40:45 GenAI Hardware
- 19:50:21 Framework
- 19:51:49 LLM Customization
- 19:52:35 SFT
- 19:56:25 Size Optimization
- 20:26:04 RAGS
- 22:21:19 Agents
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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐳𝐮𝐫𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) :https://www.edureka.co/microso....ft-certified-azure-s
The "Azure Architect Full Course" provides a thorough overview of essential Azure topics, including computing, networking, storage, security, and more. This Full Course is designed for aspiring Azure architects and provides practical insights into building scalable and secure solutions on the Microsoft Azure cloud platform.
00:00:00 Introduction
00:02:53 What is Azure?
00:14:07 Quiz
00:14:29 How To Create Azure Free Account?
00:20:07 Introduction to Azure Portal
00:35:07 Microsoft Azure Demo
01:09:04 Azure Virtual Machine
01:30:50 Quiz
01:31:14 Microsoft Azure Storage Overview
02:14:21 Azure Active Directory
02:41:18 Azure Load Balancer
03:14:44 Quiz
03:15:06 Azure Firewall
03:44:45 Quiz
03:45:06 Azure App Service
04:02:31 Quiz
04:03:03 Azure Database Services
04:33:49 Azure Data Factory
05:05:14 Quiz
05:05:37 Azure Databricks
05:39:02 Quiz
05:39:24 Azure Data Lake
05:54:05 Quiz
05:54:28 Azure Machine Learning
06:20:54 Introduction To Azure Kubernetes Service (AKS)
07:10:40 ARM Templates
07:38:57 Introduction to Azure IoT
07:57:18 What is Azure Service Bus?
08:54:26 AI on Microsoft Azure
09:07:00 Azure Certifications
09:17:17 How to become an Azure Data Engineer?
09:41:50 Azure Interview Questions And Answers
📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU
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For a code implementation, check out these repos:
https://github.com/luisguiserr....ano/manning/tree/mas
https://github.com/luisguiserr....ano/manning/tree/mas
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
An introduction to logistic regression and the perceptron algorithm that requires very little math (no calculus or linear algebra), only a visual mind.
0:00 Introduction
0:08 Series of 3 videos
0:41 E-mail spam classifier
7:19 Classification goal: split data
11:36 How to move a line
12:21 Rotating and translating
18:47 Perceptron Trick
23:20 Correctly and incorrectly classified points
24:20 Positive and negative regions
27:18 Perceptron Error
29:40 Gradient Descent
34:36 A friendly introduction to deep learning and neural networks
37:48 Activation function (sigmoid)
38:31 Log-Loss Error
41:37 Perceptron Algorithm
42:45 Logistic regression algorithm
44:48 Thank you!
CORRECTION: At 10:56 we shouldn't divide by 4 to get the covariance, we should divide by 1+1+1+1/3, which is 10/3. That means the covariances are the following:
Var(x) = 1.056
Var(y) = 0.864
Cov(x,y) = 0.768
(Thank you Shivkumar Pippal!)
Mean, variance, covariance, and the covariance matrix for a dataset and a weighted dataset.
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
0:00 Introduction
0:09 The covariance matrix
2:22 Average
3:23 X-variance
5:06 Problem: Same variances
7:59 Formulas
10:30 Center points
For a code implementation, check out this repo:
https://github.com/luisguiserr....ano/manning/tree/mas
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
An introduction to support vector machines (SVMs) that requires very little math (no calculus or linear algebra), only a visual mind.
This is the third of a series of three videos.
- Linear Regression: https://www.youtube.com/watch?v=wYPUhge9w5c
- Logistic Regression: https://www.youtube.com/watch?v=jbluHIgBmBo
0:00 Introduction
1:42 Classification goal: split data
3:14 Perceptron algorithm
6:00 Split data - separate lines
7:05 How to separate lines?
12:01 Expanding rate
18:19 Perceptron Error
19:26 SVM Classification Error
20:34 Margin Error
25:13 Challenge - Gradient Descent
27:25 Which line is better?
28:24 The C parameter
30:16 Series of 3 videos
30:30 Thank you!
Battle-tested approaches for building agents with state-of-the-art AI capabilities
Agents are everywhere these days, but with so much information available, it’s easy to feel overwhelmed. Most tutorials and videos focus on specific frameworks without covering the fundamental principles behind these systems.
This course takes a different approach. Over four modules, you'll learn to implement the four core agentic patterns from scratch, using just Python and Groq LLMs:
* Reflection Pattern
* Tool Use Pattern
* Planning Pattern
* Multi-Agent Pattern
Explore the written lessons on my Substack blog:
https://theneuralmaze.substack.com/
Check out the code here:
https://github.com/neural-maze/agentic_patterns
––––––––––––––––––––––––––––––
If you like this content, you can also follow me here:
📩 Substack - https://theneuralmaze.substack.com/
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💻 GitHub - https://github.com/MichaelisTrofficus
🐦 Twitter - https://x.com/moteropedrido
––––––––––––––––––––––––––––––
––––––––––––––––––––––––––––––
0:00 Introduction
2:48 Module 1 - Reflection Pattern
20:40 Module 2 - Tool Pattern
44:22 Module 3 - Planning Pattern
1:13:16 Module 4 - MultiAgent Pattern
1:41:00 Conclusion
––––––––––––––––––––––––––––––
/ mehulchoudhary
Creative Commons — Attribution 3.0 Unported — CC BY 3.0
Free Download / Stream: https://bit.ly/3wVnIXs
Music promoted by Audio Library https://bit.ly/3TDW3TT
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🌟 Join me to Master Python for AI Projects 👉 https://python-course-earlybird.framer.website/?&utm_source=aiagentpy
📝 Get my AI agents note 👉 https://thu-vu.kit.com/847779a4f4
🔗 Github repo for this project 👉 https://github.com/thu-vu92/social-media-agent
-----------
👉 Junie, the coding agent by JetBrains: https://jb.gg/try_junie . Try it in your IDE with a single JetBrains AI subscription – AI Ultimate is recommended for regular usage.
👉 Check out PyCharm, the only Python IDE you need: https://jb.gg/check_pc
-----------
🔑 TIMESTAMPS
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
0:00 - Intro
0:58 - Are AI agents just hype?
1:57 - Popular agentic applications
2:54 - AI agent project ideas
4:48 - Ways to build an AI agent
7:26 - Coding agents Junie (sponsor)
8:31 - Crash course on AI agents
15:31 - Building your first AI agent - Project walkthrough
51:50 - Conclusions
#ai #python #agent
Ready to become a certified Architect on Cloud Pak for Data? Register now and use code IBMTechYT20 for 20% off of your exam → https://www.ibm.com/training/c....ertification/ibm-cer
Learn more about Agentic AI here → [Topic Page on .com]
Agentic AI is transforming technology—but autonomy brings big risks 🤖. Phaedra Boinodiris & Matt Bellio explore risks like misinformation, security gaps, and decision-making errors, and share strategies for AI governance and responsible deployment 🚀. Learn how to navigate the future of autonomous AI!
AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://www.ibm.com/account/re....g/us-en/signup?formi
#agenticai #autonomousai #airisks #technologyinnovation
Want to automate anything with Claude Code, even if you're a total beginner? In this step-by-step tutorial, I’ll show you how to use Claude Code, Blotato MCP, and AI automation workflows to build powerful systems without being a developer. Whether you're into no-code automation, AI agents, or building passive income systems, this guide will help you turn ideas into automated machines fast.
--
I'm Sabrina Ramonov. I'm on a mission to teach 1 million people AI. I'm also the solo founder of Blotato.com. It's a SaaS app for creators/entrepreneurs to go viral faster with AI, just like me (0 to 500k+ in 6 months solo).
How I got here…
0: Born in Quezon City, Philippines 🇵🇭
22 yrs old: Graduated UC Berkeley with degrees in Computer Science and Physics... and $250k in student loans.
23 yrs old: Started Qurious, as Founder/CEO, doing natural language processing (AI) in healthcare.
25 yrs old: Closed pilots with billion-dollar pharmaceutical companies, but failed to reach product market fit.
26 yrs old: Pivoted Qurious a lot, then settled on real-time call guidance for sales and customer support, combining streaming speech recognition (AI) and natural language processing (AI).
28 yrs old: Made the Forbes 30 Under 30 list, while living in a living room of 2BR San Francisco apartment.
30 yrs old: Sold 100% of Qurious to Pegasystems (NASDAQ: Pega) in cash and stock deal ($10M+ total).
31 yrs old: Integrated Qurious with Pegasystems Customer Service to launch Pega Voice AI, now used by 1000s of support agents.
32 yrs old: Started angel investing in VC/seed funds, crypto, and passion areas 🍄 while recovering from burnout by hiking, skiing, and reading. Dropped 50 lbs to get back in shape.
33 yrs old: Started making free content to teach 1 million people AI.
34 yrs old: 0 to 500k+ followers in 6 months with $0 budget, 0 team, $0 paid ads, 0 masterminds.
Today: Started https://Blotato.com to distribute 100+ pieces of content per week. 1000s of creators & entrepreneurs use Blotato to go viral faster with AI.
Get FREE AI prompts, playbooks, and agents 👉 https://sabrina.dev/p/free
🚀 Complete Claude Code Resources & Course: https://skool.com/scrapes
Don't miss the next build - https://www.youtube.com/@simon....scrapes?sub_confirma
Grab the full resources for this video here (free): https://scrapeshq.notion.site/....Every-Level-of-Claud
Think you're getting the most out of Claude Code? You're probably just scratching the surface. In this video, we break down the 7 levels of mastery, guiding you from basic prompting all the way to building fully autonomous systems. Stop the trial-and-error and start unlocking Claude Code's true potential today.
00:00 - Every Level of Claude Code Explained in 39 Minutes
00:41 - Level 1: The One Thing You Need To Know
03:25 - Level 2: Personalizing CLAUDE for better responses
10:25 - Level 3: Slash Commands, Skills & Hooks - Repeatability
21:21 - Level 4: Connecting Claude Code to Your Apps (MCPs)
26:18 - Level 5: Move from Executor to Supervisor
29:34 - Level 6: Agent Teams
35:57 - Level 7: Fully Autonomous Systems
#claudecodetutorial #masterclaudecode #claudecode
This is my 50 Claude Code tips from 6 months of daily use personally and at Meta as a Staff Software Engineer. I've been coding with Claude Code basically 12 hours a day really trying to understand what makes Claude Code tik. Here's everything I wish I knew when I started, from foundations to advanced parallel workflows.
⏱️ TIMESTAMPS
0:00 - Intro
1:04 - ACT 1: Foundations (Tips 1-25)
1:18 - Tip 1: Run from root directory
1:56 - Tip 2: Run /init immediately
2:54 - Tip 3: CLAUDE.md is hierarchical
3:27 - Tip 4: Keep CLAUDE.md concise
3:58 - Tip 5: Structure: What, Domain, Validation
5:36 - Keyboard Shortcuts
5:58 - Tip 6: Shift+Tab toggles modes
6:40 - Tip 7: Escape interrupts
7:43 - Tip 8: Double Escape clears input
7:59 - Tip 9: Double Escape on empty = rewind
8:29 - Tip 10: Screenshot and drag
8:44 - Tip 11: Add context to screenshots
9:09 - Essential Commands
9:41 - Tip 12: /clear resets context
10:13 - Tip 13: /context shows token usage
11:42 - Tip 14: Let auto-compaction work
12:23 - Tip 15: /model switches models
12:49 - Tip 16: /resume recovers sessions
13:21 - Tip 17: /mcp shows MCP status
14:19 - Tip 18: /help shows all commands
14:33 - Tip 19: Git is your safety net
15:24 - CLAUDE.md Deep Dive
15:52 - Tip 20: Add a Critical Rules section
17:08 - Tip 21: Ask Claude to update rules
17:46 - Tip 22: Use workflow triggers
18:27 - Tip 23: Commit CLAUDE.md to git
19:34 - Tip 24: dangerously-skip for throwaway envs
20:39 - Tip 25: Combine skip with allowlists
20:59 - ACT 2: Daily Workflow (Tips 26-32)
21:38 - Tip 26: Start features in Plan Mode
23:46 - Tip 27: Fresh context beats bloated
24:29 - Tip 28: Persist before ending sessions
25:03 - Tip 29: Lazy load context
26:09 - Tip 30: Give verification commands
27:32 - Tip 31: Consider Opus for complex work
28:18 - Tip 32: Read thinking blocks
29:01 - ACT 3: Power User (Tips 33-40)
29:34 - Tip 33: Four composability primitives
29:54 - Tip 34: Skills = recurring workflows
31:33 - Tip 35: Commands = quick shorthand
32:18 - Tip 36: Never create commands manually
33:02 - Tip 37: MCPs = external service docs
33:52 - Tip 38: Ask Claude to install MCPs
34:15 - Tip 39: Subagents = isolated context
37:10 - Tip 40: Avoid instruction overload
37:48 - ACT 4: Advanced (Tips 41-50)
38:02 - Tip 41: Run multiple instances
39:06 - Tip 42: iTerm split panes
40:33 - Tip 43: Enable notifications
41:10 - Tip 44: Git worktrees for isolation
41:40 - Tip 45: /chrome connects browser
43:17 - Tip 46: Powerful for debugging
43:28 - Hooks & Automation
43:41 - Tip 47: Hooks intercept actions
44:10 - Tip 48: Auto-format with PostToolUse
44:24 - Tip 49: Block dangerous commands
44:43 - Tip 50: Explore the plugin ecosystem
45:32 - Context is King (Outro)
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In this video, I walk through my complete workflow for tackling large coding projects using Claude Code's plan mode. I demonstrate how to start with a rough dictated prompt, use plan mode to explore the codebase and generate clarifying questions, and break complex work into multi-phase plans that can span multiple context windows. I show my custom rules configuration that keeps plans concise and adds unresolved questions, how to monitor context usage throughout implementation, and my strategy of storing plans as GitHub issues to preserve them across context resets. This approach combines upfront planning with aggressive auto-accept during implementation phases, allowing AI to handle substantial features while maintaining control and code quality. I share practical tips including my favorite concision rule, the benefits of multi-phase planning, and how to effectively manage context windows for large projects.
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