TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models.

#programming #deeplearning #100secondsofcode

🔗 Resources

TensorFlow Docs https://www.tensorflow.org/
Fashion MNIST Tutorial https://www.tensorflow.org/tutorials/keras/classification
Neural Networks Overview for Data Scientists https://www.ibm.com/cloud/learn/neural-networks
Machine Learning in 100 Seconds https://youtu.be/PeMlggyqz0Y

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MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2024 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com

Lecture Outline
0:00​ - Introduction
3:42​ - Sequence modeling
5:30​ - Neurons with recurrence
12:20 - Recurrent neural networks
14:08 - RNN intuition
17:14​ - Unfolding RNNs
19:54 - RNNs from scratch
22:41 - Design criteria for sequential modeling
24:24 - Word prediction example
31:50​ - Backpropagation through time
33:40 - Gradient issues
37:15​ - Long short term memory (LSTM)
40:00​ - RNN applications
44:00- Attention fundamentals
46:46 - Intuition of attention
49:13 - Attention and search relationship
51:22 - Learning attention with neural networks
57:45 - Scaling attention and applications
1:00:08 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

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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⭐️ Contents ⭐️
- 00:00:00 Introduction
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- 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
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- 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
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🔥 Microsoft Power BI Certification Training: PwC Academy: https://www.edureka.co/power-b....i-certification-trai

This video on *Power BI Desktop* covers everything you need to know, from its evolution to practical applications. We'll dive into the history of Power BI, explaining what Power BI and Power BI Desktop are and why Power BI Desktop is a key tool for business intelligence. You'll also learn how to install Power BI Desktop, explore its features, and understand how to use it to create insightful reports. Finally, we'll guide you through the process of publishing a Power BI report. Whether you're a beginner or looking to expand your knowledge, this tutorial provides valuable insights into Power BI Desktop.

✅ 00:00 - Introduction to Power BI Desktop
✅ 01:08 - Evolution of Power BI
✅ 02:47 - What is Power BI?
✅ 03:22 - What is Power BI Desktop?
✅ 03:55 - Why is Power BI Desktop used?
✅ 04:28 - Power BI Desktop installation
✅ 05:04 - How is Power BI Desktop used?
✅ 17:09 - How to Publish Power BI Report?

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📝Feel free to share your comments below.📝

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🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

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About Power BI Certification Course

Edureka's Power BI certification course helps you master Business Analytics, covering Power BI Desktop, DAX, Service, Data Transformation, Reports, and more. With live instructor-led sessions and real-time projects, you’ll gain hands-on experience from industry experts with over 10 years of experience and be prepared to clear the PL-300 exam.
- - - - - - - - - - - - - -

What are the prerequisites for the Edureka Power BI training course?

There is no prior technical knowledge required for this Power BI training course. However, a fundamental understanding of Microsoft Excel, R and Python Scripts will be an advantage. To help you brush up concepts of R and Python Scripts, we will provide self-paced videos absolutely free in your LMS.
- - - - - - - - - - - - - -

Why should you learn Power BI course?

Power BI is a business analytics service provided by Microsoft. It provides interactive visualizations with self-service business intelligence capabilities, where end users can create reports and dashboards by themselves without having to depend on any information technology staff or database administrator. Microsoft Power BI Plus: Certified by PwC program shares knowledge on how it provides cloud-based BI services - known as Power BI Services, along with a desktop-based interface called Power BI Desktop. It offers Data modeling capabilities including data preparation, and data discovery, which can be utilized for implementing efficient interactive reports and dashboards.
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For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US & Others: +18885487823 (toll-free)

#powerbi #powerbidesktop #powerbitutorial

🔥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

🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

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This video is about the Discrete Fourier Transform.

Grokking Machine Learning Book:
https://www.manning.com/books/....grokking-machine-lea
40% discount promo code: serranoyt

00:00 Introduction
00:58 The Discrete Fourier Transform
06:08 The Inverse Discrete Fourier Transform
09:11 The Formula
14:23 Matrix Form
15:45 Next Steps

State Space Models (SSMs) are a new architecture that is revolutionizing Large Language Models. Learn about them in this friendly video!

00:00 Introduction
00:33 Example of state space models
10:34 SSMs for language generation
17:40 Mamba
18:32 Convolutions

Grokking Machine Learning, by Luis Serrano
www.manning.com/books/grokking-machine-learning
40% discount code: serranoyt

Correction: At 30:42 I write "X = Y". They're not equal, what I meant to say is "X and Y are identically distributed".

The variance is a measure of how spread out a distribution is. In order to estimate the variance, one takes a sample of n points from the distribution, and calculate the average square deviation from the mean.
However, this doesn't give a good estimate of the variance of the distribution. The best estimate, however, is obtained when dividing by n-1 instead of n.
WHY!?!?!?!?!?!?!?
In this video, we dig deeper into why the variance calculation should be divided by n-1 instead of by n. For this, we use an alternate definition of the variance, which doesn't use the mean in its calculation.

*[0:00] Introduction and Bessel's Correction*
- Introducing Bessel's Correction and why we divide by \( n-1 \) instead of \( n \) to estimate variance.

*[0:12] Introduction to Variance Calculation*
- Explaining the premise of calculating variance and introducing the concept of estimating variance using a sample instead of the entire population.

*[1:01] Definition of Variance*
- Defining variance as a measure of how much values deviate from the mean and outlining the basic steps of variance calculation.

*[1:52] Introduction to Bessel's Correction*
- Discussing why we divide by \( n-1 \) when calculating variance and introducing Bessel's Correction.

*[2:35] Challenges of Bessel's Correction*
- Sharing personal challenges in understanding the rationale behind Bessel's Correction and discussing my research process on the topic.

*[3:20] Alternative Definition of Variance*
- Presenting an alternative definition of variance to aid in understanding Bessel's Correction and expressing curiosity about its presence in the literature.

*[4:45] Quick Recap of Mean and Variance*
- Briefly revisiting the concepts of mean and variance, demonstrating how they are calculated with examples, and explaining how variance reflects different distributions.

*[7:05] Sample Mean and Variance Estimation*
- Explaining the challenges of estimating the mean and variance of a distribution using a sample and discussing why sample variance is not a good estimate.

*[8:49] Bessel's Correction and Why \( n-1 \) is Used*
- Explaining how Bessel's Correction provides a better estimate of variance and why we divide by \( n-1 \) instead of \( n \). Emphasizing the importance of making a correct variance estimate.

*[10:51] Why Better Estimation Matters?*
- Discussing why the original estimate is poor and why making a better estimate is crucial. Explaining the significance of sample mean as a good estimate.

*[13:02] Issues with Variance Estimation*
- Illustrating the problems with variance estimation and demonstrating with examples why using the correct mean is essential for accurate estimates. Explaining the accuracy of estimates made using \( n-1 \).

*[15:04] Introduction to Correcting the Estimate*
- Discussing the underestimated variance and the need for correction in estimation.

*[15:57] Adjusting the Variance Formula*
- Explaining the adjustment in the variance formula by changing the denominator from \( n \) to \( n - 1 \).

*[16:22] Calculation Illustration*
- Demonstrating the calculation process of variance with the adjusted formula using examples.

*[16:57] Better Estimate with Bessel's Correction*
- Discussing how the corrected estimate provides a more accurate variance estimation.

*[18:24] New Method for Variance Calculation*
- Introducing a new method for calculating variance without explicitly calculating the mean.

*[20:06] Understanding the Relation between Variance and Variance*
- Explaining the relationship between variance and variance, and how they are related mathematically.

*[21:52] Demonstrating a Bad Calculation*
- Illustrating a flawed method for calculating variance and explaining the need for correction.

*[23:37] The Role of Bessel's Correction*
- Explaining why removing unnecessary zeros in variance calculation leads to better estimates, equivalent to Bessel's Correction.

*[25:08] Summary of Estimation Methods*
- Summarizing the difference between the flawed and corrected estimation methods for variance.

*[26:02] Importance of Bessel's Correction*
- Emphasizing the significance of Bessel's Correction for accurate variance estimation, especially with smaller sample sizes.

*[30:19] Mathematical Proof of Variance Relationship*
- Providing two proofs of the relationship between variance and variance, highlighting their equivalence.

*[35:24] Acknowledgments and Conclusion*

Thanks @mkan543 for the summary!

In this video, Dr. Raj Dandekar (MIT PhD) teaches you how to build a production level SLM entirely from scratch.

You will learn the following:

(1) Creating the dataset
(2) Tokenizing the dataset
(3) Creating input-target pairs
(4) Creating the entire SLM architecture
(5) Setup the SLM for pre-training
(6) Pre-training the SLM
(7) Inference

Google Colab Notebook: https://colab.research.google.....com/drive/1k4G3G5MxY

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/
💼 LinkedIn - https://www.linkedin.com/in/migueloteropedrido/
💻 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
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Learn how to build smart AI workflows with N8N: automate tasks, connect apps, and deploy AI agents.

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00:02:57 — What's n8n?
00:04:44 — How to use it
00:12:42 — Core concepts
00:28:38 — Integrations
00:37:17 — AI Agents
00:39:06 — Inbox Manager overview
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01:22:38 — Generate draft replies

I sit down with my dear friend Vin (Internet Vin) for a deep, hands-on walkthrough of how he uses Obsidian and Claude Code together as a thinking partner, idea generator, and personal operating system. Vin demonstrates live how Claude Code can read, reference, and surface patterns across an entire Obsidian vault of interlinked markdown files — turning years of personal notes into actionable insights, project ideas, and even custom commands. This episode covers everything from the basic setup to advanced workflows like tracing how ideas evolve over time, generating contextual startup ideas, and delegating tasks to autonomous agents. If you are serious about getting the most out of LLMs, this is the episode that shows you how your own writing becomes the fuel.

Timestamps
00:00 – Intro
02:10 – What Is Claude Code?
06:45 – What Is Obsidian?
10:28 – Obsidian CLI: Giving Claude Code Access to Your Vault
14:53 – Thinking Tools: Ghost, Challenge, Emerge, Drift, Ideas, Trace
22:51 – The Role of Reflection in Building a Powerful Vault
25:15 – How This Relates to OpenClaw (Autonomous Agents)
29:13 – Live Demo: /Connect — Bridging Two Domains
31:25 – Meeting Notes & External Info
33:23 – Why Vin Keeps a Strict Separation: Human-Written vs. Agent-Written
35:42 – How Claude Code uses Obsidian
41:46 – Live Demo: /Ideas — Generating Actionable Ideas from Your Vault
47:10 – The /Graduate Command
50:29 – Why Obsidian Is the Missing Link for AI Companies
54:53 – The Alpha: Why 99.99% of People Won't Do This
57:38 – Closing Thoughts & Where to Follow Vin

Key Points

* Claude Code is a command-line agent that can control your computer through natural language — and its power multiplies when you feed it rich, persistent context files instead of re-explaining projects every session.
* Obsidian is uniquely valuable because it sits on top of interlinked markdown files; the new Obsidian CLI lets Claude Code see both the files and the relationships between them.
* Vin built custom slash commands (/trace, /connect, /ideas, /ghost, /drift, /challenge) that let him use Claude Code as a thinking partner — surfacing latent patterns, contradictions, and ideas he would never see on his own.
* Writing and daily reflection are the engine of the entire system: the more you write, the more context the agent has, and the more it can do for you.
* Markdown files are the real oxygen of LLMs; if you are serious about building a personal OS with AI, a centralized note-taking tool built on markdown is foundational

Numbered Section Summaries

1. Obsidian as an Interlinked Knowledge Base

Vin introduces Obsidian as an interface that sits on top of a folder of markdown files, with the critical addition of backlinks — connections between files that mirror how the brain forms associations. He walks through his own vault, showing how daily notes, project files, and notes on people all link together in a visual graph.

2. Obsidian CLI: The Bridge Between Your Vault and Claude Code

The real breakthrough comes from Obsidian CLI, which gives Claude Code access to both the files and their interrelationships. This means the agent can see that a note about filmmaking is connected to a note about world building, and can surface cross-domain patterns you have been circling for months without realizing it.

3. Custom Slash Commands as Thinking Tools

Vin demonstrates a suite of custom commands he built: /context loads his full life and work state; /today pulls calendar, tasks, and daily notes into a prioritized plan; /trace tracks how an idea has evolved over time; /connect bridges two domains using the vault's link graph; /ghost answers a question the way Vin would; /challenge pressure-tests his current beliefs. These turn Claude Code from a generic assistant into a deeply personalized thinking partner.

4. Markdown Files as the Foundation of the AI Era

I make the case that if you are serious about using LLMs to their full potential, a centralized markdown-based note-taking system is table stakes. Writing and reflection are the raw material; files are perfect memory where human recall is flawed; and the 99.99% of people who skip this step are leaving massive value on the table.

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FIND VIN ON SOCIAL
X: https://x.com/internetvin
Youtube: https://www.youtube.com/@otherstuffpod
Personal Website: https://internetvin.com/Index

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Summary ⤵️
The end-to-end, definitive course on Claude Code for beginners! I'll take you through a full four-hour masterclass where I start by teaching you how to set up and install Claude Code, how to configure your IDE or integrated development environment (we'll use Antigravity), how to utilize your CLAUDE.md file as your project brain, how to build your first project in Antigravity using Claude Code in under 15 minutes, advanced Claude Code functionality including hooks, slash commands, and more.

I also teach you how to spin up multiple Claude Code instances and have them work on your behalf; how to parallelize work using sub-agents; how to use Git work trees to accomplish many hours of work in just a few minutes; how to conserve tokens and use context management to crush your coding and software projects; how to deploy things to the cloud using Modal and related services, and in general... how to be awesome at Claude Code!

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🤙 Blog: https://nicksaraev.com

Why watch?
If this is your first view—hi, I’m Nick! TLDR: I spent six years building automated businesses with Make.com (most notably 1SecondCopy, a content company that hit 7 figures). Today a lot of people talk about automation, but I’ve noticed that very few have practical, real world success making money with it. So this channel is me chiming in and showing you what *real* systems that make *real* revenue look like.

Hopefully I can help you improve your business, and in doing so, the rest of your life 🙏

Like, subscribe, and leave me a comment if you have a specific request! Thanks.

Chapters
00:00:00 Introduction to Claude Code
00:01:07 Learning the Basics of Claude Code
00:03:44 Setting Up Claude Code
00:08:05 Terminal vs. Graphical User Interface
00:12:23 Understanding IDEs
00:13:53 Exploring Visual Studio Code
00:19:11 Getting Started with Antigravity
00:24:01 Building Your First Web App with Claude Code
00:30:29 Utilizing the CLAUDE.md File (Project Brain)
00:34:40 Approaches to Website Design in CC +Antigravity
00:41:47 Importance of Verification in AI
00:54:42 Advanced Claude Code Functionality
00:55:13 Understanding the .claude Directory
02:01:24 Adjusting the Proposal Design
02:01:55 Testing Payment Functionality
02:05:20 Leveraging GitHub for Project Management
02:06:12 Setting Up the Project
02:07:50 The Power of Automation
02:13:53 Context Management Explained
02:19:56 Understanding MCP Tools
02:27:23 Strategies for Token Management
02:35:29 Creating Skills for Efficiency
02:41:15 The Structure of Skills
02:44:39 Building a New Skill
02:50:58 Introduction to Model Context Protocol
02:58:15 Evaluating Token Usage in MCPs
03:07:57 Gmail Label Insights
03:09:07 Exploring Claude Code Plugins
03:11:08 Introduction to Sub-Agents
03:12:15 Transforming Skills into Sub-Agents
03:14:14 Scaling Email Classification
03:18:54 Creating Useful Sub-Agents
03:26:27 Understanding Agent Teams
03:32:59 Enabling and Using Agent Teams
04:02:34 Utilizing Git Worktrees
04:03:08 Deploying APIs with Modal

MIT 15.401 Finance Theory I, Fall 2008
View the complete course: http://ocw.mit.edu/15-401F08
Instructor: Andrew Lo

License: Creative Commons BY-NC-SA
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