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Learn how to build an AI-enabled organization: https://online.stanford.edu/co....urses/xdgt224-buildi

Incorporating AI has become a dominant aspect of digital transformation strategies. And there’s no hotter AI technology right now than ChatGPT. If you’re a business manager or a technical team leader, it’s time to start thinking about generative AI and its potential for your company, beyond having it write your emails.

In this webinar, join Arvind Karunakaran, Assistant Professor at Stanford University and a faculty affiliate of the Institute for Human-Centered Artificial Intelligence (HAI), to learn about the potential of Generative AI and how it might impact your work and your organization.

You Will Learn:
- What capabilities and challenges are emerging for Generative AI in a workplace
- How to cut through the hype and see the potential of this new technology for the future of work
- How you can anticipate the effects of Generative AI on your role and begin to reimagine or even reskill the work of your team or company

#chatgpt

Redux: Use const for Action Types
#100DaysOfCode

🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐂𝐨𝐮𝐫𝐬𝐞: https://www.edureka.co/masters....-program/automation- (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
In this Edureka's Full course explore the comprehensive world of Test Automation Engineering in this Full course. From fundamental concepts to advanced strategies, learn how to design, implement, and maintain automated test suites. Additionally, explore various software testing tools to enhance your skill set.

📢📢 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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Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

A complete guide to the mathematics behind neural networks and backpropagation.

In this lecture, I aim to explain the mathematical phenomena, a combination of linear algebra and optimization, that underlie the most important algorithm in data science today: the feed forward neural network.

Through a plethora of examples, geometrical intuitions, and not-too-tedious proofs, I will guide you from understanding how backpropagation works in single neurons to entire networks, and why we need backpropagation anyways.

It's a long lecture, so I encourage you to segment out your learning time - get a notebook and take some notes, and see if you can prove the theorems yourself.

As for me: I'm Adam Dhalla, a high school student from Vancouver, BC. I'm interested in how we can use algorithms from computer science to gain intuition about natural systems and environments.

My website: adamdhalla.com
I write here a lot: adamdhalla.medium.com
Contact me: adamdhalla@protonmail.com

Two good sources I recommend to supplement this lecture:

Terence Parr and Jeremy Howard's The Matrix Calculus You Need for Deep Learning: https://arxiv.org/abs/1802.01528

Michael Nielsen's Online Book Neural Networks and Deep Learning, specifically the chapter on backpropagation http://neuralnetworksanddeeple....arning.com/chap2.htm

ERRATA----
I'm pretty sure the Jacobians part plays twice - skip it when you feel like stuff is repeating, and stop when you get to the part about the "Scalar Chain Rule" (00:24:00).

And, here are the timestamps for each chapter mentioned in the syllabus present at the beginning of the course.

PART I - Introduction
--------------------------------------------------------------
00:00:52 1.1 Prerequisites
00:02:47 1.2 Agenda
00:04:59 1.3 Notation
00:07:00 1.4 Big Picture
00:10:34 1.5 Matrix Calculus Review
00:10:34 1.5.1 Gradients
00:14:10 1.5.2 Jacobians
00:24:00 1.5.3 New Way of Seeing the Scalar Chain Rule
00:27:12 1.5.4 Jacobian Chain Rule

PART II - Forward Propagation
--------------------------------------------------------------
00:37:21 2.1 The Neuron Function
00:44:36 2.2 Weight and Bias Indexing
00:50:57 2.3 A Layer of Neurons

PART III - Derivatives of Neural Networks and Gradient Descent
--------------------------------------------------------------
01:10:36 3.1 Motivation & Cost Function
01:15:17 3.2 Differentiating a Neuron's Operations
01:15:20 3.2.1 Derivative of a Binary Elementwise Function
01:31:50 3.2.2 Derivative of a Hadamard Product
01:37:20 3.2.3 Derivative of a Scalar Expansion
01:47:47 3.2.4 Derivative of a Sum
01:54:44 3.3 Derivative of a Neuron's Activation
02:10:37 3.4 Derivative of the Cost for a Simple Network (w.r.t weights)
02:33:14 3.5 Understanding the Derivative of the Cost (w.r.t weights)
02:45:38 3.6 Differentiating w.r.t the Bias
02:56:54 3.7 Gradient Descent Intuition
03:08:55 3.8 Gradient Descent Algorithm and SGD
03:25:02 3.9 Finding Derivatives of an Entire Layer (and why it doesn't work well)

PART IV - Backpropagation
--------------------------------------------------------------
03:32:47 4.1 The Error of a Node
03:39:09 4.2 The Four Equations of Backpropagation
03:39:12 4.2.1 Equation 1: The Error of the last Layer
03:46:41 4.2.2 Equation 2: The Error of any layer
04:03:23 4.2.3 Equation 3: The Derivative of the Cost w.r.t any bias
04:10:55 4.2.4 Equation 4: The Derivative of the Cost w.r.t any weight
04:18:25 4.2.5 Vectorizing Equation 4
04:35:24 4.3 Tying Part III and Part IV together
04:44:18 4.4 The Backpropagation Algorithm
04:58:03 4.5 Looking Forward

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Summary:
AI Agents seem overwhelming, but in 2026, we've gotten to the point that any non-technical person can create and manage their own AI agents to accomplish tasks. I cover everything simply: what an agent actually is, what to automate, how to start, and build two agents step by step using two of the leading platforms. Then dive into common pitfalls and how to avoid them. This is everything you need to get started with building AI agents in 2026, no coding required.

Chapters
0:00 Intro
0:50 What is an agent?
1:37 Where we're at right now
2:08 What to automate first
4:58 How to start
7:01 Time to build
7:24 Build 1
14:04 Build 2
20:43 More complex agents
22:03 Zapier vs n8n
22:40 Common Pitfalls (and how to avoid them)
24:44 The real skill

So what was the breakthrough that allowed deep nets to combat the vanishing gradient problem? The answer has two parts, the first of which involves the RBM, an algorithm that can automatically detect the inherent patterns in data by reconstructing the input.

Deep Learning TV on
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Twitter: https://twitter.com/deeplearningtv

Geoff Hinton of the University of Toronto, a pioneer and giant in the field, was able to devise a method for training deep nets. His work led to the creation of the Restricted Boltzmann Machine, or RBM.

Structurally, an RBM is a shallow neural net with just two layers – the visible layer and the hidden layer. In this net, each node connects to every node in the adjacent layer. The “restriction” refers to the fact that no two nodes from the same layer share a connection.

The goal of an RBM is to recreate the inputs as accurately as possible. During a forward pass, the inputs are modified by weights and biases and are used to activate the hidden layer. In the next pass, the activations from the hidden layer are modified by weights and biases and sent back to the input layer for activation. At the input layer, the modified activations are viewed as an input reconstruction and compared to the original input. A measure called KL Divergence is used to analyze the accuracy of the net. The training process involves continuously tweaking the weights and biases during both passes until the input is as close as possible to the reconstruction.

If you’ve ever worked with an RBM in one of your own projects, please comment and tell me about your experiences.

Because RBMs try to reconstruct the input, the data does not have to be labelled. This is important for many real-world applications because most data sets – photos, videos, and sensor signals for example – are unlabelled. By reconstructing the input, the RBM must also decipher the building blocks and patterns that are inherent in the data. Hence the RBM belongs to a family of feature extractors known as auto-encoders.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
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https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
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This Azure tutorial video will help you understand need of cloud computing, what is cloud computing, what is Azure, Azure services and more.
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Cloud infrastructure is set to dominate the IT space. Microsoft Azure is growing faster than any other cloud services provider with an incredible 154% YOY growth rate. The data is impressive and quite convincing as to why you could consider learning cloud technologies like MS Azure. The opportunities for Microsoft Azure certified aspirants have drastically multiplied in recent years. Cloud technologies like Azure are evolving at a rapid clip, outpacing the average rate of growth in the IT sector as a whole. Now, let us get started and understand what is Azure and how does it work along with the various Azure services.

Below topics are explained in this Azure tutorial for beginners:
1. Why cloud computing? ( 00:28 )
2. What is cloud computing? ( 01:39 )
3. What is Azure? ( 03:58 )
4. Azure services ( 05:41 )
5. Uses of Azure ( 09:16 )
6. Key takeaways ( 09:58 )

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You will find in-depth content on Azure. Browse further to discover similar resources on related topics, made available to you as a learning path. Enjoy top-quality learning for FREE.

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Learn how to train Chat GPT to write in your own style so it can produce on-brand blog posts, social media posts, video scripts and more.

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This Edureka video on 'Top 10 Web Frameworks in 2021' lists down the best frameworks that you can use to develop web applications.

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For Online Training and Certification, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free) for more information.

Anthropic’s Barry Zhang (Applied AI), Erik Schluntz (Research), and Alex Albert (Claude Relations) discuss the potential of AI agents, common pitfalls to avoid, and how to prepare for the evolving landscape.

Read more advice on building agents: https://www.anthropic.com/rese....arch/building-effect

00:00 Introduction
00:26 Defining AI agents and workflows
02:55 Anatomy of an agent prompt
04:29 Behind the scenes stories
07:29 Why write about agents now
08:53 Overhyped and underhyped aspects of agents
09:57 Identifying useful applications of agents
10:47 Coding agents: Potential and challenges
12:47 The future of agents in 2025
16:26 Advice for developers exploring agents