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In this video on What Is Pi Network, we embark on a journey through the world of Pi Network. Discover what sets Pi apart from other cryptocurrencies as we unravel its unique features and functionalities. Gain a deeper understanding of how Pi works, including its innovative mining approach and decentralized nature. We'll also delve into the current price of Pi and explore its potential trajectory in the ever-evolving crypto market. Join us as we peer into the future of Pi coin and discuss the possibilities it holds for reshaping the landscape of digital currency. Get ready to dive into the exciting realm of Pi Network!

00:00 Introduction
02:10 What is Pi network ?
03:30 What makes Pi different from others ?
04:44 How Pi works ?
06:14 Pi network price
06:59 What is the future of Pi coin ?

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In this React Interview Questions and Answers you will learn the latest and top questions asked by companies for React interview. This ReactJS Interview Questions & Answers video covers all kinds of questions starting from basic to advanced questions so that you can get benefited. We can assure you that if you watch this React interview preparation video completely you can crack any React job interview easily.
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👉Following questions are covered in this video:
00:00 - React Interview Questions
01:12 - Differentiate Between Virtual DOM and Real DOM
03:10 - What is React?
04:32 - What is the meaning of Virtual DOM?
05:51 - What are some of the important features of React?
07:24 - What is the meaning of JSX?
08:41 - Can browser read a JSX File?
09:53 - Why is React widely used today?
11:48 - Are there any Disadvantages to using react?
13:32 - Differentiate between Angular and React
14:44 - What is the meaning of the component based architecture of react?
15:56 - How does rendering work in React?
16:55 - What are states in React?
18:37 - What is the use of an arrow function in React?
19:47 - What is a higher-order component in React?
20:56 - What us the meaning of create-react-app in React?
21:52 - What are some of the advantages of using create-react-app n React?
23:00 - What is the meaning of Redux?
24:19 - What is the difference between props and states?
25:21 - What are the three phases of a component life cycle in React?
26:48 - What are events in React?
27:47 - How are events created in React?
28:45 - How is routing in React different from conventional routing?
30:25 - Differentiate between Flux and Redux in React
32:00 - Can AJAX be used with React?
33:06 - What is the meaning of synthetic events in React?
34:05 - What are stateful components in React?
34:57 - What are refs in React?
36:04 - What are Controlled Components in React?
37:02 - Why is a router required in React?
38:11 - What are the components of Redux in React?
39:12 - What are the advantages of using Redux?
40:37 - What are the disadvantages of using MVC in React?
41:58 - What are pure components in React?
43:04 - What are higher-order components (HOCs) used for?
44:02 - What are keys in React?
45:06 - Differentiate between a controlled components and an uncontrolled components in React
46:05 - How can you tell React to build in the production mode?
47:40 - What is the use of the second argument that is passed to set state? is it optional?
49:33 - What is the Strict Mode component used in React?
50:25 - What would you do if your React application is rendering slowly?
51:50 - What would you do if react application is rendering slowly?
54:26 - What are the predefined prop types presentin React?
55:13 - What is React Fiber?
56:02 - What are Hooks in React?

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Why should you learn React.JS?

React is a JavaScript library that specializes in helping developers build user interfaces, or UIs. React on its own is a very functional, modular way to create expressive UI, and with its single responsibility mentality, it can be integrated with other systems as well as utilize its own ecosystem to develop full single page and multi page apps.

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This K Means clustering algorithm tutorial video will take you through machine learning basics, types of clustering algorithms, what is K Means clustering, how does K Means clustering work with examples along with a demo in python on K-Means clustering - color compression. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K Means clustering work.

Below topics are covered in this K-Means Clustering Algorithm Tutorial:
1. Types of Machine Learning? ( 07:08 )
2. What is K Means Clustering? ( 00:10 )
3. Applications of K Means Clustering ( 09:27 )
4. Common distance measure ( 10:20 )
5. How does K Means Clustering work? ( 12:27 )
6. K Means Clustering Algorithm ( 20:08 )
7. Demo In Python: K Means Clustering ( 26:20 )
8. Use case: Color compression In Python ( 38:38 )

Dataset Link - https://drive.google.com/drive..../folders/1RdPdB3Fkqo

What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

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Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations.

✏️ Course created by Tatev Karen Aslanyan.

✏️ Expanded course with 100 questions: https://academy.lunartech.ai/p....roduct/deep-learning

⭐️ Contents ⭐️
⌨️ 0:00:00 Introduction
⌨️ 0:08:20 Question 1: What is Deep Learning?
⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning?
⌨️ 0:15:25 Question 3: What is a Neural Network?
⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning
⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way
⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network?
⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them
⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network?
⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works
⌨️ 0:53:45 Question 10: What is Gradient Descent?
⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning?
⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning?
⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent?
⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN
⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN
⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason?
⌨️ 1:37:50 Question 17: What do you understand by a computational graph?
⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning?
⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry?
⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems?
⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks?
⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima?
⌨️ 2:03:38 Question 23: How is GD different from SGD?
⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term?
⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent.
⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)?
⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model?
⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages?
⌨️ 2:34:12 Question 29: What is RMSProp and how does it work?
⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods
⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs?
⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam?
⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN?
⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN?
⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN?
⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN?
⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN?
⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients?
⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients?
⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights?
⌨️ 3:29:18 Question 41: What is Dropout and how does it work?
⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN?
⌨️ 3:35:06 Question 43: Is Dropout like Random Forest?
⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing?
⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN?
⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN?
⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN?
⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI?
⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality?
⌨️ 3:56:47 Question 50: What are Generative Models, give examples?

Official audio for I Want To Wake Up by Pet Shop Boys taken from their 1987 album, 'Actually'.

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Lyrics:

[Chorus]
Hit music on the radio
Hit music on the radio

[Verse 1]
When you dance with me we dance forever
All night long to your favourite

[Chorus]
Hit music on the radio
Hit music on the radio

[Verse 2]
When you dance with me we dance forever
All night long to the latest song
Live a lie, dance together
All night long to your favourite

[Chorus]
Hit music
Hit music

[Bridge]
I've been working hard all day
To pay the bills I have to pay
Don't have the strength to work all night
Or fight until it's almost light

[Chorus]
Music on the radio
D-dance music at the disco

[Verse 3]
Live a lie, dance together
All night long to your favourite song
Live and die, it's all that we know
I need a friend at the journey's end

[Chorus]
Hit music in stereo

[Interlude]

[Verse 4]
It's all about love and it's about forgetting
Choose a song when the night's too long
We all need love and we want protection
I need a friend at the journey's end
In Kensington or Spanish Harlem
All night long to your desperate

[Chorus]
Hit music on the radio
Music at the disco

[Chorus]
Hit music on the radio
Hit music on the radio

#PetShopBoys #HitMusic

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Learn how to build AI agents from scratch in this beginner-friendly tutorial!

Jan breaks down the complex world of AI agents into simple, understandable components and shows you how to create a fun "Santa's Helper" agent that researches toys and sends wish lists - all without writing a single line of code.

What you'll learn:
▶︎ Understanding AI agent components and architecture
▶︎ Setting up large language models (LLMs)
▶︎ Implementing agent memory systems
▶︎ Writing effective agent system prompts
▶︎ Connecting research and web search tools
▶︎ Automating email communications
▶︎ Testing and debugging agents
▶︎ Real-world business applications

⏰ TIMESTAMPS
00:00 What are AI agents and how do they work
00:30 AI agent components and architecture explained
02:31 Understanding agent tools and capabilities
05:19 Building your first agent in n8n
06:30 Connecting language models to your agent
07:37 Why memory is crucial for AI agents
09:35 Creating effective system prompts for agents
12:08 Adding web search capabilities to agents
14:38 Setting up email automation tools
15:26 Testing and debugging your AI agent
18:22 Business use cases and advanced applications

📚 RESOURCES MENTIONED
https://www.youtube.com/watch?v=SzPE_AE0eEo
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Battle-tested approaches for building agents with state-of-the-art AI capabilities

In this video we understand how intersection over union works and we also implement it in PyTorch. This is a very important metric to understand when it comes to object detection as it is used pretty much all the time.

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Object Detection Playlist:
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PyTorch Playlist:
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0:00 - Introduction
01:12 - Explanation of IOU
09:52 - Coding in PyTorch

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Movie Starts At 02:35

Destiny throws aspiring fashion designer Sia (Neha Dhupia) and television producer Raj (Sonu Sood) together during a chance encounter. He boosts Sia's career using his many connections. While Sia's designs get international attention, the pair start an intense romance, but not without their share of complications and misunderstandings. Time will tell if they can withstand what's in store for them.

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This is a clip from a conversation with Gary Marcus from Oct 2019. New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days. You can watch the full conversation here: https://www.youtube.com/watch?v=vNOTDn3D_RI
(more links below)

Podcast full episodes playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO

Podcasts clips playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO

Podcast website:
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Podcast on iTunes:
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Podcast on Spotify:
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Podcast RSS:
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Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. For now, I post a few clips every Tue & Fri. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel. I hear the 6% and partially agree, so am torn about the whole thing. I tried creating a separate clips channel but the YouTube algorithm makes it very difficult for that channel to grow unless the main channel is already very popular. So for a little while, I'll keep posting clips on the main channel. I ask for your patience and to see these clips as supporting the dissemination of knowledge contained in nuanced discussion. If you enjoy it, consider subscribing, sharing, and commenting.

Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence.

Subscribe to this YouTube channel or connect on:
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GTC: NVIDIA CEO and co-founder Jen-Hsun Huang explains how deep learning paves the way to truly self-driving cars, enabled by the NVIDIA DRIVE PX self-driving car computer, at the 2015 GPU Technology Conference, in San Jose, Calif. For more NVIDIA news: http://nvda.ly/KthtM