Movie Starts At : 01:33

A comedy of errors takes place when Sanjay and Anjali send their friends Raj and Anjali respectively to spy on each other. Raj and Anjali fall in love with each other but back off assuming the other to be married to their friend. When Raj realises that his love interest Anjali is not his friend's wife he tries to meet her and tell her his feelings, but is shocked to find Anjali engaged to someone else. Will Raj be able to stop Anjali's marriage and tell her the truth?

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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Cbr1GI

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Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
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To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/....cs224n/index.html#sc

0:00 Introduction
0:56 Deep Learning for NLP 5 years ago
1:34 Future of Deep Learning + NLP
3:32 Why has deep learning been so successful recently?
5:03 Big deep learning successes
6:20 NLP Datasets
8:12 Machine Translation Data
9:54 Pre-Training
12:10 Self-Training
18:27 Large-Scale Back-Translation
20:03 Unsupervised Word Translation
27:43 Unsupervised Neural Machine Translation
31:14 Why Does This Work?
33:46 Unsupervised Machine Translation
34:49 Attribute Transfer
38:10 Cross-Lingual BERT
43:03 Huge Models in Computer Vision
44:29 Training Huge Models
47:41 So What Can GPT-2 Do?
50:04 GPT-2 Results
51:16 How can GPT-2 be doing translation?
52:12 GPT-2 Question Answering
53:31 What happens as models get even bigger?
54:24 GPT-2 Reaction
59:26 High-Impact Decisions

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Shane Legg, the visionary founder of Deepmind has just dropped a bombshell: he envisions DeepMind achieving Artificial General Intelligence, or AGI, by the year 2028. This announcement comes as a seismic shift in the AI landscape, setting the stage for a future where machines possess human-level cognitive abilities. The implications are nothing short of revolutionary, promising to reshape industries, economies, and the very fabric of our existence!
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05:04 How AGI will be created
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In this video, we dive deep into Generative AI, exploring the power of Large Language Models like ChatGPT. Discover how these advanced models generate human-like text and uncover their possibilities for various applications. Want to learn more about it? Visit our blog for an in-depth exploration of Generative AI, LLMs, and ChatGPT's capabilities! https://bit.ly/3WXuHIi

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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.

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⭐️ 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?

MIT 18.156 Projection Theory, Spring 2025
Instructor: Lawrence D Guth
View the complete course: https://ocw.mit.edu/courses/18....-156-projection-theo
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

We formulate the main questions and goals of the class, especially the exceptional set problem.

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📌Generative AI Course: Masters Program : https://www.edureka.co/masters....-program/generative-
🔥Integrated MS+PGP Program in Data Science & AI:https://www.edureka.co/dual-ce....rtification-programs
Learn the fundamentals of generative artificial intelligence in this beginner-friendly course (2025 edition). We’ll cover what generative AI is, how it differs from traditional AI, key architectures (GANs, VAEs, Transformers), hands-on examples, prompt engineering, and real-world applications you can build today. No prior AI or deep-learning experience required.

✅ What you’ll learn:
What is generative AI & why it matters in 2026
Core models: GAN (Generative Adversarial Network), VAE (Variational Autoencoder)
Transformer architecture and large language models (LLMs)
Text-, image-, and audio-generation use cases
Basics of prompt-engineering and building your own simple GenAI app
Ethical, bias, and safety considerations for generative systems

00:00:00 Introduction
00:02:00 What is Generative AI?
00:15:29 Generative AI Examples
00:34:09 Generative AI Tools
00:55:33 What Is Artificial Intelligence?
01:06:15 Types Of Artificial Intelligence
01:16:30 What is Deep Learning
01:27:43 TensorFlow Explained
01:52:29 Convolutional Neural Network
02:13:05 Artificial Neural Network
02:44:56 Recurrent Neural Networks
03:13:50 Keras
03:38:59 What is LLM?
03:58:43 What Are GANs?
04:11:04 Transformers in Gen AI
04:18:43 Prompt Engineering Explained
04:32:28 Prompt Engineering for Code Generation
04:41:47 How to Become a Prompt Engineer?
04:46:51 Building a Chatbot with Prompt Engineering
05:03:01 GitHub Copilot
05:20:26 What is LangChain?
05:37:42 What is RAG?
06:00:34 Generate Images Using DC-GAN
06:25:08 Midjourney
06:43:30 OpenAI API using Python
06:51:44 Generative AI in Marketing
07:02:14 The Future of Generative AI and Job Opportunities
07:07:58 Nvidia's Latest Breakthrough in Generative AI
07:14:36 DeepSeek vs OpenAI: Who Wins the AI Race?
07:27:09 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
07:31:32 DeepSeek Training Cost
07:37:01 Exploring the Ethics of Generative AI
07:44:31 What is Agentic AI?
07:54:30 Agentic AI vs Generative AI
08:12:31 Dangers of AI
08:35:17 Artificial Intelligence Project Ideas
08:48:54 Top 10 Benefits Of Artificial Intelligence
09:00:42 GenAI Roadmap
09:09:27 Top 5 Generative AI Career Opportunities
09:25:40 Generative AI Interview Questions

📌 By the end of this course you’ll be equipped to explore your own generative-AI experiments and skill up for deeper tools.

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How To Build Your First AI Agents. Sign up for a paid Hyperagent plan and get $500 in bonus credits: https://www.hyperagent.com/zinho

You can build an entire team of AI agents — and it's way easier than you think. In this video, I show you how to build your first AI agent from scratch using Hyperagent (built by the team behind Airtable). No code, no complicated setup.

I walk through the one pattern every good agent is built on: solve the problem yourself first in a normal conversation, then turn that solution into an agent that does it on repeat, forever. By the end, you'll have a real agent connected to your actual tools, running on a schedule, handing you finished work while you sleep.

📂 Every prompt from this video + templates: https://www.skool.com/ai-launchpad/about

Chapters:
0:00 - Intro
01:23 - Chatbot vs. Agent
02:00 - The one pattern
02:43 - Connect your tools
03:29 - Solve the problem first
05:56 - Turn it into an Agent
07:04 - Building a skill
09:01 - Building a second agent
10:39 - Command Center
12:05 - The Recap

🔗 Try Hyperagent — first 500 signups get $500 in free credits: https://www.hyperagent.com/zinho
🔗 Free Skool community (prompts + workflows): https://www.skool.com/ai-launchpad/about

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