Wide & Deep Learning (https://research.googleblog.co....m/2016/06/wide-deep- combines the power of memorization and generalization by jointly training wide linear models and deep neural networks. We've open-sourced the implementation with an easy-to-use API in TensorFlow. It's effective for generic large-scale regression and classification problems with sparse inputs, such as recommender systems, search, ranking problems and more. We hope you find it useful in your machine learning projects.

Check out our blog post with links to tutorials, code samples, and our research paper:
- Google Research Blog post: https://research.googleblog.co....m/2016/06/wide-deep-
- TensorFlow Linear Model Tutorial: https://www.tensorflow.org/tutorials/wide/
- TensorFlow Wide & Deep Learning Tutorial: https://www.tensorflow.org/tut....orials/wide_and_deep
- Research paper: http://arxiv.org/abs/1606.07792

Watch all Machine Learning videos: https://goo.gl/gnXPPw

Subscribe to the Google Developers YouTube channel: http://goo.gl/mQyv5L

Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG

In this Python tutorial, We'll see how to create an AI Text Generation Solution with GPT-Neo from Eleuther AI.
We'll learn
1. About GPT-Neo
2. How to install the latest Hugging Face Transformers Package
3. Load Text Generation pipeline and Download Pre-trained GPT-Neo Models
4. Text Generation

Eleuther AI - https://www.eleuther.ai/
GPT-Neo on Hugging Face Model Hub - https://huggingface.co/EleutherAI/gpt-neo-1.3B
GPT-Neo - https://github.com/EleutherAI/gpt-neo
GPU Focused GPT-NeoX - https://github.com/EleutherAI/gpt-neox/
Colab Code - https://colab.research.google.....com/drive/1UByjdT5l_

This course is a detailed introduction to system design for software developers and engineers.

Building large-scale distributed systems like Google, Facebook, Amazon, and Twitter requires an in-depth understanding of computer science principles. This allows systems to handle millions of users concurrently despite hardware failures.

We discuss the fundamental concepts of system design in this course like requirement selection, API design, Database Design, Network protocols, Fault-tolerance, design trade-offs, solution tradeoffs, and low-level design.

Gaurav Sen created this course. Check out his channel: @gkcs

You can learn about distributed systems and system design using the following resources:
Designing Data-Intensive Applications by Martin Kleppmann: https://amzn.to/3SyNAOy
System Design Simplified: https://interviewready.io

Let us know your thoughts and suggestions in the comments!

⭐️ Contents ⭐️
⌨️ (0:00:00) What is System Design
⌨️ (0:02:27) Design Patterns
⌨️ (0:04:07) Live Streaming System Design
⌨️ (0:07:42) Fault Tolerance
⌨️ (0:08:32) Extensibility
⌨️ (0:09:49) Testing
⌨️ (0:10:32) Summarizing the requirements
⌨️ (0:11:27) Core requirement - Streaming video
⌨️ (0:14:52) Diagramming the approaches
⌨️ (0:17:12) API Design
⌨️ (0:20:02) Database Design
⌨️ (0:22:32) Network Protocols
⌨️ (0:29:07) Choosing a Datastore
⌨️ (0:32:57) Uploading Raw Video Footage
⌨️ (0:34:37) Map Reduce for Video Transformation
⌨️ (0:39:24) WebRTC vs. MPEG DASH vs. HLS
⌨️ (0:41:55) Content Delivery Networks
⌨️ (0:42:27) High-Level Summary
⌨️ (0:44:48) Introduction to Low-Level Design
⌨️ (0:47:00) Video Player Design
⌨️ (0:49:17) Engineering requirements
⌨️ (0:50:32) Use case UML diagram
⌨️ (1:02:32) Class UML Diagram
⌨️ (1:06:27) Sequence UML Diagram
⌨️ (1:11:49) Coding the Server
⌨️ (1:24:23) Resources for System Design

🎉 Thanks to our Champion and Sponsor supporters:
👾 Nattira Maneerat
👾 Heather Wcislo
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👾 Justin Hual
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Machine Learning is widely used in organizations to solve business problems. In this video, we'll look at the Top 10 Machine Learning Projects that are majorly used in the industries. You'll learn the different algorithms and the models that are required for the projects. This video will also help any machine learning enthusiast to get an idea about how these projects are being implemented and what their benefits are.

🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate:
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Get the Simplilearn app: https://simpli.app.link/OlbFAhqMqgb

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MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2023 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

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

Lecture Outline
0:00​ - Introduction
8:14 ​ - Course information
11:33​ - Why deep learning?
14:48​ - The perceptron
20:06​ - Perceptron example
23:14​ - From perceptrons to neural networks
29:34​ - Applying neural networks
32:29​ - Loss functions
35:12​ - Training and gradient descent
40:25​ - Backpropagation
44:05​ - Setting the learning rate
48:09​ - Batched gradient descent
51:25​ - Regularization: dropout and early stopping
57:16​ - Summary

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us on @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

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

Professor Christopher Manning & PhD Candidate Abigail See, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/....cs224n/index.html#sc

0:00 Introduction
0:27 Announcements
1:09 Overview
2:47 Natural Language Generation (NLG)
5:00 Recap: training a (conditional) RNN-LM
6:21 Recap: decoding algorithms
6:47 Recap: greedy decoding
7:32 Recap: beam search decoding
8:57 Aside: Do the hosts in Westworld use beam search?
10:07 What's the effect of changing beam size k?
13:16 Effect of beam size in chitchat dialogue
15:58 Sampling-based decoding
18:22 Softmax temperature
21:03 Decoding algorithms: in summary
22:55 Summarization: task definition
27:04 Summarization: two main strategies
28:20 Pre-neural summarization
31:00 Summarization evaluation: ROUGE
35:20 Neural summarization (2015-present)
38:53 Neural summarization: copy mechanisms
42:58 Neural summarization: better content selection
43:47 Bottom-up summarization
45:46 Neural summarization via Reinforcement Learning
49:36 Pre- and post-neural dialogue
50:56 Seq2seq-based dialogue
52:36 Irrelevant response problem
54:19 Genericness / boring response problem
56:38 Repetition problem
59:12 Storytelling
59:35 Generating a story from an image

GPT-3 was in all news its Human-like Text Generation capabilities. Shame it was never opensourced by Open AI (!). Hence EleutherAI with a bunch of researchers set out to create a true GPT-3 open-source alternative and GPT-J-6B is the product of those efforts.

GPT-J-6B is a 6 billion parameter, autoregressive text generation model trained on The Pile.

Useful Links:
✅ Mesh Transformer JAX - https://github.com/kingoflolz/mesh-transformer-jax
✅ GPT-J-6B: 6B JAX-Based Transformer Blogpost by Aran Komatsuzaki - https://arankomatsuzaki.wordpr....ess.com/2021/06/04/g
✅Web Demo of GPT-J-6B for Text Generation - https://6b.eleuther.ai/
✅Colab (Python) Notebook - https://colab.research.google.....com/github/kingoflol
✅JAX - https://jax.readthedocs.io/en/....latest/notebooks/qui

Related Videos:

🎥 AI Text Generation with GPT-3 OpenSource Alternative GPT-Neo Model using Hugging Face Hub
- https://www.youtube.com/watch?v=0PuVk6c8Ua8

🎥 AI-Generated Blog Content with GPT-Neo (GPT-3 Alternative) + Gradio | Python ML Web App - https://youtu.be/d_xRYyy2LFM

This is the 2nd video for HTML registration form in New responsive design course from Freecodecamp.org.

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Will we see a new generation of airships roaming our skies? Head to https://www.odoo.com/r/veritasium to start building your own website for free.

If you’re looking for a molecular modeling kit, try Snatoms – a kit I invented where the atoms snap together magnetically – https://ve42.co/SnatomsV

▀▀▀
Thank you to Eli Dourado for letting us explore the argument he describes in his article: https://ve42.co/Dourado

A huge thank you to Dan Grossman and Nick Allman for their time, help, and expertise.

Also a massive thank you to those who helped us understand the world of modern airships, and provided valuable feedback - Prof. Barry Prentice, Gennadiy Verba, Prof.
Christoph Pflaum, Heather Roszczyk, Dr. Casey Handmer, Richard Van Trueren, & Thibault Proux.

We are also grateful for the collaboration of the companies who are working hard to make this comeback happen - Atlas LTA, Buoyant Aircraft Systems International, Hybrid Air Vehicles, LTA Research, & Flying Whales.


▀▀▀
References:

How Airships Could Overcome a Century of Failure, Bloomberg Originals via YouTube - https://ve42.co/AirshipsCoF

Why the Airship May Be the Future of Air Travel, Undecided with Matt Ferrell via YouTube - https://ve42.co/FutureAirships

Airship, Wikipedia - https://ve42.co/AirshipWiki

Handmer, C. (2020). A quick note on airships. Casey Handmer’s Blog - https://ve42.co/Handmer2020

UNCTAD (2020). Review of Maritime Transport 2020 - https://ve42.co/RMT2020

National Transportation Research Center (2023). Freight Analysis Framework Version 5 (FAF5) - https://ve42.co/FAF5

Hybrid Air Vehicles (2023). HAV - https://ve42.co/HAV

LTA Research (2023). Lighter Than Air (LTA) Research - https://ve42.co/LTAResearch

OceanSkyCruises (2023). North Pole Expedition - OceanSkyCruises - https://ve42.co/NPExpedition

Flying Whales (2023). Flying Whales - https://ve42.co/FlyingWhales

Buoyant Aircraft Systems International (2023). BASI - https://ve42.co/BASI

Atlas LTA (2023). Atlas Electric Airships | Atlas LTA Airships - https://ve42.co/AtlasLTA

Prentice, B. (2021). Hydrogen gas-fuelled airships could spur development in remote communities. The Conversation - https://ve42.co/HydrogenAirships

Grossman, D. (2009). The Hindenburg Disaster. Airships - https://ve42.co/Hindenburg1

Hindenburg Disaster, Wikipedia - https://ve42.co/HindenburgWiki

What happened to the Hindenburg?, Jared Owen via Youtube - https://ve42.co/Owen2019

National Museum of the U.S. Navy. USS Akron (ZRS-4) - https://ve42.co/USSAkron

USS Akron, Wikipedia - https://ve42.co/USSAkronWiki


▀▀▀
Special thanks to our Patreon supporters:
Adam Foreman, Amadeo Bee, Anton Ragin, Balkrishna Heroor, Benedikt Heinen, Bernard McGee, Bill Linder, Burt Humburg, Dave Kircher, Diffbot, Evgeny Skvortsov, Gnare, Jesse Brandsoy, John H. Austin, Jr., john kiehl, Josh Hibschman, Juan Benet, KeyWestr, Lee Redden, Marinus Kuivenhoven, Mario Bottion, MaxPal, Meekay, meg noah, Michael Krugman, Orlando Bassotto, Paul Peijzel, Richard Sundvall, Sam Lutfi, Stephen Wilcox, Tj Steyn, TTST, Ubiquity Ventures

▀▀▀
Written by Casper Mebius & Derek Muller
Directed by Casper Mebius
Edited by Jack Saxon
Filmed by Derek Muller, Jamie MacLeod, Han Evans, & Raquel Nuno
Animation by Mike Radjabov & Fabio Albertelli
Additional video/photos supplied by Getty Images, Pond5, & Envato Elements
Music from Epidemic Sound & Pond5
Produced by Casper Mebius, Derek Muller, & Han Evans


More footage & photos from:
Thermite Rail Welding video by dulevoz via YouTube - https://www.youtube.com/watch?v=rNjosF789X4

O’Rourke, T. (2016). Chronicle Covers: When the Hindenburg burst into flames. San Francisco Chronicle - https://ve42.co/Hindenburg2

Wind turbine blade transport video by DOLL Fahrzeugbau via YouTube - https://www.youtube.com/watch?v=5aPXuap0LZw

Wind turbine blade transport through mountains video by CGTN via Youtube - https://www.youtube.com/watch?v=9dtUrY8_1CM

Former Airship Hangar by Stefan Kühn - https://ve42.co/Aerium

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
This video on Artificial Intelligence gives you a brief introduction to AI discussing how it can be a threat as well as useful.

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

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Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free) for more information.

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?