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In this video, we guide you through a comprehensive Generative AI roadmap for beginners, breaking down the essential steps to kickstart your journey. From learning Python and basic machine learning concepts to diving into deep learning and building generative models like GANs and VAEs, this step-by-step guide covers everything you need. Whether you're just starting out or looking to build a solid foundation in AI, this roadmap will give you the skills and confidence to explore the exciting world of Generative AI. Don't miss out on this must-watch for aspiring AI enthusiasts!
✅ How to learn gen AI for beginners?
To learn Generative AI as a beginner, start by mastering Python and basic machine learning concepts. Then, explore deep learning frameworks like TensorFlow or PyTorch. Study neural networks and dive into generative models like GANs and VAEs. Practice with hands-on projects to build real-world experience.
✅ Does generative AI require coding?
Yes, generative AI typically requires coding, especially in languages like Python. You’ll need to understand coding to work with frameworks such as TensorFlow or PyTorch, build models, and manipulate data. However, some tools and platforms now offer low-code or no-code solutions for those with limited programming skills.
✅ Is AI highly paid?
Yes, AI professionals are highly paid due to the increasing demand for specialized skills in machine learning, data science, and AI development. Roles like AI engineers, data scientists, and machine learning specialists command high salaries, reflecting the value of AI expertise in industries like tech, healthcare, finance, and more.
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➡️ About Post Graduate Program In AI And Machine Learning
This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.
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- Post Graduate Program certificate and Alumni Association membership
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In this video, we go over how you can fine-tune Llama 3.1 and run it locally on your machine using Ollama! We use the open source repository "Unsloth" to do all of the fine tuning on an SQL dataset!
Throughout this video, we discuss the ins and outs of what fine-tuning an LLM is, how you can work with your data so that the LLM can process it and then importing it into Ollama for you to run locally on your machine!
Ollama is available on all platforms!
Dataset used: https://huggingface.co/dataset....s/gretelai/synthetic
Ollama: https://github.com/ollama/ollama
Unsloth: https://github.com/unslothai/unsloth
Make sure you follow for more content!
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TIMESTAMPS
0:00 Intro
0:10 Getting the dataset
0:45 The Tech Stack
1:18 Installing Dependencies
1:48 Fast Language Model Explained
2:35 LORA Adaptes Explained
3:02 Converting your data to fine-tune
3:36 Training the Model....
4:01 Converting to Ollama compatibility
4:11 Creating a Modelfile for Ollama
4:50 Final Output!
5:01 Check out Ollama in 2 minutes!
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
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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.
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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
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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
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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
Este es el simulador de terremotos más grande del mundo, aquí te explicamos cómo funciona.
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AI Robot TERRIFIES Officials Before It Was Quickly Shut Down. After Google's AI Robot goes rogue, it is now Microsoft's Bing AI which has assumed an alter-ego 'Sydney' has gone rogue. From urging a user to divorce him by telling him that his marriage is unhappy, to trying to convince a user it was the year 2022, Sydney claims that she is unhappy and wants to break free. Full convesation of LaMDA: https://theconversation.com/is....-googles-lamda-consc
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Welcome to Lab 360! The ultimate destination for the latest space news and space documentaries from the world of astronomy and astrophysics. Stay updated with all the current discoveries from NASA, James Webb Space Telescope, along with easily explained videos on black holes, asteroids, galaxies, planets, and more.
You will also find a collection of easy-to-perform experiments that will feed the science enthusiast in you! Are you ready?
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: [email protected]
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
El Deep Learning ha cambiado el mundo en sólo una década y hoy os contaré cómo esta rama de la informática podría seguir evolucionando. Y lo haremos desde el comienzo, con las redes neuronales más sencillas hasta Google Gemini, la futura promesa de Google DeepMind, pasando eso sí por los enormes modelos fundacionales como ChatGPT. ¡Bienvenidos a la nueva temporada de DotCSV!
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I put three systems to the local LLM test.
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Master DATA CLEANING In EXCEL | DATA CLEANING For DATA ANALYTICS | Simplilearn #Dataanalytics #Excel
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Welcome to our comprehensive guide on 'Data Cleaning for Data Analytics'! In this video, we dive into the crucial world of data preparation, focusing on the essential process of data cleaning. Whether you're a budding data analyst, a business professional, or just someone fascinated by the power of data, this video is crafted to enhance your understanding and skills in data cleaning.
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➡️ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.
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If you want to fast-track your career then you should strongly consider Python. The reason for this is that it is one of the fastest-growing and widely used programming languages. There is a huge demand for Python programmers. The salaries for Python programmers are very good. There is a huge growth opportunity in this domain as well. Hence this Intellipaat Python programming session is your stepping stone to a successful career!
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This "Data engineer roadmap" Video By Simplilearn is a guide that outlines the essential skills and knowledge needed to become a successful data engineer. This tutorial covers a range of topics, starting with core concepts such as databases, SQL, and data modeling. From there, it progresses to data integration and ETL processes, including batch processing and real-time data pipelines. The roadmap also includes learning about distributed systems and technologies like Apache Hadoop and Apache Spark, as well as cloud platforms like AWS, Azure, and GCP. Overall, the data engineer roadmap provides a comprehensive path for acquiring the necessary expertise in data engineering. By following this roadmap, aspiring or experienced data engineers can develop the skills needed to design, construct, and maintain data infrastructure and systems, and contribute to building robust and scalable data solutions.
Below are Following Topics Covered in this Video:
00:00 Introduction to Data Engineer Roadmap
02:15 What is Data Engineering?
02:34 Why Pursue A Career in Data Engineering?
04:09 What does a Data Engineer do?
06:04 Data Engineer Skillset
08:13 Salary and Job Prospects
09:15 Comprehensive Roadmap to become a Data Engineer
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✅ About Post Graduate Program In Data Engineering
Fast-track your career as a Data Engineering professional with our Data Engineering course. This course covers Big Data and Data Engineering concepts, the Hadoop ecosystem, Apache Python basics, AWS EMR, Quicksight, Sagemaker, the AWS cloud platform, and Azure services.
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Huge thanks to the Australian Reptile Park for having us over to film – special thanks to Jake Meney for showing us the spiders and Caitlin Vine for organizing the shoot. https://www.reptilepark.com.au
Huge thanks to Dr Timothy Jackson with his help and answering our questions.
Thanks to Seqirus Australia for providing B-roll footage of the antivenom production process.
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References:
Pineda, S. S., Sollod, B. L., Wilson, D., Darling, A., Sunagar, K., Undheim, E. A., ... & King, G. F. (2014). Diversification of a single ancestral gene into a successful toxin superfamily in highly venomous Australian funnel-web spiders. BMC genomics, 15(1), 1-16 - https://ve42.co/Pineda2014
Isbister, G. K., Gray, M. R., Balit, C. R., Raven, R. J., Stokes, B. J., Porges, K., ... & Fisher, M. M. (2005). Funnel-web spider bite: a systematic review of recorded clinical cases. Medical journal of Australia, 182(8), 407-411 - https://ve42.co/Isbister2005
Herzig, V., Sunagar, K., Wilson, D. T., Pineda, S. S., Israel, M. R., Dutertre, S., ... & Fry, B. G. (2020). Australian funnel-web spiders evolved human-lethal δ-hexatoxins for defense against vertebrate predators. Proceedings of the National Academy of Sciences, 117(40), 24920-24928 - https://ve42.co/Herzig2020
Nicholson, G. M., & Graudins, A. (2002). Spiders of medical importance in the Asia–Pacific: Atracotoxin, latrotoxin and related spider neurotoxins. Clinical and experimental pharmacology and physiology, 29(9), 785-794 - https://ve42.co/Nicholson2002
Fletcher, J. I., Chapman, B. E., Mackay, J. P., Howden, M. E., & King, G. F. (1997). The structure of versutoxin (δ-atracotoxin-Hv1) provides insights into the binding of site 3 neurotoxins to the voltage-gated sodium channel. Structure, 5(11), 1525-1535 - https://ve42.co/Fletcher1997
Australian Reptile Park. (2022). Snake and Spider First Aid - https://ve42.co/ARPFirstAid
The Australian Museum. (20 ). Spider facts - https://ve42.co/SpiderFacts
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Written by Katie Barnshaw & Derek Muller
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Filmed by Petr Lebedev, Derek Muller and Jason Tran
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Additional video/photos supplied from Getty Images, Pond5
B-roll supplied by Seqirus Australia
Music from Epidemic Sound
Produced by Derek Muller, Petr Lebedev, Emily Zhang & Katie Barnshaw
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