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Deep Learning is the the most exciting subfield of Artificial Intelligence, yet the necessary hardware costs keep many people from participating in its research and development. I wanted to see just how cheap a deep learning PC could be built for in 2020, so I did some research and put together a deep learning PC build containing brand new parts that comes out to about 450 US dollars. I chose NewEgg for the parts because it has a global shipping policy, deep learning belongs to the world not just the United States. In this episode, I’m going to walk you through what the deep learning stack looks like (CUDA, Jupyter, PyTorch, etc.) , why i chose the various hardware components, and then I’ll show you how to setup the full deep learning software stack on your PC. Enjoy!
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DIY Deep Learning PC parts list (about $450):
-------------------------------------------------------
GPU (GTX 1650): https://bit.ly/31Jb4Hu
Motherboard (MSI A320M ): https://bit.ly/2uyCaop
Hard Drive (Seagate Firecuda 1TB): https://bit.ly/2tKxUSi
RAM (SK Hynix 8 GB): https://bit.ly/2UMpWD8
Power Supply (Corsair 450W): https://bit.ly/2w74yOT
CPU (AMD Ryzen 3 Series 4 Core 3.1 ghz): https://bit.ly/31HwiFl
PC Case (2 fans built-in): https://bit.ly/39p7IMo
--------------------------------------------------------
Note* - each part price is always fluctuating +/- 10 dollars in price
The ABS $600 pre-built pc:
https://bit.ly/2OJjcCh
PyTorch’s Image Classifier Example:
https://pytorch.org/tutorials/....beginner/blitz/cifar
Linus Tech Tips POV PC Build Guide:
https://www.youtube.com/watch?v=v7MYOpFONCU
Instructables PC Build Guide:
https://www.instructables.com/....id/Build-a-Gaming-Co
Nvidia’s CUDA Documentation:
https://docs.nvidia.com/
Docker:
http://docker.com/
Petronetto’s Deep Learning Docker Image:
https://github.com/petronetto/....docker-python-deep-l
Another Deep Learning Docker Image:
https://github.com/NVAITC/ai-lab
Are you a total beginner to machine learning? Watch this:
https://www.youtube.com/watch?v=Cr6VqTRO1v0
Learn Python:
https://www.youtube.com/watch?v=T5pRlIbr6gg
Live C Programming:
https://www.youtube.com/watch?v=giF8XoPTMFg
CUDA Explained:
https://www.youtube.com/watch?v=1cHx1baKqq0
Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams!
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Can't afford a PC right now? That's OK, use Google Colab for a free cloud GPU:
https://colab.research.google.com/
Credits:
Nvidia team
PyTorch team
Image/GIF assets are from across the Web, i take no credit for them
(except some memes)
Comedy Central (“Nathan for you” clip)
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
DATA is available on the Trimble Learn platform: https://learn.trimble.com/lear....n/course/external/vi
This course is intended to introduce the Deep Learning (Convolutional Neural Network (CNN)) functionalities within the Trimble eCognition Developer Software and consists of 4 videos.
+ Introduction to Deep Learning 1 of 4: Introduction and Set-up
+ Introduction to Deep Learning 2 of 4: Creating Samples
+ Introduction to Deep Learning 3 of 4: Create / Train / Save CNN
+ Introduction to Deep Learning 4 of 4: Apply CNN with OBIA
This course is for free and can be conducted also with the Developer Trial version: https://geospatial.trimble.com/ecognition-trial.
Accessing this course from the Trimble Learn platform, you will have to create an account (also for free) and enroll to this course. Additionally to the DATA you will also receive a CERTIFICATE if you finish the course on the Trimble Learn platform.
Enjoy diving into eCognitions Deep Learning world!
______________Video Content_________________
00:00 - Introduction & Theory
04:13 - Create Project and load Data
05:32 - Rename Aliases
06:43 - Save Project
(⊙_☉)
Data scientists use a variety of metrics in order to objectively determine the performance of a model. This clip will provide an overview of some of the most common metrics such as error, precision, and recall.
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
URL for article on metrics (this Wikipedia article is actually very informative) -
https://en.wikipedia.org/wiki/Precision_and_recall
Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal
Introducing the new Lambda Tensorbook – the world’s most powerful laptop for deep learning: https://rzr.to/lambda-tensorbook
Co-created with Lambda, this sleek laptop is powered by the latest NVIDIA GeForce RTX 3080 Max-Q 16GB GPU and machine learning tools including PyTorch to give engineers everything needed to create, train and test anytime, anywhere.
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Enroll today at https://www.coursera.org/speci....alizations/deep-lear to get access to the specialization!
About this Specialization:
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You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
Dr. B.S. Manjunath, distinguished professor in the Department of Electrical & Computer Engineering at UC Santa Barbara, discusses the use of computer video technology to assist with visual analysis of issues like human stress and disease, methane gas release, and underwater mapping. He also discusses what we know about human vision, how it works compared to how computer vision works. Recorded on 10/21/2021. [4/2022] [Show ID: 37871]
More from: GRIT Talks
(https://www.uctv.tv/grit)
Explore More Science & Technology on UCTV
(https://www.uctv.tv/science)
Science and technology continue to change our lives. University of California scientists are tackling the important questions like climate change, evolution, oceanography, neuroscience and the potential of stem cells.
UCTV is the broadcast and online media platform of the University of California, featuring programming from its ten campuses, three national labs and affiliated research institutions. UCTV explores a broad spectrum of subjects for a general audience, including science, health and medicine, public affairs, humanities, arts and music, business, education, and agriculture. Launched in January 2000, UCTV embraces the core missions of the University of California -- teaching, research, and public service – by providing quality, in-depth television far beyond the campus borders to inquisitive viewers around the world.
(https://www.uctv.tv)
In this deep learning with Python and Pytorch tutorial, we'll be actually training this neural network by learning how to iterate over our data, pass to the model, calculate loss from the result, and then do backpropagation to slowly fit our model to the data.
Text-based tutorials and sample code: https://pythonprogramming.net/....training-deep-learni
Linode Cloud GPUs $20 credit: https://linode.com/sentdex
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#pytorch #deeplearning #machinelearning
PyData New York City 2017
Slides: https://ericmjl.github.io/baye....sian-deep-learning-d
In this talk, I aim to do two things: demystify deep learning as essentially matrix multiplications with weights learned by gradient descent, and demystify Bayesian deep learning as placing priors on weights. I will then provide PyMC3 and Theano code to illustrate how to construct Bayesian deep nets and visualize uncertainty in their results. 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
This interview is published from deeplearning.ai’s Deep Learning Specialization (https://www.coursera.org/speci....alizations/deep-lear on Coursera.
It is part of the course on “Convolutional Neural Networks” (https://www.coursera.org/learn..../convolutional-neura
We at iNeuron are happy to announce multiple series of courses. Finally we are covering Big Data,
Cloud,AWS,AIops and MLops. Check out the syllabus below.
30 Days Data Science Interview preparation - https://rb.gy/q1c58g
Big Data Master-https://rb.gy/xg1ob7
Business analytics-https://rb.gy/herdd8
Aws Cloud Masters- https://rb.gy/x5poyj
AI OPS- https://rb.gy/kionj6
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Deep Learning NVR DVA3219 is an AI-powered video analytics server perfect for those who are concerned about privacy and security.
Powered by NVIDIA GeForce® graphic card, advanced deep learning-based algorithms are built into DVA3219 to transform a vast amount of unstructured video data into situational awareness and business intelligence. The video analytics process is done entirely on-premises, meaning no footage is uploaded to the cloud for analysis purposes.
With Deep Learning NVR DVA3219, you'll be able to enjoy the benefits of AI-powered video analytics without sweating over sensitive information and regulatory requirements.
Learn more about DVA3219
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A visual introduction to the structure of an artificial neural network. More to come!
Support me on Patreon! https://patreon.com/vcubingx
Source Code: https://github.com/vivek3141/dl-visualization
Here's the course I referred to in the video. I am not affiliated with NYU.
https://www.youtube.com/playli....st?list=PLLHTzKZzVU9
Here's 3blue1brown's video on Linear Transformations:
https://youtu.be/kYB8IZa5AuE
Special thanks to Matt Henderson, David Ha, Oliver Ni and Sumedh Shenoy for reviewing the video.
And also thanks to Grant Sanderson himself for giving me some manim tips!
I've been quite active on twitter, follow me here!
https://twitter.com/vcubingx
Join the discord server!
https://discord.gg/Kj8QUZU
These videos were made using 3blue1brown's library, manim:
https://github.com/3b1b/manim
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The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1
0:00 Intro
1:55 One input Perceptron
3:30 Two input Perceptron
4:40 Three input Perceptron
5:17 Activation Functions
6:58 Neural Network
9:45 Visualizing 2-2-2 Network
10:59 Visualizing 2-3-2 Network
12:33 Classification
13:05 Outro
Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more
https://www.youtube.com/channe....l/UCNU_lfiiWBdtULKOw In this video we will understand about the max pooling layer in CNN
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Reviewing Lambda and Razer's Tensorbook, a laptop aimed at deep learning, with 16GB of VRAM (GPU memory), 64GB of RAM, 2TB of NVMe storage and an 8-core intel i7 11800H CPU.
https://lambdalabs.com/deep-le....arning/laptops/tenso
Neural Networks from Scratch book: https://nnfs.io
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Deep learning, a subset of Machine learning, uses neural networks to teach computers the human thinking process. Let ProjectPro assists you on this journey of learning by providing video tutorials as well as hands-on experience of industry-level projects.
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