NVIDIA announced its new CPU today, a collaboration with ARM yielding the "Grace" CPU. The company is also releasing new RTX GPUs (the A4000, A5000, A6000).
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NVIDIA's GTC 2021 conference saw the unveil of a series of new GPUs for professionals and data center applications, but all of these were overshadowed by NVIDIA's renewed interest in the CPU space. NVIDIA has been trying to purchase ARM since 2020 (currently pending regulatory approval), and in the meantime, the two organizations are working together to produce the new NVIDIA Grace CPU for data center and server applications. The NVIDIA Grace CPU aims to improve bandwidth of CPU-to-GPU communications and CPU-to-CPU communications, outperforming traditional PCIe signaling with NVIDIA's NVLink.

The GPUs announced are part of the former Quadro line, branding that NVIDIA is slowly moving away from. The A5000 and A4000 GPUs are in the high-end of Ampere RTX cards, with mobile versions also available in Max-Q laptops.

Separately from all of this, NVIDIA talked more about its Omniverse software and a few RTX games, including Black Myth: Wukong, Boundary, Narak: Bladepoint, and Bright Memory. Cyberpunk also got a comically short pseudo-reference.

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TIMESTAMPS

00:00 - NVIDIA GTC 2021 Announcements
01:03 - NVIDIA's New "Grace" ARM CPU
05:11 - New GPUs from NVIDIA: A4000, A5000
10:51 - A10, A16, & Omniverse

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This Edureka video on "Best Laptops for Machine Learning" will provide you the detail and comprehensive knowledge about the best laptops that you can use for machine learning.

Below is the Link to Laptops

TensorBook: https://lambdalabs.com/deep-le....arning/laptops/tenso
MacBook: https://www.apple.com/shop/buy-mac/macbook-pro
Asus ROG Strix GL702VS: https://www.asus.com/Laptops/ROG-GL702VS/
ASUS ROG Zephyrus S: https://www.asus.com/Laptops/ROG-Zephyrus-S-GX531/
Dell XPS 15 9560: https://www.dell.com/en-in/sho....p/laptops-2-in-1-pcs
Razer Blade 15: https://www.razer.com/gaming-laptops/razer-blade
MSI GS65: https://www.msi.com/Laptop/GS65-Stealth-Thin-8RF
Acer Predator (Helios 300 and Triton 700): https://www.acer.com/ac/en/US/....content/predator-ser

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3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!

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Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR

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The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for:
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Information Architects who want to gain expertise in Predictive Analytics
'R' professionals who wish to work Big Data
Analysts wanting to understand Data Science methodologies

-------------------------------------

Why learn Data Science?
Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices.
Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes.
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In this video we will see Why Data Structures are important for Every Programmer?

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This is my solution to costFunctionReg.m function in Programming assignment 2 from the famous Machine Learning course by Andrew Ng.

Github: https://github.com/AladdinPerz....on/Courses/tree/mast

Machine learning models don't understand words. They should be converted to numbers before they are fed to RNN or any other machine learning model. In this tutorial, we will look into various techniques for converting words to numbers. These techniques are,
1) Using unique numbers
2) One hot encoding
3) Word embeddings

Deep learning playlist: https://www.youtube.com/playli....st?list=PLeo1K3hjS3u
Machine learning playlist : https://www.youtube.com/playli....st?list=PLeo1K3hjS3u

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