The Intelligent Inspection toolset, powered by Deep Learning technology, provides powerful and robust on-device anomaly detection as well as object classification that is not possible with rule-based machine vision.

The user-friendly interface, on-device traning and example-based approach makes it easy to ensure that inspected items fulfill required quality and sorting demands, which helps to improve yield, reliability, productivity and increase customer satisfaction.

Intelligent Inspection toolset is part of the SICK Nova 2D SensorApp and runs directly on the InspectorP6xx 2D vision sensors.

And that’s not all, traditional rule-based machine vision tools are also included, combining benefits with deep learning side-by-side.

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GTC: NVIDIA CEO and co-founder Jen-Hsun Huang explains how deep learning paves the way to truly self-driving cars, enabled by the NVIDIA DRIVE PX self-driving car computer, at the 2015 GPU Technology Conference, in San Jose, Calif. For more NVIDIA news: http://nvda.ly/KthtM

At Mirakl, we empower marketplaces with Artificial Intelligence solutions. Catalogs data is an extremely rich source of e-commerce sellers and marketplaces products which include images, descriptions, brands, prices and attributes (for example, size, gender, material or color). Such big volumes of data are suitable for training multimodal deep learning models and present several technical Machine Learning and MLOps challenges to tackle.

We will dive deep into two key use cases: deduplication and categorization of products. For categorization the creation of quality multimodal embeddings plays a crucial role and is achieved through experimentation of transfer learning techniques on state-of-the-art models. Finding very similar or almost identical products among millions and millions can be a very difficult problem and that is where our deduplication algorithm comes to bring a fast and computationally efficient solution.

Furthermore we will show how we deal with big volumes of products using robust and efficient pipelines, Spark for distributed and parallel computing, TFRecords to stream and ingest data optimally on multiple machines avoiding memory issues, and MLflow for tracking experiments and metrics of our models.


Connect with us:
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#RanjanSharma
Explained Gradient Descent in Machine Learning and in Deep Learning in Hindi || Ranjan Sharma

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Keep Practicing :-)
Happy Learning !!


#GradientDescent #GradientDescentMachineLearning,
gradient descent in hindi,
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gradient descent algorithm,
gradient descent python,
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I share some of the best books for learning neural network or deep learning. I've been learning machine learning/deep learning for the past three years now, these books have all been instrumental throughout.

Book links (in order):
Make Your Own Neural Network by Tariq Rashid (Author)
Neural Networks and Deep Learning by Michael Nielsen (http://neuralnetworksanddeeplearning.com/)
Deep Learning by Ian Goodfellow, Yoshua Bengio & Aaron Courville : https://amzn.to/30UMTGl

If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those.

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Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching.

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#neuralnetwork #deeplearning #bestbook

*I use affiliate links on the products that I recommend. These give me a small portion of the sales price at no cost to you. I appreciate the proceeds and they help me to improve my channel!

DISCUSSION MEETING

WORKSHOP ON INVERSE PROBLEMS AND RELATED TOPICS (ONLINE)

ORGANIZERS: Rakesh (University of Delaware, USA) and Venkateswaran P Krishnan (TIFR-CAM, India)

DATE: 25 October 2021 to 29 October 2021

VENUE: Online

This week-long program will consist of several lectures by experts on different types of inverse problems and the underlying basic techniques to understand them. The targeted audience for these lectures are Master's and PhD students, and researchers with a strong background in PDEs. Prior experience of research in inverse problems will not be assumed.

The topics that we cover during this week-long program are:

1. Calderon problem.
2. Fractional Calderon problem.
3. Geometric inverse problems.
4. Integral geometry problems.
5. Inverse problems for Maxwell's equations.
6. Inverse problems involving non-linear equations.
7. Inverse problems for hyperbolic and transport equations.
8. Probabilistic inverse problems.
9. Machine Learning approaches in inverse problems.

CONTACT US: program@icts.res.in
PROGRAM LINK: https://www.icts.res.in/discussion-meeting/ip2021

The progress of AI research will be closely tied to innovations in hardware.
Facebook's Chief AI Scientist Yann LeCun describes how advances in deep learning (DL) research will influence the hardware architecture of the future.
LeCun says the demand for DL-specific hardware will likely only increase. New architectural concepts such as dynamic networks, associative-memory structures, and sparse activations will affect the type of hardware architecture that will be required in the future.

Twitter: @julsimon
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- What is the AWS Deep Learning AMI?
- Running the AWS Deep Learning AMI on a GPU instance
- Training a Deep Learning model on a GPU with the MNIST dataset

I am happy to have read, "Deep Learning with Python" by Francois Chollet. The book is a 5/5 stars! He lays a easy to understand base foundation for the reader while providing and explaining simple code. I really liked the way he started with a simple model and then added more details to it for a better fit while providing testing and logic as to why it was better. The code also follows the same progression where it is short and simple at the beginning and then becomes longer and more detailed as the models become more complicated.

Traditional neural networks, CNN (convolution neural networks), and RNN (recurrent neural networks) are all covered in great detail with real examples. There are many cool topics covered such as generative adversarial networks. The book also does a great job at pointing out how deep learning can be used and what has been over hyped in the media and online.

Buy the book here (my affiliate link):
https://amzn.to/2w3LDUY


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Hello World, Welcome to our channel where we talk about tech and programming and Data Science stuffs.
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#RTXA6000#intel#ai#DeepLearning#yangcom
CPU: Intel Xeon Scalable Gold 6248 (2EA)
M/B: ASUS WS C621E SAGE
RAM: SAMSUNG DDR4-3200 ECC/REG 768GB(64Gx12)
SSD: CORSAIR MP400 M.2 NVMe 8TB
HDD: Seagate IronWolf Pro 20TB(6EA)
VGA: NVIDIA RTX A6000 D6 48GB (4EA)
POWER: SuperFlower SF-2000F14HP LEADEX PLATINUM
CASE: 4U RackMount
COOLER: NOCTUA NH-D9 DX-3647 4U (2EA)


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We are cooking a new surprise for our CODING CHAMPS. 😲
DevTown is being upgraded to SOMETHING NEW, SOMETHING EVEN MORE AWESOME on www.devtown.in 🤩

STAY TUNED! LAUNCHING SOON! 👀

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Le deep learning, une technique qui révolutionne l'intelligence artificielle...et bientôt notre quotidien !

Écrit et réalisé par David Louapre © Science étonnante

Le billet qui accompagne la vidéo : http://wp.me/p11Vwl-23E

* MES LIVRES :
- "Mais qui a attrapé le bison de Higgs ?"
https://www.amazon.fr/gp/product/B07R7BZZ5J/

- "Insoluble, mais vrai !"
https://www.amazon.fr/gp/product/2081486776/

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* LE BLOG :
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La vidéo de Fei Fei Li à TED : https://www.ted.com/talks/fei_....fei_li_how_we_re_tea

La leçon inaugurale de Yann Le Cun au Collège de France : http://www.college-de-france.f....r/site/yann-lecun/in

Références :
==========
Russakovsky, Olga, et al. « Imagenet large scale visual recognition challenge. » International Journal of Computer Vision 115.3 (2015): 211-252. http://arxiv.org/pdf/1409.0575

Radford, Alec, Luke Metz, and Soumith Chintala. « Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. » arXiv preprint arXiv:1511.06434 (2015). http://arxiv.org/pdf/1511.06434

Zeiler, Matthew D., and Rob Fergus. « Visualizing and understanding convolutional networks. » Computer vision–ECCV 2014. Springer International Publishing, 2014. 818-833. http://arxiv.org/pdf/1311.2901

Vinyals, Oriol, et al. "Show and tell: A neural image caption generator." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. http://arxiv.org/pdf/1411.4555.pdf

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
00:29​ - Create a model - Theory
01:46 - Train a model - Theory
02:59 - Create Convolutional Neural Network (alg.)
04:33 - Shuffle labeled sample patches (alg.)
05:27 - Train Convolutional Neural Network (alg.)
06:52 - Save Convolutional Neural Network (alg.)


(⊙_☉)