BRN Discussion Ongoing
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Proga
Regular
We haven't seen any revenue for those licencing agreements yet so they probably be in the next 4c if they've paid. It's not unusual for companies to take 60-90 days to pay invoices.
thelittleshort
Top Bloke
Just saw this on LinkedIn
Free online machine learning course from Stanford taught by Andrew Ng
www.coursera.org
Approximately 3 months to complete
Suggested pace of 9 hours/week
English
Subtitles: English
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrewโs pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If youโre looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
โข Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
โข Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
โข Build and train a neural network with TensorFlow to perform multi-class classification.
โข Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
โข Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
โข Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
โข Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
โข Build a deep reinforcement learning model.
Free online machine learning course from Stanford taught by Andrew Ng
Machine Learning
Offered by Stanford University and DeepLearning.AI. #BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and ... Enroll for free.
www.coursera.org
Approximately 3 months to complete
Suggested pace of 9 hours/week
English
Subtitles: English
WHAT YOU WILL LEARN
- Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
- Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
- Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
- Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model
About this Specialization
431,767 recent viewsThe Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated version of Andrewโs pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If youโre looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Applied Learning Project
By the end of this Specialization, you will be ready to:โข Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
โข Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
โข Build and train a neural network with TensorFlow to perform multi-class classification.
โข Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
โข Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
โข Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
โข Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
โข Build a deep reinforcement learning model.
Proga
Regular
@Bravo, @Diogenese pointed out 8 Gen 2 have some low end SNN capabilities."The choice is up to the partners"...interesting. Qualcomm is leaving the partners or the individual phone makers in this case, to decide what capabilities they want in the phones. I wonder if this could mean they're potentially leaving the door open to their partners to choose to implement Akida for additional AI capabilities at the edge.
It's not beyond the realms of possibilities it it?
View attachment 22741
View attachment 22742
First Listen: Taking Qualcomm's Snapdragon Sound for a Spin
Qualcomm hopes to bring a handful of new experiences to next year's Bluetooth headphones and speakers, including dynamic spatial audio and lossless playback. We gave the upgrades a quick listen. Here are our thoughts.au.pcmag.com
buena suerte :-)
BOB Bank of Brainchip
Awesome ... And they rounded us out @ 70c very nice
alwaysgreen
Top 20
The half yearly report will be more interesting imo. It's not a cash report but shows all revenue generated in the last 6 months.
Fingers crossed for $10 mill plus.
Violin1
Regular
Totally agree FF. The company is moving forward but the trajectory not what we anticipated a year ago. Understandable and we should recognise and accept this publicly between now and release of the next 4c or we'll play into the hands of the dickheads once again. Seriously - we'll get there - but pleeeease don't roll-the-dice on this quarter's revenue. Everything is heading the right direction. I hate to say it again but listen to Master Po!!!
Patience grasshopper.....patience.....
Hi FMF,
Can you please give me a link to this quote??
โAt the very low end, we have added an ARM M33 MCU and spiking neural network with BrainChip core licensed for selected applications โ we have licensed what we need to license from BrainChip including the software to get the ball rolling.
Thanks heaps,
Iseki
Fullmoonfever
Top 20
About half way down.
ARM battles RISC-V at Renesas
Dr. Sailesh Chittipeddi, Executive Vice President and General Manager of IoT and Infrastructure Business Unit of Renesas Electronics talks to eeNews Europe
www.eenewseurope.com
Brilliant. Thanks so much!About half way down.
ARM battles RISC-V at Renesas
Dr. Sailesh Chittipeddi, Executive Vice President and General Manager of IoT and Infrastructure Business Unit of Renesas Electronics talks to eeNews Europe
I have previously looked and looked thru Renesas product catalogue to see where this chip is.
The whole computer chip business seems to be running to a totally new paradigm. Gone are the days you'd but some CPU and add some other microcontrollers all off the shelf. Now it seems to be all about hiring the designers from renesas, arm, or megachips or sifive and they'll design something uniquely for you.
Fullmoonfever
Top 20
No probs.
Someone posted the article when it first came out so occasionally I have a scrounge around for M4, M4f and M33 controllers to see what might turn up.
The RA series has potential as does the V2L I posted about in the Renesas thread a while back...imo.
If you have a look at the Edge Impulse public project pages I posted the other day, the dropdown menu for device targets also has a list of devices which I presume are compatible with the Akida NN as they put it.
These include a Texas Instrument, ST Micro, Sony Spresense amongst the usual suspects Arduino, Raspberry and others.
wilzy123
Founding Member
You used too many words. Readership does not contain enough neurons to compute.
TechGirl
Founding Member
hmmmm 
www.prophesee.ai
Researchers at the DLR Institute of Propulsion Technology of the German Aerospace Center explore the possibilities of harnessing event-based vision (EBV) for capturing flow fields in both air and water flows. Using a laser or other light source for illumination, the event-camera captures the motion of small (micrometer) sized particles carried with the flow.
Maps of the evolving flow field can then be obtained from the event tracks produced by particles. Compared to previous studies, a much higher number of particles can be tracked simultaneously, resulting in highly resolved velocity maps.
This has been made possible through recent advances in EBV hardware development featuring high-speed, high resolution systems such as the EVK4 HD by Prophesee.
Event data produced by small particles in a turbulent water flow
Flow velocity measurement
Within the ongoing study a variety of aspects from event-capture to algorithms capable of recovering flow velocity information are being explored. So far flow measurements of up to several meters per second have been demonstrated on a field of view of 100 mm width.
The high temporal resolution of the event data provides velocity information nearly equivalent to imagery obtained with costly high-speed cameras operating beyond 1000 frames per second.
The hardware demands for event-based flow velocimetry are minimal: it only requires an event camera and a small laser with light sheet forming lenses to capture the motion of small particles moving with the flow. This makes the system compact and very portable.
The โtable-topโ setup on the right was used to record the event data and flow fields above. Here, the water is โseededโ with small (10 ยตm) particles that scatter light from a thin (1 mm) laser sheet, that is introduced from the side.
Owing to the high sensitivity of the event camera, a laser power of less than 1 watt is already sufficient to capture the event imagery shown above.
Simple โtable-topโ setup for EBV imaging of a water flow involving a small water tank, laser light sheet and Prophesee event-based camera, here EVK 2 HD
Measurements close to surfacesMotion of individual particles can be efficiently reconstructedpossible by suppressing laser scatterusing the underlying flow field as guidanceBy synchronizing between different unitsA low-cost laser or LED light source can be usedModerate laser power is sufficient to imageMulti-view imagingReconstruction of particle paths in time and 2d spaceinstead of commonly used high-power lasersmicrometer-sized particles in water and airis readily extended to time-resolved 3d particle tracking
Event-based vision has many advantages for fluid flow measurement.
WATCH THE WEBINAR
The Institute of Propulsion Technology is strictly focused on the improvement of gas turbines in aviation and electric power generation by medium- to long-term exploitation of the inherent technical potentials. It is a part of DLR which is the Federal Republic of Germanyโs research centre for aeronautics and space, conducting research and development activities in the fields of aeronautics, space, energy, transport, security and digitalization. At the DLR Institute of Propulsion Technology the department of Engine Measurement Systems is devoted to the development and application of laser optical measurement and imaging techniques for the aerothermodynamic investigation of aero engine components and related fields. The development and adaption of new measurement techniques contributes to the desired progress in turbomachinery research, with their application both at the test rigs of the Institute as well as on the facilities of external customers.
High efficiency real-time flow visualization with event-based vision
Researchers at the DLR Institute of Propulsion Technology are exploring the possibilities of harnessing the potentials of event-based vision for fluid flow measurement.
www.prophesee.ai
EVENT-BASED VISION ENABLES HIGH EFFICIENCY REAL-TIME FLOW VISUALIZATION
Real-time flow visualization High resolution flow field data resolution of tracked particlesResearchers at the DLR Institute of Propulsion Technology of the German Aerospace Center explore the possibilities of harnessing event-based vision (EBV) for capturing flow fields in both air and water flows. Using a laser or other light source for illumination, the event-camera captures the motion of small (micrometer) sized particles carried with the flow.
Maps of the evolving flow field can then be obtained from the event tracks produced by particles. Compared to previous studies, a much higher number of particles can be tracked simultaneously, resulting in highly resolved velocity maps.
This has been made possible through recent advances in EBV hardware development featuring high-speed, high resolution systems such as the EVK4 HD by Prophesee.
Event data produced by small particles in a turbulent water flow
(The field of view is about 55 mm wide and 30 mm high)
Turbulent flow in a water tank reconstructed from captured event data
Flow velocity measurement
Within the ongoing study a variety of aspects from event-capture to algorithms capable of recovering flow velocity information are being explored. So far flow measurements of up to several meters per second have been demonstrated on a field of view of 100 mm width.
The high temporal resolution of the event data provides velocity information nearly equivalent to imagery obtained with costly high-speed cameras operating beyond 1000 frames per second.
The hardware demands for event-based flow velocimetry are minimal: it only requires an event camera and a small laser with light sheet forming lenses to capture the motion of small particles moving with the flow. This makes the system compact and very portable.
The โtable-topโ setup on the right was used to record the event data and flow fields above. Here, the water is โseededโ with small (10 ยตm) particles that scatter light from a thin (1 mm) laser sheet, that is introduced from the side.
Owing to the high sensitivity of the event camera, a laser power of less than 1 watt is already sufficient to capture the event imagery shown above.
Simple โtable-topโ setup for EBV imaging of a water flow involving a small water tank, laser light sheet and Prophesee event-based camera, here EVK 2 HD
Measurements close to surfacesMotion of individual particles can be efficiently reconstructedpossible by suppressing laser scatterusing the underlying flow field as guidanceBy synchronizing between different unitsA low-cost laser or LED light source can be usedModerate laser power is sufficient to imageMulti-view imagingReconstruction of particle paths in time and 2d spaceinstead of commonly used high-power lasersmicrometer-sized particles in water and airis readily extended to time-resolved 3d particle tracking
Event-based vision has many advantages for fluid flow measurement.
- It is suitable for real-time flow visualization โ The event tracks produced by the movement of the particle field directly visualize the flow field without the need of further processing.
- Event-records obtained from fast moving flows can readily be played back in โslow-motionโ mode to visualize the flow dynamics.
- Event cameras are tolerant against intensity variations within the light sheet because its sensors only react on intensity changes.
- They are also tolerant against laser flare (light scatter on surfaces) and non-uniform background intensity. Their tolerance against high background intensity allows particles to be tracked in bright environments.
- Particle event tracks are clearly distinguishable from the background and can be extracted either by feature detection or by using the clusters formed in the motion-compensation analysis.
WATCH THE WEBINAR
ABOUT DLR INSTITUTE OF PROPULSION TECHNOLOGY
The Institute of Propulsion Technology is strictly focused on the improvement of gas turbines in aviation and electric power generation by medium- to long-term exploitation of the inherent technical potentials. It is a part of DLR which is the Federal Republic of Germanyโs research centre for aeronautics and space, conducting research and development activities in the fields of aeronautics, space, energy, transport, security and digitalization. At the DLR Institute of Propulsion Technology the department of Engine Measurement Systems is devoted to the development and application of laser optical measurement and imaging techniques for the aerothermodynamic investigation of aero engine components and related fields. The development and adaption of new measurement techniques contributes to the desired progress in turbomachinery research, with their application both at the test rigs of the Institute as well as on the facilities of external customers.
David holland
Regular
Means Absolutely nothing
They will need to try backpropagation.
Adam82
Member
Yes great video to watch!!
Learning
Learning to the Top ๐ตโโ๏ธ
Another million people know about Akida.
www.linkedin.com
Learning
#visioneqxx #tecdays #efficiency #leadinelectric #leadincarsoftware | Markus Schรคfer
Itโs official! Our #VISIONEQXX is a huge hit in China! โ๐๐ฉ๐ฆ ๐ธ๐ข๐ต๐ฆ๐ณ๐ด๐ฉ๐ฆ๐ฅ ๐ฐ๐ง ๐ฆ๐ญ๐ฆ๐ค๐ต๐ณ๐ช๐ค ๐ท๐ฆ๐ฉ๐ช๐ค๐ญ๐ฆ ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ฐ๐ญ๐ฐ๐จ๐บโ โ๐๐ฒ๐ถ๐ช๐ฑ๐ฑ๐ฆ๐ฅ ๐ธ๐ช๐ต๐ฉ ๐ต๐ฐ๐ฑ-๐ฆ๐ฏ๐ฅ ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ฐ๐ญ๐ฐ๐จ๐ช๐ฆ๐ด ๐ต๐ฉ๐ข๐ต ๐ข๐ฑ๐ฑ๐ณ๐ฐ๐ข๐ค๐ฉ ๐ต๐ฉ๐ฆ ๐ค๐ฆ๐ช๐ญ๐ช๐ฏ๐จ ๐ฐ๐ง ๐ฆ๐ฏ๐ฆ๐ณ๐จ๐บ ๐ฆ๐ง๐ง๐ช๐ค๐ช๐ฆ๐ฏ๐ค๐บโ โ๐๐ฉ๐ช๐ด ๐ช๐ด๐ฏโ๐ต ๐ฆ๐น๐ค๐ฆ๐ฆ๐ฅ๐ช๐ฏ๐จ ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ต๐ณ๐ข๐ฅ๐ช๐ต๐ช๐ฐ๐ฏ๐ข๐ญ ๐ด๐ฆ๐ฏ๐ด๐ฆ, ๐ฃ๐ถ๐ต ๐ค๐ณ๐ถ๐ด๐ฉ๐ช๐ฏ๐จ ๐ธ๐ช๐ต๐ฉ ๐จ๐ณ๐ฆ๐ข๐ต ๐ด๐ถ๐ฑ๐ฆ๐ณ๐ช๐ฐ๐ณ๐ช๐ต๐บโ Just a taste of the...
Learning
Proga
Regular
Mercedes-Benz are having sales problems in China. So are Tesla. All those lock-downs can't have helped.Another million people know about Akida.
View attachment 22757
View attachment 22759
#visioneqxx #tecdays #efficiency #leadinelectric #leadincarsoftware | Markus Schรคfer
Itโs official! Our #VISIONEQXX is a huge hit in China! โ๐๐ฉ๐ฆ ๐ธ๐ข๐ต๐ฆ๐ณ๐ด๐ฉ๐ฆ๐ฅ ๐ฐ๐ง ๐ฆ๐ญ๐ฆ๐ค๐ต๐ณ๐ช๐ค ๐ท๐ฆ๐ฉ๐ช๐ค๐ญ๐ฆ ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ฐ๐ญ๐ฐ๐จ๐บโ โ๐๐ฒ๐ถ๐ช๐ฑ๐ฑ๐ฆ๐ฅ ๐ธ๐ช๐ต๐ฉ ๐ต๐ฐ๐ฑ-๐ฆ๐ฏ๐ฅ ๐ต๐ฆ๐ค๐ฉ๐ฏ๐ฐ๐ญ๐ฐ๐จ๐ช๐ฆ๐ด ๐ต๐ฉ๐ข๐ต ๐ข๐ฑ๐ฑ๐ณ๐ฐ๐ข๐ค๐ฉ ๐ต๐ฉ๐ฆ ๐ค๐ฆ๐ช๐ญ๐ช๐ฏ๐จ ๐ฐ๐ง ๐ฆ๐ฏ๐ฆ๐ณ๐จ๐บ ๐ฆ๐ง๐ง๐ช๐ค๐ช๐ฆ๐ฏ๐ค๐บโ โ๐๐ฉ๐ช๐ด ๐ช๐ด๐ฏโ๐ต ๐ฆ๐น๐ค๐ฆ๐ฆ๐ฅ๐ช๐ฏ๐จ ๐ช๐ฏ ๐ต๐ฉ๐ฆ ๐ต๐ณ๐ข๐ฅ๐ช๐ต๐ช๐ฐ๐ฏ๐ข๐ญ ๐ด๐ฆ๐ฏ๐ด๐ฆ, ๐ฃ๐ถ๐ต ๐ค๐ณ๐ถ๐ด๐ฉ๐ช๐ฏ๐จ ๐ธ๐ช๐ต๐ฉ ๐จ๐ณ๐ฆ๐ข๐ต ๐ด๐ถ๐ฑ๐ฆ๐ณ๐ช๐ฐ๐ณ๐ช๐ต๐บโ Just a taste of the...
Learning
Mercedes-Benz has cut prices on its EQE and EQS electric luxury sedans in China by as much as $33,000 in a bid to boost sales. Strong competition from domestic and foreign automakers in the world's largest EV market has impacted sales of the EQE and EQS sedan, with the automaker announcing price cuts on its website on November 15.
Mercedes Cuts EQE And EQS Sedan Prices In China By As Much As $33K
Mercedes-Benz only sold approximately 8,800 EVs in China from January through July 2022, including less expensive EQA, EQB and EQC models.
insideevs.com
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