Yantra
Posted by
Manish Panchmatia
on Sunday, September 23, 2018
Labels:
India,
Spiritual Science
/
Comments: (0)
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Yantra (यन्त्र) (Sanskrit) (literally "machine, contraption" is a mystical diagram, mainly from the Tantric traditions of the Indian religions.
They are used for the worship of deities in temples or at home; as an aid in meditation; used for the benefits given by their supposed occult powers based on Hindu astrology and tantric texts. They are also used for adornment of temple floors, due mainly to their aesthetic and symmetric qualities. Specific yantras are traditionally associated with specific deities.
They are used for the worship of deities in temples or at home; as an aid in meditation; used for the benefits given by their supposed occult powers based on Hindu astrology and tantric texts. They are also used for adornment of temple floors, due mainly to their aesthetic and symmetric qualities. Specific yantras are traditionally associated with specific deities.
Yantras are usually associated with a particular deity and are used for specific benefits, such as: for meditation; protection from harmful influences; development of particular powers; attraction of wealth or success, etc.They are often used in daily ritual worship at home or in temples, and sometimes worn as a talisman.
As an aid to meditation, yantras represent the deity that is the object of meditation. These yantras emanate from the central point, the bindu. The yantra typically has several geometric shapes radiating concentrically from the center, including triangles, circles, hexagons, octagons, and symbolic lotus petals. The outside often includes a square representing the four cardinal directions, with doors to each of them
Yantras can be on a flat surface or three dimensional. Yantras can be drawn or painted on paper, engraved on metal, or any flat surface. They tend to be smaller in size than the similar mandala, and traditionally use less color than mandalas.
Occult yantras are used as good luck charms, to ward off evil, as preventative medicine, in exorcism, etc., by using their magical power. When used as a talisman, the yantra is seen to represent a deity who can be called on at will by the user. They are traditionally consecrated and energized by a priest, including the use of mantras which are closely associated to the specific deity and yantra. Practitioners believe that a yantra that is not energized with mantra is lifeless
A yantra comprises geometric shapes, images, and written mantra. Triangles and hexagrams are common, as are circles and lotuses of 4 to 1,000 petals.
Reference : https://en.wikipedia.org/wiki/Yantra
Image Courtesy : Desktop calendar of year 2017 by Ganesha Housing Corporation Limited
AI/ML for SMB
Posted by
Manish Panchmatia
on Saturday, September 22, 2018
Labels:
ArtificialIntelligence,
Innovation,
MachineLearning,
Management,
software
/
Comments: (0)
Full article...>>
1. Salesforce : Einstein AI Intelligent CRM
2. MailChimp : Email
marketing
3. Clare.AIClare.AI is
an example of a plug and play app for (1) online banking services,
and (2) to handle a larger volume of customer inquiries without hiring more
staff.
4. Malaysian company, Supahands for
upwork like service to select crowd sourcing v/s freelancer
5.
smart speaker or home robot can be used at retail outlet
6. Digital Genius : Intelligent customer service
query. The AI platform for customer service
7. Acquisio : online ads on Facebook, Bing, Adwords
8. Darktrace : Data security. Enterprise Immune System technology for cyber security.
9. Crayon : Competitor analysis
10. Aivo : chat bot
12. Datorama: end to end marketing integration platform
13. VoxPro multilingual customer experience and
technical support solutions
14. bold360 Better customer engagement
15.
call center
16.
SAP CoPilot: Digital Assistant in the Enterprise
18. AISense: Call, Meeting Transcriptions
19. WalkMe: AI for Software Training. Integrated with Athenahealth,
Salesforce, Workday etc.
20.
Workday: Finance and HR
21.
Athenahealth uses WalkMe to train
doctor, nurse about how to use system
22.
ServiceChannel for restaurants
23.
Niles for conversation
24. MESH, Not Rocket Science:
Branded Bots
25.
Acculation Social Media content
decision.
Reference:
1. https://www.salesforce.com/blog/2016/09/introducing-salesforce-einstein.html
2. https://www.cmswire.com/information-management/6-ways-artificial-intelligence-will-impact-the-future-workplace/
5. https://www.cmswire.com/digital-workplace/8-examples-of-artificial-intelligence-ai-in-the-workplace/
Node.js
What is Node.js?
Node.js is JavaScript runtime built on Chrome’s V8 JavaScript engine.
- Event Driven
- Non-Blocking
- Single thread
For Details
Read: https://nodesource.com/blog/understanding-the-nodejs-event-loop/
Watch: https://www.youtube.com/watch?v=8aGhZQkoFbQ
Its package eco system is npm : https://www.npmjs.com/ It contains over 400k public library.
How to install Node.js Refer : https://github.com/creationix/nvm
Why one should user Node.js ? https://blog.hellotangible.com/10-reasons-to-use-node-js-b74ed06bb366
Node.js core API : https://nodejs.org/api/
Hapi HTTP server https://hapijs.com/
Getting Started with NPM https://docs.npmjs.com/getting-started/what-is-npm
Ref: https://blog.hellotangible.com/an-absolute-beginners-guide-to-node-js-c27e6d108b5c
Node.js is JavaScript runtime built on Chrome’s V8 JavaScript engine.
- Event Driven
- Non-Blocking
- Single thread
For Details
Read: https://nodesource.com/blog/understanding-the-nodejs-event-loop/
Watch: https://www.youtube.com/watch?v=8aGhZQkoFbQ
Its package eco system is npm : https://www.npmjs.com/ It contains over 400k public library.
How to install Node.js Refer : https://github.com/creationix/nvm
Why one should user Node.js ? https://blog.hellotangible.com/10-reasons-to-use-node-js-b74ed06bb366
Node.js core API : https://nodejs.org/api/
Hapi HTTP server https://hapijs.com/
Getting Started with NPM https://docs.npmjs.com/getting-started/what-is-npm
Ref: https://blog.hellotangible.com/an-absolute-beginners-guide-to-node-js-c27e6d108b5c
Managing Storage with Containers
https://www.meetup.com/Docker-Bangalore/events/253542738/
Flex Volume Drivers in Kubernetes and CSI, Peeyush Gupta, IBM
=============================================================
Storage in container
- stateless / stateful
- volumes
- dynamic provisioning
- PVC, PV and Storage Class
* Storage Class refers to dynamic provisioning
* PVC refers to Storage Class
* POD refers to PVC for volume.
Kubelets running on host, request Flex Driver. Flex Driver implements vendor specific APIs for storage/volume : 1. Mount 2. Unmount 3. attach 4. detach.
This binary need to be placed(copied) at specific path for each POD.
For CNI also cadico driver need to be placed(copied) at specific path for each POD. The better alternative is CSI = Container Storage Interface.
CO = Container Orachstrator. Example: Kubernetes (K8S), MESOS, Cloud Foundray, OpenShift (by RedHat).
CO has 1. node 2. controller 3. identity
There is a single binary for node and controller. Based on Identity either node or controller role can be played.
1. Indemotent APIs
2. Sidecar container
2.1 Driver register (Identity Service)
2.2 Extended Provisoner (watch create volume)
2.3 Extended Attacher (watch attach)
2.4 Liveness Probe
2.5 Extended snapshotter (started in mid July 2018)
Containerized Gluster Storage in Kubernetes - Saravanakumar, Red Hat
====================================================================
GlusterFS was born by oil industry. Oil industry need to process data from different hosts to detect presence of oil. Now it is more than 10 years old.
Steps (all steps as sudo)
1. install and start glusterd service on all host.
2. gluster peer status
3. gluster volume create
4. gluster volume start
This will start gluster on all host.
5. gluster volume status
6. mount -t glusterfs
PVC access mode
1. ROX: Read only by many nodes
2. RWO: Read/Write by single node
3. RWX: Read/Write by many nodes
Heketi provides a RESTful management interface which can be used to manage the life cycle of GlusterFS volumes
Storage requirements for running Spark workloads on Kubernetes, Rachit Arora
============================================================================
Spark core engine runs over 1. Yarn, 2. MESOS, 3. Standalone Schedular, 4. K8S
1. Spark SQL 2. Spark Streaming 3. Spark Machine Learning Lib 4. GraphX runs over Spark Core Engine
* Data engineer 1. Ingest and store data from multiple source 2. Prepare Data.
* Data Scientis 2. Prepare Data 3. Analysze Data build model
* Application Developer 4. Visulize Data
Now new trend is serverless analytics.
'Spark over K8S' provides Jupyter-Kernel gateway for data scientist to analyze data
Distributed FS
1. NFS and BigNFS
2. HDFS
3. DBFS (Data Briks FS)
4. S3 / Object Storage
5. Portworx
6. GlusterFS
URLs:
datascience.ibm.com
www.ibm.com/analytics/us/en/watson-data-platform/tutorial
Tweeter Handle : @k8sBLR
Flex Volume Drivers in Kubernetes and CSI, Peeyush Gupta, IBM
=============================================================
Storage in container
- stateless / stateful
- volumes
- dynamic provisioning
- PVC, PV and Storage Class
* Storage Class refers to dynamic provisioning
* PVC refers to Storage Class
* POD refers to PVC for volume.
Kubelets running on host, request Flex Driver. Flex Driver implements vendor specific APIs for storage/volume : 1. Mount 2. Unmount 3. attach 4. detach.
This binary need to be placed(copied) at specific path for each POD.
For CNI also cadico driver need to be placed(copied) at specific path for each POD. The better alternative is CSI = Container Storage Interface.
CO = Container Orachstrator. Example: Kubernetes (K8S), MESOS, Cloud Foundray, OpenShift (by RedHat).
CO has 1. node 2. controller 3. identity
There is a single binary for node and controller. Based on Identity either node or controller role can be played.
1. Indemotent APIs
2. Sidecar container
2.1 Driver register (Identity Service)
2.2 Extended Provisoner (watch create volume)
2.3 Extended Attacher (watch attach)
2.4 Liveness Probe
2.5 Extended snapshotter (started in mid July 2018)
Containerized Gluster Storage in Kubernetes - Saravanakumar, Red Hat
====================================================================
GlusterFS was born by oil industry. Oil industry need to process data from different hosts to detect presence of oil. Now it is more than 10 years old.
Steps (all steps as sudo)
1. install and start glusterd service on all host.
2. gluster peer status
3. gluster volume create
4. gluster volume start
This will start gluster on all host.
5. gluster volume status
6. mount -t glusterfs
PVC access mode
1. ROX: Read only by many nodes
2. RWO: Read/Write by single node
3. RWX: Read/Write by many nodes
Heketi provides a RESTful management interface which can be used to manage the life cycle of GlusterFS volumes
Storage requirements for running Spark workloads on Kubernetes, Rachit Arora
============================================================================
Spark core engine runs over 1. Yarn, 2. MESOS, 3. Standalone Schedular, 4. K8S
1. Spark SQL 2. Spark Streaming 3. Spark Machine Learning Lib 4. GraphX runs over Spark Core Engine
* Data engineer 1. Ingest and store data from multiple source 2. Prepare Data.
* Data Scientis 2. Prepare Data 3. Analysze Data build model
* Application Developer 4. Visulize Data
Now new trend is serverless analytics.
'Spark over K8S' provides Jupyter-Kernel gateway for data scientist to analyze data
Distributed FS
1. NFS and BigNFS
2. HDFS
3. DBFS (Data Briks FS)
4. S3 / Object Storage
5. Portworx
6. GlusterFS
URLs:
datascience.ibm.com
www.ibm.com/analytics/us/en/watson-data-platform/tutorial
Tweeter Handle : @k8sBLR
MLCC
Posted by
Manish Panchmatia
on Saturday, August 18, 2018
Labels:
ArtificialIntelligence,
Bangalore,
Education,
Innovation,
MachineLearning,
Meetup
/
Comments: (0)
Full article...>>
Let me share key take away points from meetup event "Google Machine Learning Study Jam"
In Feb/March 2018, Google announced MLCC Machine Learning crash course In July 2018, MLCC Study Jam series comes to India. Click here and click here to know more. I attended one such event with my friend, by Industry 5.0 meetup.
Here are few useful links
TensorFlow Content Bundle, Spring 2018
Gradients and Partial Derivatives : YouTube Video
Later on, I found the Maths play list is good. All videos by Eugene Khutoryansky are excellent
Another YouTube video by Christopher Gondek. Here also, the playlist about 'Machine Learning Visualization' is good.
In Feb/March 2018, Google announced MLCC Machine Learning crash course In July 2018, MLCC Study Jam series comes to India. Click here and click here to know more. I attended one such event with my friend, by Industry 5.0 meetup.
Here are few useful links
TensorFlow Content Bundle, Spring 2018
Gradients and Partial Derivatives : YouTube Video
Later on, I found the Maths play list is good. All videos by Eugene Khutoryansky are excellent
Another YouTube video by Christopher Gondek. Here also, the playlist about 'Machine Learning Visualization' is good.
Microsoft announced about FPGA based Edge Computing : Brainwave project
AutoML and transfer learning. At present, they are at nascent stage. Once let it fully evolved then we may not need people who know AI/ML. The machine themselves will learn. I did little Googling and found few links : http://www.ml4aad.org/automl/ and https://automl.info/
As per my knowledge, after completing any Machine Learning course till one completely switch his/her career path, Kaggle is the only platform to get hands-on experience. I came to know one more such platform Seedbank I found one seed about 'Piano Transcription' quite interesting. We discussed with Sanjay Chitnis about creating similar seed to recognize Indian Raga
There is an interesting book 'Pattern Recognition and Machine Learning (Information Science and Statistics)' by Chrisopher M. Bishop
http://playground.tensorflow.org is an excellent, browser based Neural network tool. It is also used as part of MLCC We discussed about L1 regularization, L2 regularization, confusion matrix, precision, accuracy, recall, F1 score, Receiver operating characteristic etc. Precision is all about how many positive case, the algorithm could able to detect out of all positive cases. Recall is about how good is the diagnostic test?
CNN is combination of filter and dimension reduction. RNN is a special case of LSTM. GAN is widely used to creation. The GAN Zoo has list of all variations of GAN
We also discussed about Semi-Supervised Learning , Topic Learning OR Keyword Learning, that is beyond supervised learning, Gold standard etc.
At the end, Sanjay drew out attention to an interesting trend that now, product cost is keep reducing. Features in products are keep increasing. Service cost is keep increasing ! India has lots of data available. There is good scope of data analytics and machine learning for General Election 2019 at India.

















