Saptarshi Basu's Blog, page 5
October 2, 2017
RudraAI VOICE BOT
#VoiceBOT #VoiceAI #VoiceOverShoppingAssistant #VoiceRecognition #MachineLearning #AI #DeepLearning
Complete Video of Voice over Shopping BOT which can place order over Voice command .
October 1, 2017
VOICE AI BOT
#VoiceBOT #VoiceAI #VoiceOverShoppingAssistant #VoiceRecognition #MachineLearning #AI #DeepLearning
Created a Voice over Shopping BOT which can place order over Voice command .
In the 1st level, Rudra could :
1) Understand voice and take appropriate action e.g.
i) Shows the current time when asked the time
ii) Opens up Google Map when asked for a place ( e.g. Where is Taj Mahal? )
iii) Opens up the appropriate Shopping Platform ( e.g. #Amazon or #Myntra or #Jabong etc ) when asked to find the item - like find me a red shirt from Jabong .
iv) Can scan images among multiple images in a folder ( think from a UI perspective that you are uploading an image ) and find the appropriate item from the shopping platform.
In the 2nd Level, I had tried to make Rudra reaching towards the Goal like buying me an item . For example, if I say -Buy me a white Shirt from Myntra , it first opens up the appropriate search , then selects the item , then selects the appropriate size , adding it to shopping bag and placing the order .
Due to security reasons, Myntra couldn't let me move to the final level after multiple testing , which is understandable but Goal accomplished !
Created a Voice over Shopping BOT which can place order over Voice command .
In the 1st level, Rudra could :
1) Understand voice and take appropriate action e.g.
i) Shows the current time when asked the time
ii) Opens up Google Map when asked for a place ( e.g. Where is Taj Mahal? )
iii) Opens up the appropriate Shopping Platform ( e.g. #Amazon or #Myntra or #Jabong etc ) when asked to find the item - like find me a red shirt from Jabong .
iv) Can scan images among multiple images in a folder ( think from a UI perspective that you are uploading an image ) and find the appropriate item from the shopping platform.
In the 2nd Level, I had tried to make Rudra reaching towards the Goal like buying me an item . For example, if I say -Buy me a white Shirt from Myntra , it first opens up the appropriate search , then selects the item , then selects the appropriate size , adding it to shopping bag and placing the order .
Due to security reasons, Myntra couldn't let me move to the final level after multiple testing , which is understandable but Goal accomplished !
September 28, 2017
How to : Your VOICE Based AI
#SpeechRecognition #VoiceBasedAI #DeepLearning #SpeechToText #Abzooba Created a simple Voice based AI Assistant using speech recognition library in python. It does the following : 1) Understands voice 2) Can convert speech to text ( My starting point , as wanted an Assistant who can help me write my stories )
3) Perform simple actions ,Like ?
3A) You ask for the current time and get's it instantly. 3B) Understands the intent or context in the speech and can perform specific action e.g. When I ask Rudra ( Have named my AI after Lord Shiva ) to find the location of a place , it automatically opens Google Map with the particular location , or when I ask to find a similar kind of shirt by providing the picture of a blue shirt , it scans the image and then find similar looking shirt in #Amazon or #Myntra.
Interesting, isn't it?
Pray to Lord Rudra and get started !
Let's look into how I did it.
Some Important Stuff FIRST -
For Speech Recognition, you need Speech Recognition library.
Do a pip install and get it installed.
pyaudio will also be required.
I am using Keras ( which using tensorflow backend) here for further processing .
Google has a great Speech Recognition API. This API converts spoken text (microphone) into written text (Python strings), briefly Speech to Text.
A text-to-speech (TTS) system converts normal language text into speech.
Let's Look into the WorkFlow :
Let's see how it works :
A) I say what TIME is it ....
Good, let's move to more complex stuff !
B) I say to find me a Location like let's say I ask - Where is Abzooba ?
Automatically opens up Chrome browser with Goggle Map location the particular address.
Great , now let's move to more Complex things .
C) I show an Image to Rudra - I have kept the image in a folder as for now but once you create a simple UI along with it you can just upload the image .
I ask Rudra to search for similar item in #Myntra
Rudra scans the image and then automatically opens up Myntra for the possible choices.
3) Perform simple actions ,Like ?
3A) You ask for the current time and get's it instantly. 3B) Understands the intent or context in the speech and can perform specific action e.g. When I ask Rudra ( Have named my AI after Lord Shiva ) to find the location of a place , it automatically opens Google Map with the particular location , or when I ask to find a similar kind of shirt by providing the picture of a blue shirt , it scans the image and then find similar looking shirt in #Amazon or #Myntra.
Interesting, isn't it?
Pray to Lord Rudra and get started !
Let's look into how I did it.
Some Important Stuff FIRST -
For Speech Recognition, you need Speech Recognition library.
Do a pip install and get it installed.
pyaudio will also be required.
I am using Keras ( which using tensorflow backend) here for further processing .
Google has a great Speech Recognition API. This API converts spoken text (microphone) into written text (Python strings), briefly Speech to Text.
A text-to-speech (TTS) system converts normal language text into speech.
Let's Look into the WorkFlow :
Let's see how it works :
A) I say what TIME is it ....
Good, let's move to more complex stuff !B) I say to find me a Location like let's say I ask - Where is Abzooba ?
Automatically opens up Chrome browser with Goggle Map location the particular address.
Great , now let's move to more Complex things .
C) I show an Image to Rudra - I have kept the image in a folder as for now but once you create a simple UI along with it you can just upload the image .
I ask Rudra to search for similar item in #Myntra
Rudra scans the image and then automatically opens up Myntra for the possible choices.
September 26, 2017
Image Classification - Deep Learning
#github project - how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks. #tensorflow #keras #CNN #Neuralnet #INCEPTIONV3 #Machinelearning #DeepLearning
ImageClassificationDeepLearningHere I will show how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks.#TENSORFLOW #KERAS #NN #NEURALNET #INCEPTIONV3 #MACHINELEARNING #DEEPLEARNINGOur brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.
How it helps ?
Let’s say you have a folder wherein Multiple images get uploaded – best example will be like OLX or Quickr which are free Buy & Sell websites. Now, you need to determine if any harmful things ( e.g. Gun etc) are being bought and sold .
SEVERAL PRE-TRAINED NETWORKS :VGG16, VGG19, ResNet50, Inception V3, and XceptionState-of-the-art deep learning image classifiers in KerasKeras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset: VGG16 VGG19 ResNet50 Inception V3 XceptionInception V3The goal of the inception module is to act as a “multi-level feature extractor” by computing 1×1, 3×3, and 5×5 convolutions within the same module of the network — the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network. The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google.
Link :https://github.com/saptakbasu7/ImageClassificationDeepLearning
ImageClassificationDeepLearningHere I will show how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks.#TENSORFLOW #KERAS #NN #NEURALNET #INCEPTIONV3 #MACHINELEARNING #DEEPLEARNINGOur brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.
How it helps ?
Let’s say you have a folder wherein Multiple images get uploaded – best example will be like OLX or Quickr which are free Buy & Sell websites. Now, you need to determine if any harmful things ( e.g. Gun etc) are being bought and sold .
SEVERAL PRE-TRAINED NETWORKS :VGG16, VGG19, ResNet50, Inception V3, and XceptionState-of-the-art deep learning image classifiers in KerasKeras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset: VGG16 VGG19 ResNet50 Inception V3 XceptionInception V3The goal of the inception module is to act as a “multi-level feature extractor” by computing 1×1, 3×3, and 5×5 convolutions within the same module of the network — the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network. The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google.
Link :https://github.com/saptakbasu7/ImageClassificationDeepLearning
September 23, 2017
Humbled!
Humbled... to be nominated for India-International Achievers’ Awards.
September 22, 2017
Let’s first write a simple Image Recognition Model using Inception V3 and Keras
Image Recognition#TENSORFLOW #KERAS #NN #NEURALNET #INCEPTIONV3 #MACHINELEARNING #DEEPLEARNING
Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.
SEVERAL PRE-TRAINED NETWORKS :
- VGG16, VGG19, ResNet50, Inception V3, and Xception
State-of-the-art deep learning image classifiers in KerasKeras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset:VGG16VGG19ResNet50Inception V3Xception
Inception V3
The goal of the inception module is to act as a “multi-level feature extractor” by computing 1×1, 3×3, and 5×5 convolutions within the same module of the network — the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network.The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google.
LET'S WRITE A NICE LITTLE PROGRAM TO CLASSIFY IMAGES
What are we going to Detect?
What does this Image say to a Computer?
Let's check it out :
import numpy as npfrom keras.preprocessing import imagefrom keras.applications import inception_v3
# Load pre-trained image recognition modelmodel = inception_v3.InceptionV3()
# Load the image file and convert it to a numpy arrayimg = image.load_img('../input/Huggies.jpg', target_size=(299, 299))input_image = image.img_to_array(img)
# Scale the image so all pixel intensities are between [-1, 1] as the model expectsinput_image /= 255.input_image -= 0.5input_image *= 2.
# Add a 4th dimension for batch size (as Keras expects)input_image = np.expand_dims(input_image, axis=0)
# Run the image through the neural network
predictions = model.predict(input_image)
# Convert the predictions into text and print thempredicted_classes = inception_v3.decode_predictions(predictions, top=1)imagenet_id, name, confidence = predicted_classes[0][0] #Let's print what the DL Program sayprint("This is a {} with {:.4}% confidence!".format(name, confidence * 100))
Output: This is a diaper with 95.24% confidence!
Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.
SEVERAL PRE-TRAINED NETWORKS :
- VGG16, VGG19, ResNet50, Inception V3, and Xception
State-of-the-art deep learning image classifiers in KerasKeras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset:VGG16VGG19ResNet50Inception V3Xception
Inception V3
The goal of the inception module is to act as a “multi-level feature extractor” by computing 1×1, 3×3, and 5×5 convolutions within the same module of the network — the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network.The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google.
LET'S WRITE A NICE LITTLE PROGRAM TO CLASSIFY IMAGES
What are we going to Detect?
What does this Image say to a Computer?
Let's check it out :
import numpy as npfrom keras.preprocessing import imagefrom keras.applications import inception_v3
# Load pre-trained image recognition modelmodel = inception_v3.InceptionV3()
# Load the image file and convert it to a numpy arrayimg = image.load_img('../input/Huggies.jpg', target_size=(299, 299))input_image = image.img_to_array(img)
# Scale the image so all pixel intensities are between [-1, 1] as the model expectsinput_image /= 255.input_image -= 0.5input_image *= 2.
# Add a 4th dimension for batch size (as Keras expects)input_image = np.expand_dims(input_image, axis=0)
# Run the image through the neural network
predictions = model.predict(input_image)
# Convert the predictions into text and print thempredicted_classes = inception_v3.decode_predictions(predictions, top=1)imagenet_id, name, confidence = predicted_classes[0][0] #Let's print what the DL Program sayprint("This is a {} with {:.4}% confidence!".format(name, confidence * 100))
Output: This is a diaper with 95.24% confidence!
September 19, 2017
HIT PROFIT BY MACHINE LEARNING BASED TRADING
I bought FRESHTROP Fruits at INR 94 on 08th AUGUST 2017 .
WHY ????– a very detailed study on prices across last 10 years or more, using logistic regression and it should that it’s at it lowest trading price.
Today it trades at 143 , today is 19th September , so in around a little more than a month.
Profit per share = 49 Rupees
If you have bought 1000 Shares, ( Investment INR 94000/- Less than a Lakh ) , you have gained around 50,000/- in just 1 month.
Tip – buy now, it will more to 250/- in a period of 1 years or little more .
It’s always make sense to invest with Machine Learning Statistics .
You can follow my blog on investment tips at http://saptak-firsttry.blogspot.in/#StockTips #MachineLearning #LogisticRegression #AlgorithmicTrading
WHY ????– a very detailed study on prices across last 10 years or more, using logistic regression and it should that it’s at it lowest trading price.
Today it trades at 143 , today is 19th September , so in around a little more than a month.
Profit per share = 49 Rupees
If you have bought 1000 Shares, ( Investment INR 94000/- Less than a Lakh ) , you have gained around 50,000/- in just 1 month.
Tip – buy now, it will more to 250/- in a period of 1 years or little more .
It’s always make sense to invest with Machine Learning Statistics .
You can follow my blog on investment tips at http://saptak-firsttry.blogspot.in/#StockTips #MachineLearning #LogisticRegression #AlgorithmicTrading
September 16, 2017
Predictions and Suggestions from a machine learning based Algorithmic trading
#MachineLearning #AlgortihmicTrading #StockMarketAutomatedTrading #LogisticRegression #Boosting
Predictions and Suggestions from a machine learning based Algorithmic trading
An algorithm is a specific set of clearly defined instructions aimed to carry out a task or process.
Algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader. The defined sets of rules are based on timing, price, quantity or any mathematical model. Apart from profit opportunities for the trader, algo-trading makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities.
We can create a Regression formula like below :
The dependent variable is the Return on capital invested and can be run across all stocks.
Error term ei can be boosted using Boosting Algos and thus increasing the prediction accuracy.
Now how to choose your Variables and what can be the ideal STOCK Equation :
YOY Quarterly sales growth > 15 andYOY Quarterly profit growth > 20 andNet Profit latest quarter > 1 andG Factor >= 7 andNet Profit latest quarter > .33 ANDOther income latest quarter < Net Profit latest quarter * .5 ANDNet Profit preceding year quarter <= 0 ANDExpected quarterly net profit > 0 ANDSales latest quarter > Sales preceding year quarter ANDReturn on invested capital > 25 andEarnings yield > 15 andBook value > 0 ANDMarket Capitalization > 15
AND Graham Number > Current price ANDPB X PE <=22.50 ANDPEG Ratio >0 ANDPEG Ratio Altman Z Score >=2.5 ANDSales growth 5Years >25 ANDProfit growth 5Years >15 ANDCurrent ratio >2 ANDMarket Capitalization >250 ANDSales >100 ANDPiotroski score > 7AND Dividend yield > 2 ANDAverage 5years dividend > 0 ANDDividend last year > Average 5years dividend ANDProfit after tax > Net Profit last year * .8 ANDDividend last year > .35 AND( Profit growth 3Years > 10 ORProfit growth 5Years > 10 ORProfit growth 7Years > 10 ) OR(Market Capitalization > 3000) AND(Average return on equity 10Years Years > 20) AND(Debt to equity < 1.5) AND(Interest Coverage Ratio > 2) AND( PEG Ratio <= 1) AND(Profit growth 5Years > 20)
AND YOY Quarterly sales growth > 40 andYOY Quarterly profit growth > 40 andAverage return on capital employed 3Years >30 andPrice to Earning ORSales growth 10Years > 10 ANDProfit growth 10Years > 12 ANDOPM 10Year > 12 ANDDebt to equity < 0.5 ANDCurrent ratio > 1.5 ANDAltman Z Score > 3 ANDAverage return on equity 10Years > 12 ANDAverage return on capital employed 10Years >12 ANDReturn on invested capital > 15 ANDSales last year / Total Capital Employed > 2 ANDAverage dividend payout 3years >15
AND PEG Ratio Sales > 500 ANDPrice to Earning < 40 ANDProfit growth > 20 ANDDebt to equity < 0.2 ANDPrice to Cash Flow > 5
OR
EPS last year >20 ANDDebt to equity <.1 ANDAverage return on capital employed 5Years >35 ANDMarket Capitalization >500 ANDOPM 5Year >15
AND Net Profit latest quarter > Net Profit preceding quarter ANDNet Profit preceding quarter > Net profit 2quarters back ANDNet profit 2quarters back > Net profit 3quarters back
AND
EPS latest quarter > 1.2 * EPS preceding year quarter ANDEPS latest quarter > 0 ANDYOY Quarterly sales growth > 25 ANDEPS last year > EPS preceding year ANDEPS > EPS last year ANDProfit growth 3Years > 25 ANDReturn on equity > 17 ANDDown from 52w high < 15 ANDMarket Capitalization > 100
AND
Price to Earning >0 and Price to Earning 10 and Dividend yield >1 and Return on capital employed >10
ANDProfit growth 5Years > Sales growth 5Years ANDSales growth 5Years > 3 ANDReturn on equity > 15 ANDWorking capital 5Years back < 0ANDPrice to Earning >0 and Return on equity 5years growth > 5 and Dividend yield >0
Note : DEBT reacts inversely to the equation . Term period will be a spread over last 15 to 20 Years.
Now , applying boosting algorithm ( like XGBoost) you can reduce the error coefficients.
Based on the above equation and a little variation choosing a flattened NN( Neural Network ) below stocks can be looked upon for Indian stock market.
1) RELIANCE INDUSTRIES2) DCB BANK3) KAJARIA CERAMICS4) INFOSYS
5) INDO COUNT INDUSTRIES
Predictions and Suggestions from a machine learning based Algorithmic trading
An algorithm is a specific set of clearly defined instructions aimed to carry out a task or process.
Algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader. The defined sets of rules are based on timing, price, quantity or any mathematical model. Apart from profit opportunities for the trader, algo-trading makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities.
We can create a Regression formula like below :
The dependent variable is the Return on capital invested and can be run across all stocks.
Error term ei can be boosted using Boosting Algos and thus increasing the prediction accuracy.
Now how to choose your Variables and what can be the ideal STOCK Equation :
YOY Quarterly sales growth > 15 andYOY Quarterly profit growth > 20 andNet Profit latest quarter > 1 andG Factor >= 7 andNet Profit latest quarter > .33 ANDOther income latest quarter < Net Profit latest quarter * .5 ANDNet Profit preceding year quarter <= 0 ANDExpected quarterly net profit > 0 ANDSales latest quarter > Sales preceding year quarter ANDReturn on invested capital > 25 andEarnings yield > 15 andBook value > 0 ANDMarket Capitalization > 15
AND Graham Number > Current price ANDPB X PE <=22.50 ANDPEG Ratio >0 ANDPEG Ratio Altman Z Score >=2.5 ANDSales growth 5Years >25 ANDProfit growth 5Years >15 ANDCurrent ratio >2 ANDMarket Capitalization >250 ANDSales >100 ANDPiotroski score > 7AND Dividend yield > 2 ANDAverage 5years dividend > 0 ANDDividend last year > Average 5years dividend ANDProfit after tax > Net Profit last year * .8 ANDDividend last year > .35 AND( Profit growth 3Years > 10 ORProfit growth 5Years > 10 ORProfit growth 7Years > 10 ) OR(Market Capitalization > 3000) AND(Average return on equity 10Years Years > 20) AND(Debt to equity < 1.5) AND(Interest Coverage Ratio > 2) AND( PEG Ratio <= 1) AND(Profit growth 5Years > 20)
AND YOY Quarterly sales growth > 40 andYOY Quarterly profit growth > 40 andAverage return on capital employed 3Years >30 andPrice to Earning ORSales growth 10Years > 10 ANDProfit growth 10Years > 12 ANDOPM 10Year > 12 ANDDebt to equity < 0.5 ANDCurrent ratio > 1.5 ANDAltman Z Score > 3 ANDAverage return on equity 10Years > 12 ANDAverage return on capital employed 10Years >12 ANDReturn on invested capital > 15 ANDSales last year / Total Capital Employed > 2 ANDAverage dividend payout 3years >15
AND PEG Ratio Sales > 500 ANDPrice to Earning < 40 ANDProfit growth > 20 ANDDebt to equity < 0.2 ANDPrice to Cash Flow > 5
OR
EPS last year >20 ANDDebt to equity <.1 ANDAverage return on capital employed 5Years >35 ANDMarket Capitalization >500 ANDOPM 5Year >15
AND Net Profit latest quarter > Net Profit preceding quarter ANDNet Profit preceding quarter > Net profit 2quarters back ANDNet profit 2quarters back > Net profit 3quarters back
AND
EPS latest quarter > 1.2 * EPS preceding year quarter ANDEPS latest quarter > 0 ANDYOY Quarterly sales growth > 25 ANDEPS last year > EPS preceding year ANDEPS > EPS last year ANDProfit growth 3Years > 25 ANDReturn on equity > 17 ANDDown from 52w high < 15 ANDMarket Capitalization > 100
AND
Price to Earning >0 and Price to Earning 10 and Dividend yield >1 and Return on capital employed >10
ANDProfit growth 5Years > Sales growth 5Years ANDSales growth 5Years > 3 ANDReturn on equity > 15 ANDWorking capital 5Years back < 0ANDPrice to Earning >0 and Return on equity 5years growth > 5 and Dividend yield >0
Note : DEBT reacts inversely to the equation . Term period will be a spread over last 15 to 20 Years.
Now , applying boosting algorithm ( like XGBoost) you can reduce the error coefficients.
Based on the above equation and a little variation choosing a flattened NN( Neural Network ) below stocks can be looked upon for Indian stock market.
1) RELIANCE INDUSTRIES2) DCB BANK3) KAJARIA CERAMICS4) INFOSYS
5) INDO COUNT INDUSTRIES
VISHWAKARMA PUJA SPECIAL - THE PROCESSION OF ANTS
RUDRA TRILOGY MYTHOLOGY SERIES
VISHWAKARMA PUJA SPECIAL - THE PROCESSION OF ANTS
the story of the procession of ants
( FROM BOOK 1- THE SECRET OF IMMORTAL CODE)
‘We are waiting for your orders, my Lord’ Vasuki asked looking at the approaching Asura army.‘Let them come forward. Close...very close’ Shiva replied, looking at the river flowing below. Vasuki presumed looking at the tridents pointing skyward, must have reached the limits of their patience. Scattered stones spanning the river looked like dead soldiers.
Shiva walked up to the edge of the mountain and stooped down. He first looked eastward then westward, and then the river beneath .The river looked beautiful. Shiva watched a column of dark red ants transport mud pebbles across the edge of the mountain. When He laid a clod of earth in their way, they strained to climb over it instead of skirting it. There’s so much to learn from this tiny creature, their fighting spirit, the greatest Lord thought.
Shiva looked back at Vasuki and smiled. ‘Have you heard of the story of the procession of ants?’‘No my Lord’ Vasuki looked completely perplexed. In this time of great panic and tension, how can the Lord think of a story?‘It is all in our deeds, my dear friend Vasuki. It is by deeds that one merits happiness or anguish, or becomes a master or a villain. It is by deeds that one attains to the rank of a king or brahmin, or of some god, or of an Indra or a Brahma. And through deeds again, one contracts disease, acquires beauty and deformity, or is reborn in the condition of a monster.Let me tell you then’...and Lord Shiva started.
‘Once there was a great warrior. After a long and dreadful war he slew the giant Vrtra that had been couching on the mountains in the limbless shape of a cloud serpent, holding the waters of heaven captive in its belly. The warrior flung his weapon, the thunderbolt into the midst of the ungainly coils; the monster shattered like a stack of withered rushes. The waters burst free and streamed in ribbons across the land, to circulate once more through the body of the world.
This flood is the flood of life and belongs to all.
The warrior was Indra, the king of the Gods.
All the divinities of the heavens were acclaiming him their saviour. Greatly elated in his triumph and in the knowledge of his strength, he summoned Vishwakarma, the god of art and craft, and commanded him to erect such a palace as should befit the unequalled splendour of the king of the gods.
The miraculous genius, Vishwakarma, succeeded in constructing in a single year a shining residence, marvellous with palaces and gardens, lakes and towers. But as the work progressed, the demands of Indra became even more exacting and his unfolding visions vaster. He required additional terraces and pavilions, more ponds, groves, and pleasure grounds. Whenever Indra arrived to appraise the work, he developed vision beyond vision of marvels remaining to be contrived.
Finally Vishwakarma brought to despair, realized that he had to do something .When Vishwakarma secretly resorted to the higher throne and presented his case, Brahma comforted the petitioner. “You will soon be relieved of your burden,” he said. “Go home in peace.”Early next morning a brahmin boy, carrying the staff of a pilgrim, made his appearance at the gate of Indra. The gate-man hurried to the master, and the master hastened to the entrance to welcome in person the auspicious guest. The boy was slender, some ten years old, radiant with the lustre of wisdom.The boy greeted the host with a gentle glance of his dark and brilliant eyes. The king bowed to the holy child and the boy cheerfully gave his blessing. The two retired to the hall of Indra, where the god ceremoniously welcomed his guest with oblations of honey, milk, and fruits, and then asked about the purpose of the boy's visit.The beautiful child replied with a voice that was as deep and soft as the slow thundering of auspicious rain clouds. “O King of Gods, I have heard of the mighty palace you are building, and have come to refer to you the questions in my mind. How many years will it require to complete this rich and extensive residence? What further feats of engineering will Vishwakarma be expected to accomplish? O Highest of the Gods,”-the boy's luminous features moved with a gentle, scarcely perceptible smile- “no Indra before you have ever succeeded in completing such a palace as yours is to be.”
Full of the wine of triumph, the king of the gods was entertained by this mere boy's pretension to have the knowledge of Indras earlier than himself.
With a fatherly smile he put the question. “Tell me, Child! Are they then so very many, the Indras and Vishwakarmas whom you have seen-or at least, whom you have heard of?”The wonderful guest calmly nodded. “Yes, indeed, many have I seen. I knew your father, Kashyapa, the Old Tortoise Man, lord and progenitor of all the creatures of the earth. And I knew your grandfather, Marichi, Beam of Celestial Light, who was the son of Brahma. Marichi was begotten God Brahma’s pure spirit; his only wealth and glory were his sanctity and devotion. Also, I know Brahma, brought forth by Vishnu from the lotus calyx growing from Vishnu's navel. And Vishnu himself-the Supreme Being, supporting Brahma in his creative endeavour-him too I know.When Brahma closes his eyes, a world goes out of being. The life of a Brahma is four hundred and thirty-two thousand years. When he dies, the lotus goes back, and another lotus is formed, and another Brahma. Then think of the galaxies beyond galaxies in infinite space, each a lotus with a Brahma sitting on it opening his eyes, closing his eyes. And Indras? There may be wise men in your court who would volunteer to count the drops of water in the oceans of the world or the grains of sand on the beaches, but no one would count those Brahmas, let alone those Indras.”While the boy was talking, an army of ants had emerged in the hall and paraded across the floor in military style. The boy laughed when he saw them, and Indra's hairs stood on end, “Why do you laugh?”, stammered Indra,“who are you? why you know so much? What are these ants doing here?"“The truth hurts.” answered the boy, “Don't ask unless you really want to know.”DO YOU WANT TO KNOW THE TRUTH???? IF YES, READ RUDRA TRILOGY HERE - http://amzn.in/0X9QeHZ
VISHWAKARMA PUJA SPECIAL - THE PROCESSION OF ANTS
the story of the procession of ants
( FROM BOOK 1- THE SECRET OF IMMORTAL CODE)
‘We are waiting for your orders, my Lord’ Vasuki asked looking at the approaching Asura army.‘Let them come forward. Close...very close’ Shiva replied, looking at the river flowing below. Vasuki presumed looking at the tridents pointing skyward, must have reached the limits of their patience. Scattered stones spanning the river looked like dead soldiers.
Shiva walked up to the edge of the mountain and stooped down. He first looked eastward then westward, and then the river beneath .The river looked beautiful. Shiva watched a column of dark red ants transport mud pebbles across the edge of the mountain. When He laid a clod of earth in their way, they strained to climb over it instead of skirting it. There’s so much to learn from this tiny creature, their fighting spirit, the greatest Lord thought.
Shiva looked back at Vasuki and smiled. ‘Have you heard of the story of the procession of ants?’‘No my Lord’ Vasuki looked completely perplexed. In this time of great panic and tension, how can the Lord think of a story?‘It is all in our deeds, my dear friend Vasuki. It is by deeds that one merits happiness or anguish, or becomes a master or a villain. It is by deeds that one attains to the rank of a king or brahmin, or of some god, or of an Indra or a Brahma. And through deeds again, one contracts disease, acquires beauty and deformity, or is reborn in the condition of a monster.Let me tell you then’...and Lord Shiva started.
‘Once there was a great warrior. After a long and dreadful war he slew the giant Vrtra that had been couching on the mountains in the limbless shape of a cloud serpent, holding the waters of heaven captive in its belly. The warrior flung his weapon, the thunderbolt into the midst of the ungainly coils; the monster shattered like a stack of withered rushes. The waters burst free and streamed in ribbons across the land, to circulate once more through the body of the world.
This flood is the flood of life and belongs to all.
The warrior was Indra, the king of the Gods.
All the divinities of the heavens were acclaiming him their saviour. Greatly elated in his triumph and in the knowledge of his strength, he summoned Vishwakarma, the god of art and craft, and commanded him to erect such a palace as should befit the unequalled splendour of the king of the gods.
The miraculous genius, Vishwakarma, succeeded in constructing in a single year a shining residence, marvellous with palaces and gardens, lakes and towers. But as the work progressed, the demands of Indra became even more exacting and his unfolding visions vaster. He required additional terraces and pavilions, more ponds, groves, and pleasure grounds. Whenever Indra arrived to appraise the work, he developed vision beyond vision of marvels remaining to be contrived.
Finally Vishwakarma brought to despair, realized that he had to do something .When Vishwakarma secretly resorted to the higher throne and presented his case, Brahma comforted the petitioner. “You will soon be relieved of your burden,” he said. “Go home in peace.”Early next morning a brahmin boy, carrying the staff of a pilgrim, made his appearance at the gate of Indra. The gate-man hurried to the master, and the master hastened to the entrance to welcome in person the auspicious guest. The boy was slender, some ten years old, radiant with the lustre of wisdom.The boy greeted the host with a gentle glance of his dark and brilliant eyes. The king bowed to the holy child and the boy cheerfully gave his blessing. The two retired to the hall of Indra, where the god ceremoniously welcomed his guest with oblations of honey, milk, and fruits, and then asked about the purpose of the boy's visit.The beautiful child replied with a voice that was as deep and soft as the slow thundering of auspicious rain clouds. “O King of Gods, I have heard of the mighty palace you are building, and have come to refer to you the questions in my mind. How many years will it require to complete this rich and extensive residence? What further feats of engineering will Vishwakarma be expected to accomplish? O Highest of the Gods,”-the boy's luminous features moved with a gentle, scarcely perceptible smile- “no Indra before you have ever succeeded in completing such a palace as yours is to be.”
Full of the wine of triumph, the king of the gods was entertained by this mere boy's pretension to have the knowledge of Indras earlier than himself.
With a fatherly smile he put the question. “Tell me, Child! Are they then so very many, the Indras and Vishwakarmas whom you have seen-or at least, whom you have heard of?”The wonderful guest calmly nodded. “Yes, indeed, many have I seen. I knew your father, Kashyapa, the Old Tortoise Man, lord and progenitor of all the creatures of the earth. And I knew your grandfather, Marichi, Beam of Celestial Light, who was the son of Brahma. Marichi was begotten God Brahma’s pure spirit; his only wealth and glory were his sanctity and devotion. Also, I know Brahma, brought forth by Vishnu from the lotus calyx growing from Vishnu's navel. And Vishnu himself-the Supreme Being, supporting Brahma in his creative endeavour-him too I know.When Brahma closes his eyes, a world goes out of being. The life of a Brahma is four hundred and thirty-two thousand years. When he dies, the lotus goes back, and another lotus is formed, and another Brahma. Then think of the galaxies beyond galaxies in infinite space, each a lotus with a Brahma sitting on it opening his eyes, closing his eyes. And Indras? There may be wise men in your court who would volunteer to count the drops of water in the oceans of the world or the grains of sand on the beaches, but no one would count those Brahmas, let alone those Indras.”While the boy was talking, an army of ants had emerged in the hall and paraded across the floor in military style. The boy laughed when he saw them, and Indra's hairs stood on end, “Why do you laugh?”, stammered Indra,“who are you? why you know so much? What are these ants doing here?"“The truth hurts.” answered the boy, “Don't ask unless you really want to know.”DO YOU WANT TO KNOW THE TRUTH???? IF YES, READ RUDRA TRILOGY HERE - http://amzn.in/0X9QeHZ
September 14, 2017
Did you know TensorFlow is Life-Saving ? Read- on
#MachineLearning #DeepLearning #ML #AI #ArtificialIntelligence #TensorFlow
Beginner’s guide to Tensorflow
Did you know TensorFlow is Life-Saving ? Read- on
INTRODUCTION:
The primary software tool of Deep Learningis TensorFlow. It is an open source artificial intelligence library, using data flow graphs to build models.
It allows developers to create large-scale neural networks with many layers.
USED FOR:
TensorFlow is mainly used for:
1) Voice/Sound Recognition2) Text Based Applications3) Image Recognition4) Video Detection
INTERESTING FACTS :
Nasa ( National Aeronautics and Space Administration) is designing a system with TensorFlow for orbit classification and object clustering of asteroids. As a result, they can classify and predict NEOs (near earth objects). ( So, in a way TensorFlow is life-saving!!! )
NOW TECHNICAL STUFF :
TensorFlow is a library for numerical computationwhere data flows through the graph.
Data in TensorFlow is represented by n-dimensional arrays called Tensors.
Graph is made of data(Tensors) and mathematical operations. § Nodes on the graph: represent mathematical operations. § Edges on the graph: represent the Tensors that flow between operations.
There is one more aspect in which TensorFlow is very different from any other programming language.
In TensorFlow, you first need to create a blueprint of whatever you want to create. While you are creating the graph, variables don’t have any value. Later when you have created the complete graph, you have to run it inside a session, only then the variables have any values.
import tensorflow as tf
Graph in TensorFlow:
GRAPH is the backbone of TensorFlow and every computation/operation/variables reside on the graph. Everything that happens in the code, resides on a default graph provided by TensorFlow. You can access this graph by:
graph = tf.get_default_graph()
Next big thing, Session!
A GRAPH is used to define operations, but the operations can only run within a SESSION. Graphs and sessions are created independently of each other.
Sess = tf.Session() Tensors in Tensorflow:
TensorFlow holds Data in tensors
i) Constants
are constants whose value can’t be changed. You can declare a constant like this:
a=tf.constant(1.0)
ii) Variables
are again Tensors which are like variables in any other language.
b=tf.Variable(2.0,name=None)iii) PlaceHolders
are tensors which are waiting to be initialized/fed. Placeholders are used for training data which is only fed when the code is actually run inside a session. What is fed to Placeholder is called feed_dict. Feed_dict are key value pairs for holding data:
a = tf.placeholder("float")
Beginner’s guide to Tensorflow
Did you know TensorFlow is Life-Saving ? Read- on
INTRODUCTION:
The primary software tool of Deep Learningis TensorFlow. It is an open source artificial intelligence library, using data flow graphs to build models.
It allows developers to create large-scale neural networks with many layers.
USED FOR:
TensorFlow is mainly used for:
1) Voice/Sound Recognition2) Text Based Applications3) Image Recognition4) Video Detection
INTERESTING FACTS :
Nasa ( National Aeronautics and Space Administration) is designing a system with TensorFlow for orbit classification and object clustering of asteroids. As a result, they can classify and predict NEOs (near earth objects). ( So, in a way TensorFlow is life-saving!!! )
NOW TECHNICAL STUFF :
TensorFlow is a library for numerical computationwhere data flows through the graph.
Data in TensorFlow is represented by n-dimensional arrays called Tensors.
Graph is made of data(Tensors) and mathematical operations. § Nodes on the graph: represent mathematical operations. § Edges on the graph: represent the Tensors that flow between operations.
There is one more aspect in which TensorFlow is very different from any other programming language.
In TensorFlow, you first need to create a blueprint of whatever you want to create. While you are creating the graph, variables don’t have any value. Later when you have created the complete graph, you have to run it inside a session, only then the variables have any values.
import tensorflow as tf
Graph in TensorFlow:
GRAPH is the backbone of TensorFlow and every computation/operation/variables reside on the graph. Everything that happens in the code, resides on a default graph provided by TensorFlow. You can access this graph by:
graph = tf.get_default_graph()
Next big thing, Session!
A GRAPH is used to define operations, but the operations can only run within a SESSION. Graphs and sessions are created independently of each other.
Sess = tf.Session() Tensors in Tensorflow:
TensorFlow holds Data in tensors
i) Constants
are constants whose value can’t be changed. You can declare a constant like this:
a=tf.constant(1.0)
ii) Variables
are again Tensors which are like variables in any other language.
b=tf.Variable(2.0,name=None)iii) PlaceHolders
are tensors which are waiting to be initialized/fed. Placeholders are used for training data which is only fed when the code is actually run inside a session. What is fed to Placeholder is called feed_dict. Feed_dict are key value pairs for holding data:
a = tf.placeholder("float")
