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.

#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

ImageClassificationDeepLearning

Here 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 #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.

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 Xception

State-of-the-art deep learning image classifiers in Keras

Keras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset: VGG16 VGG19 ResNet50 Inception V3 Xception

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.

Link :https://github.com/saptakbasu7/ImageClassificationDeepLearning 

Humbled!

Humbled... to be nominated for India-International Achievers’ Awards.

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 Keras

Keras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset:

1. VGG16

1. VGG19

1. ResNet50

1. Inception V3

1. Xception

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 np

from keras.preprocessing import image

from keras.applications import inception_v3

# Load pre-trained image recognition model

model = inception_v3.InceptionV3()

# Load the image file and convert it to a numpy array

img = 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 expects

input_image /= 255.

input_image -= 0.5

input_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 them

predicted_classes = inception_v3.decode_predictions(predictions, top=1)

imagenet_id, name, confidence = predicted_classes[0][0]

#Let's print what the DL Program say

print("This is a {} with {:.4}% confidence!".format(name, confidence * 100))

Output: This is a diaper with 95.24% confidence!

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

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 and

YOY Quarterly profit growth  > 20 and

Net Profit latest quarter  > 1 and

G Factor >= 7 and

Net Profit latest quarter > .33 AND

Other income latest quarter < Net Profit latest quarter * .5 AND

Net Profit preceding year quarter <= 0 AND

Expected quarterly net profit > 0 AND

Sales latest quarter > Sales preceding year quarter   AND

Return on invested capital > 25 and

Earnings yield > 15 and

Book value > 0 AND

Market Capitalization > 15

AND

Graham Number > Current price AND

PB X PE <=22.50 AND

PEG Ratio >0 AND

PEG Ratio <1 .5="" and="" o:p="">

Altman Z Score >=2.5 AND

Sales growth 5Years >25 AND

Profit growth 5Years >15 AND

Current ratio >2 AND

Market Capitalization >250 AND

Sales >100  AND

Piotroski score > 7

AND

Dividend yield > 2 AND

Average 5years dividend > 0 AND

Dividend last year > Average 5years dividend AND

Profit after tax > Net Profit last year * .8 AND

Dividend last year > .35 AND

( Profit growth 3Years > 10 OR

Profit growth 5Years > 10 OR

Profit 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 and

YOY Quarterly profit growth  > 40 and

Average return on capital employed 3Years >30 and

Price to Earning <6 o:p="">

OR

Sales growth 10Years > 10 AND

Profit growth 10Years > 12 AND

OPM 10Year > 12 AND

Debt to equity < 0.5 AND

Current ratio > 1.5 AND

Altman Z Score > 3 AND

Average return on equity 10Years > 12 AND

Average return on capital employed 10Years >12 AND

Return on invested capital > 15 AND

Sales last year / Total Capital Employed > 2 AND

Average dividend payout 3years >15

AND

PEG Ratio <1 and="" o:p="">

Sales > 500 AND

Price to Earning < 40 AND

Profit growth > 20 AND

Debt to equity < 0.2 AND

Price to Cash Flow > 5

OR

EPS last year >20 AND

Debt to equity <.1 AND

Average return on capital employed 5Years >35 AND

Market Capitalization >500 AND

OPM 5Year >15

AND

Net Profit latest quarter > Net Profit preceding quarter AND

Net Profit preceding quarter > Net profit 2quarters back AND

Net profit 2quarters back > Net profit 3quarters back

AND

EPS latest quarter > 1.2 * EPS preceding year quarter AND

EPS latest quarter > 0 AND

YOY Quarterly sales growth > 25 AND

EPS last year > EPS preceding year AND

EPS > EPS last year AND

Profit growth 3Years > 25 AND

Return on equity > 17 AND

Down from 52w high < 15 AND

Market Capitalization > 100

AND

Price to Earning >0 and Price to Earning <10 5years="" and="" equity="" growth="" on="" return="">10 and Dividend yield >1 and Return on capital employed >10

AND

Profit growth 5Years > Sales growth 5Years AND

Sales growth 5Years > 3 AND

Return on equity > 15 AND

Working capital 5Years back < 0

AND

Price 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 INDUSTRIES

2)  DCB BANK

3)  KAJARIA CERAMICS

4)  INFOSYS

5)  INDO COUNT INDUSTRIES