The kernel appears to have died. It will restart automatically

Got strange issue on my new setup
No module named 'numpy.core._multiarray_umath'

Solution: Just upgrade numpy

Success formula in our life

1. Insatiable appetite for knowledge
2. Know the basics 
3. Identify the gaps in your knowledge 
4. Constantly update 
5. Expose the linkages 
6. Expose diverse views 
7. Write, argue and debate

Reference: https://www.youtube.com/watch?v=_svETMOY9zY

Hyper parameters with GridSearch

Building a better model involves iteration and tuning these hyperparameters.
Grid search uses cross validation, splitting the training data up into folds for training and testing of each hyperparameter combination. After all hyperparameter variants are trained, the original test data is used to validate the final model.

GridSearchCV takes a dictionary that describes the parameters that should be tried and a model to train. The grid of parameters is defined as a dictionary, where the keys are the parameters and the values are the settings to be tested.

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import Sequential
import time

def dense_model(units, dropout):
    model = Sequential()
    model.add(Dense(units, activation='relu', input_shape=(28, 28,)))
    model.add(Dropout(dropout))
    model.add(Dense(units, activation='relu'))
    model.add(Dropout(dropout))
    model.add(Flatten())
    model.add(Dense(10, activation='softmax'))
    model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
    return model

hyperparameters = {
    'epochs': [1],
    'batch_size': [64],
    'units': [32, 64, 128],
    'dropout': [0.1, 0.2, 0.4]
}

model = KerasClassifier(build_fn=dense_model, verbose=0)
start = time.clock()
grid = GridSearchCV(estimator=model, param_grid=hyperparameters, cv=6, verbose=4)
grid_result = grid.fit(x_train, y_train)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))

y_true, y_pred = np.argmax(y_test, axis=1), grid.predict(x_test)
print()
print(classification_report(y_true, y_pred))
print()
print(time.clock() - start)

How to get current available GPUs in tensorflow?

Working with a GPU can be 20-50x faster!
Reference Link:
https://stackoverflow.com/questions/38559755/how-to-get-current-available-gpus-in-tensorflow

Another Computer Vision application on AWS

URL: http://iamsam.tech/

Technical areas: S3 Bucket, Amazon ML+ Rekognition, boto3 python package

The AI Index 2018 report is out!

My most interesting areas are DL and NLP
http://cdn.aiindex.org/2018/AI%20Index%202018%20Annual%20Report.pdf

Anomaly detection system POC

Got an opportunity to work on time series data and Anomaly detection application on Data center.

This system is capable of monitoring health of servers and provides remediation whenever server is unhealthy. Our Intelligent system uses Machine Learning to detect or forecast Anomalies and warn the Service providers to ensure timely action

· We have designed the Orchestrator in such a way that it monitors those specific services which are added to our database. When any service goes down it raises a ticket, then it will run corresponding remediation solution to resolve the issue. Finally it closes the ticket after it completes the resolution.

· ELK Stack is used to collect the data/stats from specific server for monitoring health, in turn we use machine learning models to predict the anomaly in the data captured for the specific duration.

· In the similar way our machine learning model also forecasts the future potential anomalies based on specified criteria and raise a warning ticket for timely action hence it is capable of preventing upcoming issues
Used Plotly for visualizing the plots.

Below are some of the important screenshots