Deep Learning with CNN (convoltuional neural network)

 

STEP 1: Understanding CNN

 

STEP 2: Software implimentations of CNN - e.g. TensorFlow

• is a low-level library for CNN that is supported by Google

• offers distributed computing.

• tensorflow official site

• tutorial site

• intall instructions

 

Programming Options - e.g. Python

History of Python as language used by some Machine Learning communities    NumPy package = numerical analysis like Matlab matplotlib package= plotting functionality from Matlab was ported to Python SciPy package = Libraries for scientific computing were built around NumPy and matplotlib Python's support for Matlab-like array manipulation and plotting is a major reason to prefer it over Perl and Ruby.

why not nodejs? Node.js is very well-suited for high I/O web applications, due to it's single-threaded asynchronous design. I myself am a huge fan of Node.js, but I know that it doesn't work well for every kind of application, particularly ones that require lots of computation, such as the case of ML.

C++ or Java ---yes you can but, lately companies like Google (TensorFlow) while having interfaces in Java are primarily supporting Python first

 

Front End Wrapper: Keras library with backend of either TensorFlow or Theano

• example Python program using Keras

  from keras.models import Sequential
  from keras.layers import Dense, Activation
  
  # Simple feed-forward architecture
  model = Sequential()
  
  model.add(Dense(output_dim=64, input_dim=100))
  model.add(Activation("relu"))
  model.add(Dense(output_dim=10))
  model.add(Activation("softmax"))
  
  # Optimize with SGD (learning process)
  model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
  
  # iterate training data in batches
  model.fit(X_train, Y_train, nb_epoch=5, batch_size=32)
  
  # alternatively feed in training data in one batch 
  model.train_on_batch(x_batch, y_batch)
  
  # Evaluate model
  loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)	
  
  # create predicitions on new data
  classes = model.predicts(X_test, batch_size=32

  
  
  

  
  concept of model in Keras:

  • a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible.

  • neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models

  • Sequentialmodel = a linear stack of layers.