DeepLearningCourse

learnings from Coursera's Deep learning course .

The broad steps in implementing neural netwoks are as follows

Define Model Structure
- number of layers, # neurons per layer
- activation functions to use
Initialize model parameters
Iterate through this
- Calculate cost function J using forward propogation
- Calculate the current gradients using backward propogation
- Update weights

Common Python commands used

import numpy as np                      => numpy is a great choice for linear algebra/math functions needed for NN  
m_train = train_set_x_orig.shape[0]     => use .shape, .reshape to access dimensions, vectorize matrices into columns
np.zeros([dim,1])                       => initialize matrices with 0's
dw = np.dot(X, (A - Y).T) /m            => dot product equivalent to Matrix multiplication ( 2D matrices)
c1 = np.multiply(Y, np.log(A))          => Element-wise matrix multiplication
db = np.sum(A - Y ) /m                  => Subtract matrices A - Y (broadcasting if needed), then add element-wise to give a single number

Popular frameworks for Neural Networks

Caffe
CNTK
Keras
Pytorch
Tensorflow
Theano
Lasagne
PaddlePaddle
mxnet
DL4J

Consider installing the following: Anaconda Python Environment -> TensorFlow -> Keras libraries

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