Neural Network Basics with Logistic Regression
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# Neural Network Basics with Logistic Regression

These are some of my notes taken from the first course of Deep Learning Specialization (Neural Networks and Deep Learning) by deeplearning.ai. This might help you gain insights on how a neural network is formed and how Logistic Regression is used to form one single neuron to stack up and form a neural network. Further down, there is a discussion on how vectorization helps us improve the efficiency of code and algorithms in Python using Numpy.

### 2. Logistic Regression

• In the following figure, the loss function (L) computes the error for a single training example; the cost function (J) is the average of the loss functions of the entire training set.

• Gradient descent is a method to determine the optimum values of the parameters using slopes, derivatives and a learning rate

• For logistic regression, the parameters w & b are optimized as follows, where alpha is the learning rate

### 4. Computing Derivatives for Gradient Descent

• The following computation graph is made by breaking down the formula for evaluating the cost function of the model
• Forward pass: Evaluation of the cost function
• Backward pass: Evaluation of the authenticity of the model and also the best fit parameters by finding the derivative of final output w.r.t different model variables
• One backward pass in a computation graph gives us the derivative of the current variable with respect to the one that we are passing to.
• e.g. Moving from block J to block v, we find the derivative of J w.r.t. v
• We apply chain rule which propagating back through the graph

### 5. Logistic Regression Derivatives

• The following image shows the gradient descent for Logistic regression method using only one data point
• That is why, we are taking loss function (L) instead of the cost function (J) as we only have one data point for now
• After we calculate “da=dL/da” & “dz=dL/dz”, we can use the following formula to get the change in w1 and w2:
• w1 = w1 - alpha * dw1
• w2 = w2 - alpha * dw2

### 6. Logistic Regression on “m” examples

• Algorithm to implement logistic regression for a dataset (m input samples) with 2 features only
• Although there is a for loop being used in the algorithm below, we will need vectorization techniques to simplify and optimize the code to work on large datasets.

### 7. Python and Vectorization

• Here, np.dot(W, X) is simply evaluating W transpose . X

### 8. Vectorizing Logistic Regression

• In the last equation, although “b” is a (1, 1) real number, python automatically converts it to a (1, m) row vector. This is called Broadcasting

### 9. Implementing Logistic Regression

• Finally, the code to implement logistic regression in python is given below (right part)

• When axis=0, the numpy operations are carried out on the vertical axis

### 11. Python and Numpy Tips

• Do not use Rank 1 vectors as shown in the image below
• Use assert() statements in your code to validate the dimensions of the arrays/vectors you are working with

### 12. Explanation (derivation) of Loss function for Logistic Regression

• Loss Function

• Cost Function

Updated Apr 13, 2020 2020-04-13T14:47:03-05:00
This post is written by Ashish Jaiswal