*This post is part of my #100DaysOfMLCode challenge. Learning about Machine Learning for 100days continuously. I have taken images from my specialization course which I have taken on Coursera.

*please ignore my writing skills. and also I didn’t organize the information well. This Post is about what I have understood from the videos which I have watched.

Coursera’s Deep Learning Specialization course homepage: https://www.coursera.org/learn/neural-networks-deep-learning#

# Python and Vectorization

## Vectorization

- Vectorization is basically a method of getting rid of explicit for-loops.
- In the deep learning era, code runs for relatively large datasets. So our code needs to perform well. Vectorization plays a key role here.
- vectorized code runs faster than non-vectorized code.
- SIMD(Single Instruction Multiple Data)

## More Vectorization Examples

- By avoiding explicit for loops, your code will run significantly faster.
- The rule of thumb to keep in mind is “whenever you are programming neural network or logistic regression just avoid the explicit for-loops whenever possible.”
- Whenever you want to compute Vector ‘u’ as a product of matrix A and another vector ‘v’, then
- definition of matrix multiplication
- in the non-vectorized version, we need to have two for loops.
- this is the vectorized version, this will eliminate the use of two- for loops.
- suppose you need to apply the exponential operation on every element of a vector/matrix.
- As you can see , there is a non-vectorized implementation on the left side. Where as right side contains Vectorized implementation. Vectorized implementation will allows us to eliminate the explicit for-loop.
- using numpy functions we can able to apply the similar operation to all the elements of a vector without the use of explicit for loop.
- we have eliminated the second for-loop from the algorithm. Instead of a for-loop we have used vector.

## Vectorizing Logistic Regression

- Vectorizing Logistic Regression means implementing Logistic Regression without a single for-loop.
- predictions can be calculated in a single step rather than for each individual training element.
- with one line of code, we can able to calculate capital Z.

## Vectorizing Logistic Regression’s Gradient Output

- we have done forward propagation and backward propagation by computing the derivatives and predictions on all the training examples without using a for-loop.
- Gradient Descent is updated as following.
- we may need an explicit for-loop whenever we need to iterate it for multiple times.
- We can able to do a single iteration of for-loop without using a for-loop.
- Broadcasting a technique through which certain parts of the code can be made more efficient and faster.

## Broadcasting in Python

- Can you calculate the percentage of calories from carbs, protein, and fats without the use of explicit for-loop.
- axis=0 represents the adding the sum of the elements vertically.
- this is an example of broadcasting in python.
- basic process of broadcasting.
- General principle of broadcasting.

## A Note on python/numpy vectors

- whenever you create a vector, don’t use the “rank 1” type vectors.
- if you are not sure about the dimensions then you can use assert statement.
- Either use column vector or row vector.

## Logistic Regression Cost Function

- single equation depicting Logistic Regression in two forms.

## Python Basics with Numpy

- In this particular Programming exercise we will learn
- How to use Numpy
- Implement some basic core deep learning functions such as the softmax, sigmoid, dsigmoid, etc.
- Learn how to handle data by normalizing inputs and reshaping images.
- Recognize the importance of vectorization.
- Understand how python broadcasting works.
- I have learnt about building basic functions with numpy.
- The data structures we use in numpy to represent the shapes ( vectors, matrices, etc) are called numpy arrays.
- Implemented logistic sigmoid function using numpy.
- Implemented sigmoid gradient using numpy.
- Two common numpy functions used in deep learning are np.shape and np.reshape.
- Learnt how to reshape the numpy arrays.
- Implemented broadcasting and softmax function.
- How vectorization plays an important role in computation of vectors and matrices.

- Vectorization plays an important role in deep learning. It provides computational efficiency and clarity.
- Implemented the basicsigmoid, sigmoid, sigmoid_derivative, image2vector, normalize rows, softmax, L1 function and L2 function.

## Logistic Regression with a Neural Network Mindset

- In this Programming assignment we are going to work with logistic regression in a way that builds intuition relevant to neural networks
- By doing this assignment you will get to know about building the general architecture of a learning algorithm
- You will get to know about initializing parameters, calculating the cost function and its gradient. Using an optimization algorithm as well

__numpy__is the fundamental package for scientific computing with Python.__h5py__is a common package to interact with a dataset that is stored on an H5 file.__matplotlib__is a famous library to plot graphs in Python.__PIL__and scipy are used here to test your model with your own picture at the end.

- Many software bugs in deep learning come from having matrix/vector dimensions that don’t fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs.

Common steps for pre-processing a new dataset are:

- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, …)
- Reshape the datasets such that each example is now a vector of size (num_px * num_px * 3, 1)
- “Standardize” the data

### The main steps for building a Neural Network are:

- Define the model structure(such as number of input features)
- Initialize the model’s parameters.
- LOOP( calculate current loss (forward propagation), Calculate current gradient (backward propagation), update parameters(gradient descent))

### Implemented the following functions by doing this exercise.

- Initializing the values of w,b.
- Optimize the loss iteratively to learn the parameters (w,b)
- Computing the cost and its gradient.
- Updating the parameters using gradient descent.
- Used the learned (w,b) to predict the labels for a given set of examples.

- Different learning rates gives different costs.
- In deep learning it is recommended to chose the learning rate which minimizes the overall cost. But if the model overfits , use other techniques to reduce overfitting.

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