Thoughts on Course 1 of Prof. Andrew Ng’s specialization

I recently passed my first course of the specialization on Coursera and I decided to share some thoughts from someone new to Deep Learning.



Course 1 is structured very nicely, in week 1 it starts from the very basic of Neural Networks, though as the course prerequisite says a user needs some experience with Machine Learning ideally using Python to grasp concepts presented in these lectures.

In the following week basics of binary classification and logistic regression are explained including the cost function, gradient descent and derivatives, as well as basics of vectorization in python and shown with examples why vectorization is so important in Deep Learning.

I feel that there cannot be a simpler way to show this concept than this. Each lecture video in the course builds on the lecture before it which makes it easier to digest information in chunks or go over a particular concept multiple times to fully understand it.

In week 3 you implement a Shallow Neural Network with the knowledge you have gained from previous lectures. You learn that a Shallow Neural Network is a neural network with 1 hidden layer, in this week you build and use activation functions, vectorization, computing costs, gradient descent and more.

Finally in week 4 you learn to implement a fully connected deep neural network including forward and backward propagations, this week also gives difference of parameters and hyper parameters and why they are important, which is covered in Course 2


Quiz and Assignments are nicely prepared to help you gauge your own understanding, they cover all important concepts delivered throughout the lectures, I recommend spending some time there to fully understand those lectures before jumping to the following week.

I would highly recommend taking this course to anyone with experience in Machine Learning looking to start with Deep Learning.


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