6 Key Takeaways from Andrew Ng's Neural Networks and Deep Learning Course

I recently completed Andrew Ng's 'Neural Networks and Deep Learning' course on Coursera, which comprehensively introduces neural networks, a fundamental aspect of machine learning and AI.

My Goal: My aim in doing the course was to understand neural networks and deep learning and get hands-on experience in building a neural network.

I wrote this blog post to concretize my understanding of the concepts I learned about neural networks and share some insights with people like you who are planning to understand neural networks.

By reading this post, you will

1. Be able to decide if you should enroll in the course. Of course, this post will not be the sole reason for your decision.
2. Understand the basics of a neural network: The 6 points mentioned in this post are the key elements of any neural network. Even if you do not enroll in the course, by reading this you will have a basic understanding of neural networks.

So, the following are the 6 things I learned about neural networks in the course.

1. What is a Neural network?

Data Science is all about finding patterns in data and creating mathematical models of the pattern. 

Neural networks are computational models inspired by the human brain's architecture.  In neural networks, the basic idea is to use multiple layers stacked together such that the algorithm can better learn the pattern that defines the underlying object in the training dataset.

Each layer is a mathematical function. 

Each layer processes the input and gives out an output which acts as an input to the next layer and so on.

2. What is the difference between using a neural network and a plain algorithm?

• The training set is processed by more than 1 layer.
• In a neural network, the algorithm tries to learn on its own and so there are 2 steps.
• First is the forward propagation, which is used to make predictions and the second is backward propagation which is used to adjust weights, enhancing learning efficiency. 
• This reminded me of the concept of Feedback amplifier in Physics learned in school.

3. What is deep learning?g

It is a marketing term used for neural networks with more than 1 hidden layer. Deep learning refers to neural networks with multiple hidden layers, enabling the modeling of complex data representations. This depth allows the network to learn hierarchical features, making it effective for tasks like image and speech recognition

4. What is Vectorization

It is all about using pre-compiled C code for performing operations on entire arrays or matrices simultaneously with high performance. Neural network uses numpy arrays and vectorization is one of the reasons that makes training neural networks in reasonable time possible.

5. The math behind Neural works

The course delves into the mathematical foundations of neural networks, including calculus for gradient computations, linear algebra for matrix operations, and optimization techniques essential for effective model training.

It is very exciting to see the math formulae being converted into Python code to build the neural network. And it actually works :) 

6. Broadcasting in python

This has been the most difficult concept to understand in the entire course. The second would be the calculus. 

Broadcasting in Python's NumPy library allows arithmetic operations on arrays of different shapes by automatically expanding their dimensions as needed. This feature simplifies code and enhances computational efficiency in neural network implementations

Hope you enjoyed reading the post and learned the basics of neural networks. 

Do share your feedback in the comments section.

Neural networks, like most innovations, have a lifecycle of adoption and evolution. If you're curious about how technologies evolve and sustain relevance, explore my post on the Life Span of Innovation. It offers a perspective on the factors that drive innovation longevity in the tech world.

Neural networks are a powerful innovation, but their true potential unfolds when supported by a robust ecosystem. Dive into my article on Do You Have an Ecosystem to Support Your Innovation? to understand how ecosystems drive adoption and maximize impact.