Week 1 — Neural Networks and Deep Learning

Notations (source) :

Week 1 — Introduction to Deep Learning

May 1, 2021

Welcome

  • AI (Artificial Intelligence) is the new Electricity to bring about a change and a new era
  • Parts of this course:
    • Neural Networks and Deep Learning. → Recognizing cats
    • Improving Deep Neural Networks: Hyperparmeter tuning, Regularization and Optimization
    • Structuring your Machine Learning Project → Best Practices for project
    • Convolutional Neural Networks
    • Natural Language Processing: Building sequence models

Introduction to Deep Learning

What is neural networks?

Neural networks are created by structuring layers, with each layer consisting of “neurons” which get activated depending on its activation function and the input.

Supervised Learning with Neural Networks

Supervised learning refers to problems when we have inputs as well as labels (outputs) to predict using machine learning techniques. Then the models figure out the mapping from inputs to output.

The data can be:

  • Structured Data (tables)
  • Unstructured Data (images, audio, etc.)

Why is Deep Learning taking off?

Three reasons:

  • Backpropagation algorithm
  • Glorot and He initialization
  • ReLU (Rectified Linear Unit) activation function

Other reasons:

  • Data
  • Computation power
  • Algorithms

The scale at which we creating data is also important, as neural networks performance don’t stagnant unlike traditional machine learning algorithms

In this course $m$ denotes no of training examples.

About this Course

  • Week 1: Introduction
  • Week 2: Basics of Neural Network programming
  • Week 3: One hidden layer Neural Networks
  • Week 4: Deep Neural Networks

Note

Any image used here for illustration if not mentioned, is attributed to Andrew Ng’s lecture slides.