The course is part of this learning path
Introduction to Deep Learning
Continue the journey to data and machine learning, with this course from Cloud Academy.
In previous courses, the core principles and foundations of Data and Machine Learning have been covered and best practices explained.
This course gives an informative introduction to deep learning and introducing neural networks.
This course is made up of 12 expertly instructed lectures along with 4 exercises and their respective solutions.
- Understand the core principles of deep learning
- Be able to execute all factors of the framework of neural nets
- It would be advisable to complete the Intro to Data and Machine Learning course before starting.
About the Author
I am a Data Science consultant and trainer. With Catalit I help companies acquire skills and knowledge in data science and harness machine learning and deep learning to reach their goals. With Data Weekends I train people in machine learning, deep learning and big data analytics. I served as lead instructor in Data Science at General Assembly and The Data Incubator and I was Chief Data Officer and co-founder at Spire, a Y-Combinator-backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. I earned a joint PhD in biophysics at University of Padua and Université de Paris VI and graduated from Singularity University summer program of 2011.
Hello, and welcome to section four on deep learning. In the last section we met linear regression and logistical regression and used them to solve both classification and regression problem. These models share a lot of elements in common. For example, they are both formulated as a hypothesis with parameters and they're both optimized, they both learn from examples by minimizing a cost function. However, these models have some limits. They don't work well in non-linear cases. So to extend these models and work with more complex cases we'll need other techniques. What is awesome about neural nets is that they provide a unified framework to deal with all cases. So you'll be able to do regression and classification you'll be able to do linear and non-linear problems all within the same framework of neural nets. So we starting this chapter to introduce them with the simplest possible neural network it's called the perceptron. We will see some elements like weights, biases nodes layers and activation function. In the end, we'll use it to perform a simple classification and see how it works. So let's get started with section four, deep learning.