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  5. Getting Started With Deep Learning: Working With Data: Gradient Descent

Exercise 2

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Overview
DifficultyBeginner
Duration1h 45m
Students140
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Description

Learn about the importance of gradient descent and backpropagation, under the umbrella of Data and Machine Learning, from Cloud Academy.

From the internals of a neural net to solving problems with neural networks to understanding how they work internally, this course expertly covers the essentials needed to succeed in machine learning.

Learning Objective

  • Understand the importance of gradient descent and backpropagation
  • Be able to build your own neural network by the end of the course

Prerequisites

 

Transcript

Hey guys, welcome back. In this video, I will review exercise two of section four. In exercise two, we build a fully connected neural network model to predict the diabetes patients that we've loaded and prepared in exercise one. So we're guided through a series of step, we start by splitting the data into train test split, then, we define a sequential model, with at least one inner layer. So, to build this model, we'll have to make a few choices. 

What the size of the input is, how many nodes we will use in each layer, the size of the outputs, and then what activation functions we will use in the inner layers, and, what activation functions we're gonna use at the output. Also, what loss function we will use, and what optimizer we will use. Finally, we'll fit the model on the training set, using a validation split of 0.1, and we'll test the trained model on the test data from the train test split. Finally we check the accuracy score, the confusion matrix, and the classification report. As usual, try to do the exercise first, and then feel free to watch the next video with the solution. Good luck.

About the Author

Students1048
Courses8
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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.