Autonomous Cars: Deep Learning and Computer Vision in Python

We'll quickly review the course outline, and give some guidance on which sections you might be able so skip given your prior experience.

We'll briefly cover why whitespace is important in Python and how it's used, how to import packages of existing code libraries, and how to use lists in Python.

We'll continue diving into Python data structures with tuples and dictionaries, and examples of using them.

We'll cover the syntax of functions in Python, how to pass functions around as parameters, and lambda functions. We'll also see how boolean operations like equality and or work.

We'll see how for and while loops with in Python, and challenge you to a very simple exercise to practice what you've learned so far.

We'll walk through some examples of using the Pandas package, to slice and dice some fake video index data from cars.

We'll cover the different charts that MatPlotLib can produce from our data, and how to load and view images.

Learn how train/test and K-fold cross-validation helps us to prevent "overfitting" models to the data they were trained with.

We'll go in depth on how linear regression learns how to fit a line to observed data, to create a simple model we can use to predict new observations.

Let's use linear regression to build a model mapping road conditions to vehicle speed.

Logistic regression builds a model that classifies data into one of two categories.

Decision trees build up a flowchart-like model, that classifies data based on various decision points that branch off to others.

In this activity, we'll implement the example from the previous lecture of predicting hiring decisions based on candidate attributes, and also see what happens when we use decision trees for the same logistic regression sample of predicting vehicle speed for an upcoming bump in the road.

We'll cover Bayes Theorem and how it can help us understand conditional probabilities, and apply it to Naive Bayes to classify email as spam or "ham." 

We'll build a real spam classifier using Naive Bayes, and see how it well it works on our problem of classifying vehicle speeds based on upcoming obstacles in the road.

We'll apply SVC to our vehicle speed classification problem, and illustrate hyperparameter tuning to find the best kernel and best set of parameters to use.

We'll explore how to construct deep neural networks for binary and multi-class classification with Keras, the importance of normalizing your input data, and how one-hot encoding is used to translate categories into a representation that's compatible with neural nets.

We'll use Keras to easily experiment with a variety of network topologies to apply deep learning to our "car approaching a bump" classification problem.

We'll go into more depth on activation functions and why ReLU is popular, and cover techniques for preventing overfitting including Dropout layers.

We'll run our previous neural network longer to make some overfitting happen, and see how a Dropout layer in Keras can improve accuracy by preventing overfitting.