 Resources from Chris Achard in 2021

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

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We’ll set up some training data for a fully connected neural network, and train the model on that data.  Then, we’ll look at how the accuracy decreases as the number of epochs increase.

Train a Sequential Keras Model with Sample Data

When training and testing a neural net, it’s important to separate training data from validation, so that you aren’t checking the accuracy of the model with the same data that you use to train it.  This will help reduce overfitting. We’ll use the `validation_split` parameter when fitting our model to automatically split data up into a training set and a validation set, and use that to check the validation accuracy of our model.

Separate Training and Validation Data Automatically in Keras with validation_split

There are some cases where you don’t want an automatic validation sample - but you want to be able to provide your own validation data set.  For example, with time series data, you may want to use a sequential set of data to do validation.  We’ll manually split our training data into training and validation sets, and then train the model with that split.

Manually Set Validation Data While Training a Keras Model

Once we have a model with good training and validation accuracy, we will create some test data and evaluate the model’s accuracy on the new data set using the `evaluate` method on the Keras model.

Evaluate a Keras Model with Test Data

There are numerous ways to set up a neural network, and it can be difficult to figure out what combination of settings and architectures will get the best results.  We’ll investigate a few different typical network topologies including adding more “depth” and “width”, and evaluate what network topology is best for our data set.  For example, you may want a very deep network for increased accuracy on very complex problems, but the training time will take longer.  Or, you may add width to your network to increase accuracy, but this has a risk of overfitting.

Testing Different Neural Network Topologies

We will view a summary of the different networks as we create them by using the `summary` method on the model.  This will help us better understand the structure of the model.

Understand the Structure of a Keras Model by Viewing the Model Summary

Once we choose a network topology and train that network, we will use it to make predictions on new data.  At this point, you can now use your network to make predictions on data in production!

Make Predictions on New Data with a Trained Keras Models

Instead of training the network every single time it is run, we’ll want to save the Keras model so that we can use it in the future.  Keras makes it easy to save both the model weights and topology to a single file, so we’ll do that here.

Save a Trained Keras Model Weights and Topology to a File

We will load a saved model from a file, and then use it to make predictions on new data.  We won’t need to know anything about the model to do this - we can do it all in a new file by loading a saved model.

Load and Use a Saved Keras Model

We’ll take a Keras network designed for continuous (linear) output, and convert it into a network for binary classification, which can divide data into two classes (for example: “dog” vs “cat”), and can be used for things like sentiment analysis ("positive" vs "negative").

Create a Neural Network for Two Category Classification with Keras

We’ll replace our set of sample data with data that we import from a CSV, by importing it as a numpy array using numpy's genfromtxt method.  Then we’ll use that data to train the binary classification model.

Import Data From a CSV to Use with a Keras Model Using NumPy’s genfromtxt Method

We will use the Keras model’s `predict` method to look at the predicted class value.  Then we will use the `predict_classes` method to have Keras make a class prediction for us, and return only a 0 or a 1, which represents the predicted class.

Make Binary Class Predictions with Keras Using predict and predict_classes

We’ll take a network set up for binary classification, and turn it into a network that can take 3 or more classes.  This network will let us go beyond classifying data into only two categories, and will allow us to expand to any number of categories (for example: “dog” vs “cat” vs “mouse”).  Then, we’ll use real data from a csv to train and test that network.

Create a Dense Neural Network for Multi Category Classification with Keras

We can specify several options on a network optimizer, like the learning rate and decay, so we’ll investigate what effect those have on training time and accuracy.  Each data sets may respond differently, so it’s important to try different optimizer settings to find one that properly trades off training time vs accuracy for your data.

Change the Learning Rate of the Adam Optimizer on a Keras Network

We’ll break our training up into multiple steps, and use different learning rates at each step.  This will allow the model to train more quickly at the beginning by taking larger steps, but we will reduce the learning rate in later steps, in order to more finely tune the model as it approaches an optimal solution.  If we just used a high learning rate during the entire training process, then the network may never converge on a good solution, and if we use a low learning rate for the entire process, then the network would take far too long to train.  Varying the learning rate gives us the best of both worlds (high accuracy, with a fast training time).

Change the Optimizer Learning Rate During Keras Model Training

As we get new data, we will want to re-train our old models with that new data.  We’ll look at how to load the existing model, and train it with new data, and then save the newly trained model.

Continue to Train an Already Trained Keras Model with New Data

In this course, we'll build three different neural networks with Keras, using Tensorflow for the backend.  Keras is a high level API for building neural networks, and makes it very easy to get started with only a few lines of code.

You don't need to know a bunch of math to take this course, and we won't spend a lot of time talking about complicated algorithms - instead, we'll get straight to building networks that you can use today.

By the end of this course, you will be able to build a neural network, train it on your data, and save the model for later use.  Then, you'll be able to load up your model, and use it to make predictions on new data!

Course Introduction: Fully Connected Neural Networks with Keras

Once we have built a multi-class classification network, we'll use it to make predictions on new data that wasn't used during training.  We'll start by calling the `predict` method, which returns the probability that each input data row belongs to each one of the possible classes.  Then, we'll use the `predict_classes` method to only output the class prediction as an integer, which might be easier to use in a production system.

Make Predictions on New Data with a Multi Category Classification Network

React Native lets you build mobile apps using only JavaScript. It uses the same design as React, letting you compose a rich mobile UI from declarative components.

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We’ll create a brand new React Native app, and get it to launch on the iOS simulator and Android emulator.  A Mac is required to build for iOS, but Android development can be done on Mac, Windows, or Linux.

Create a React Native App and Run it on the iOS Simulator and Android Emulator

Once your React Native app is up and running, we’ll make a simple change and reload the simulator to show the change.  We’ll look at manually reloading the simulator, reloading with Live Reload and reloading with Hot Reloading, and talk about the pros and cons of each method.

Reload the Simulator when Changes Occur in React Native Apps

Displaying text is a key component of mobile apps, so we’ll look at how to show text on the screen, and how it differs from text on the web.  We’ll also format the text style to change the size, alignment, and more.

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