Intergalactic Zoo from RTK3 Games – Game by Mammoth Interactive Student in Construct 2

We at Mammoth Interactive love hearing when you use what you learned in our courses to publish your own games. RTK3 Games is out with a new arcade game made in Construct 2!

Intergalactic Zoo is a plat-former game for kids where you try and collect all the gems before time runs out. Collect those gems in quick enough time to unlock a small present, a large present and/or a key to help rescue the Mama animals. Save the mama animals to go to bonus level to save the babies.

– 5 Characters to choose from
– 25 Levels
– 5 themes for the players to discover
– Text screens have voice over for younger gamers who cannot read yet
– Great Background Music
– Local High Score Save

Watch the game trailer below!

Want to make a game like this? Enroll in Mammoth Interactive courses on game development with Scirra’s Construct game engine:

Make Games Without Coding in Construct 3

Learn to Make a Game in 30 Minutes with Construct 3

The Complete Game Developer Course – Build 80 Games in Construct 2

Curriculum of our Android Studio, Java and TensorFlow course

Enroll now in our mobile machine learning masterclass to get all of these topics and learn from the comfort of your couch. Now dirt cheap at https://training.mammothinteractive.com/p/mobilemachinelearning/?product_id=509994&coupon_code=SPECIALBLOGCOUPON

Introduction to Machine Learning + Software

  • An interview with your instructor
  • Intro to the Course (9:56)
  • Update! Resources Folder

Intro to Android Studio

  • Intro and Topics List (2:25)
  • Downloading and Installing Android Studio (6:44)
  • Exploring Interface (12:12)
  • Setting up an Emulator and Running Project (6:43)

Intro to Java

  • Intro to Language Basics (2:46)
  • Variable Types (14:00)
  • Operations on Variables (10:49)
  • Array and Lists (9:26)
  • Array and List Operations (7:59)
  • If and Switch Statements (11:34)
  • While Loops (10:09)
  • For Loops (8:51)
  • Functions Intro (8:39)
  • Parameters and Return Values (7:05)
  • Classes and Objects Intro (12:13)
  • Superclass and Subclasses (11:42)
  • Static Variables and Axis Modifiers (7:27)

Intro to App Development

  • Intro to Android App Development (1:57)
  • Building Basic User Interface (12:15)
  • Connecting UI to Backend (6:12)
  • Implementing Backend and Tidying UI (9:09)

Intro to Machine Learning Concepts

  • Introduction to ML Concepts
  • Downloading and Installing PyCharm and Python (6:55)
  • Exploring PyCharm (7:48)

Python Language Basics

  • Intro and Topics List (2:40)
  • Intro to Variables (13:17)
  • Variables Operations and Conversions (12:35)
  • Collection Types (12:47)
  • Collections Operations (8:42)
  • Control Flow If Statements (12:50)
  • While and For Loops (10:44)
  • Functions (11:23)
  • Classes and Objects (15:40)
  • Source Code

Intro to TensorFlow

  • TensorFlow Intro (2:53)
  • Topics List (6:09)
  • Importing TensorFlow to PyCharm (4:25)
  • Constant Nodes and Sessions (9:01)
  • Variable Nodes (10:45)
  • Placeholder Nodes (7:35)
  • Operation Nodes (12:47)
  • Loss, Optimizers, and Training (11:56)
  • Building a Linear Regression Model (20:30)
  • Source Code

Machine Learning in Android Studio Projects

  • Introduction to Level 2 (5:15)

Introduction to Tensorflow Estimator

  • Introduction (3:08)
  • Topics List (4:12)
  • Setting up Prebuilt Estimator Model (15:15)
  • Evaluating and Predicting with Prebuilt Model (7:42)
  • Building Custom Estimator Function (10:12)
  • Testing Custom Estimator Function (7:00)
  • Summary and Model Comparison (9:46)
  • Source Code

Intro to Android ML Model Import

  • Intro and Demo (4:09)
  • Topics List (4:22)
  • Formatting and Saving Model (8:25)
  • Saving Optimized Graph File (14:48)
  • Starting Android Project (9:01)
  • Building UI (14:56)
  • Implementing Inference Functionality (9:14)
  • Testing and Error Fixing (11:01)
  • Source Files

Simple MNIST

  • Intro and Demo (3:50)
  • Topics List and Intro to MNIST Data (10:24)
  • Building Computational Graph (14:20)
  • Training and Testing Model (14:24)
  • Saving and Freezing Graph for Android Import (12:33)
  • Setting up Android Studio Project (13:07)
  • Building User Interface (15:58)
  • Loading Digit Images (10:02)
  • Formatting Image Data (10:59)
  • Making Prediction Using Model (7:32)
  • Displaying Results and Summary (13:13)
  • Source Files

MNIST With Estimator

  • Introduction (3:08)
  • Topics List (2:38)
  • Building Custom Estimator Function (15:34)
  • Building Input Functions, Training, and Testing (13:38)
  • Predicting Using Model and Model Comparisons (9:37)
  • Source Files

Build Image Recognition Apps

  • Introduction to Building Image Recognition Apps (6:34)

Weather Prediction

  • Intro and Demo (3:49)
  • Tasks List (4:36)
  • Retrieving the Data (14:00)
  • Formatting Data Sets (14:02)
  • Building Computational Graph (11:47)
  • Writing, Training, Testing, and Evaluating Functions (12:24)
  • Training, Testing, and Freezing the Model (9:48)
  • Setting up Android Project (8:05)
  • Building the UI (15:29)
  • Build App Backend and Project Summary (13:46)
  • Source Code

Text Prediction

  • Intro and Demo (4:13)
  • Tasks List (3:17)
  • Processing Text Data (13:18)
  • Building Data Sets and Model Builder Function (16:16)
  • Building Computational Graph (8:37)
  • Writing, Training, and Testing Code (15:11)
  • Training, Testing, and Freezing Graph (12:27)
  • Setting up Android Project (7:41)
  • Setting up UI (5:19)
  • Setting up Vocab Dictionary (8:34)
  • Formatting Input and Running Through Model (7:55)
  • Source Code

Stock Market Prediction

  • Intro and Demo (3:47)
  • Task List (5:17)
  • Retrieving Data via RESTful API Call (16:30)
  • Parsing JSON Data Pycharm Style (6:37)
  • Formatting Data (15:45)
  • Building the Model (13:26)
  • Training and Testing the Model (9:54)
  • Freezing Graph (10:06)
  • Setting up Android Project (6:07)
  • Building UI (8:25)
  • Requesting Data Via AsyncTask (8:25)
  • Parsing JSON Data Android Style (12:04)
  • Running Inference and Displaying Results (17:42)
  • Source Code

Image Analysis with Keras

  • Introduction to Level 4 (9:44)

Simple CIFAR-10

  • Intro and Demo (4:47)
  • Topics List (3:47)
  • Exploring CIFAR-10 Dataset (10:48)
  • Update! CIFAR_10 Android Fix
  • Formatting Input Data (13:13)
  • Building the Model (16:24)
  • Freezing Graph and Training Model (16:56)
  • Setting up the Android Project (16:43)
  • Setting up UI (9:02)
  • Loading and Displaying Image (6:33)
  • Formatting Image Data for Model Input (13:55)
  • Predicting and Displaying Results (13:26)
  • Summary and Outro (6:39)
  • Source Code

Face Detection

  • Intro and Demo (3:20)
  • Tasks List (3:09)
  • Loading Face and Non Face Images (15:56)
  • Reformatting Input Data (11:12)
  • Build Model + Write, Train & Test Scripts (19:12)
  • Freeze Graph + Train & Test Model (14:38)
  • Setting up Android Project (11:49)
  • Setting up UI (7:52)
  • Loading and Display Images (10:11)
  • Formatting Data and Running Inference (12:47)
  • Displaying Results and Summary (8:51)
  • Source Code

Emotions Detection

  • Intro and Demo (3:39)
  • Tasks List (2:34)
  • Loading and Formatting Data (11:13)
  • Build Training and Testing Datasets (6:52)
  • Building the Model (9:57)
  • Build Functions to Train, Test, & Predict (11:58)
  • Training and Testing the Model (11:06)
  • Setting up Android Project (6:33)
  • Importing and Displaying Images (6:25)
  • Convert Images and Run Inference (8:17)
  • Displaying Results and Summary (7:48)
  • Source Code

Increase Efficiency of Machine Learning Models

  • Intro to Increasing ML Efficiency (5:47)
  • Source Code

Introduction to Tensorflow Lite

  • Tensorflow Lite (10:19)

Text Summarizer

  • Introduction (6:22)
  • How a Model Is Built (13:08)
  • Training and Summarizing Mechanisms (9:31)
  • Training and Summarizing Code (7:44)
  • Testing the Model (5:27)
  • Source Code

Object Localization

  • Introduction (4:13)
  • Examining Project Code (15:05)
  • Testing with a Mobile Device (7:30)

Object Recognition

  • Introduction (7:30)
  • Examining Code (22:29)
  • Testing on Mobile Device (5:36)

Intro to Tensorboard

  • Introduction (2:55)
  • Examining Computational Graph In Tensorboard (13:46)
  • Analyzing Scalars and Histograms (13:01)
  • Modifying Model Parameters Across Multiple Runs (10:32)
  • Source Code

Advanced ML Concepts

  • Introduction (18:49)
  • Source Code

Advanced MNIST

  • Intro and Demo (3:42)
  • Topics List (3:41)
  • Building Neuron Functions (11:18)
  • Building the Convolutional Layers (11:51)
  • Building Dense, Dropout, and Readout Layers (14:38)
  • Loss & Optimizer Functions, Training, & Testing (19:51)
  • Optimizing Saved Graph (10:57)
  • Setting up Android Project (12:30)
  • Setting Up UI (10:58)
  • Load and Display Digit Images (6:14)
  • Formatting Model Input (13:52)
  • Displaying Results and Summary (11:11)
  • Source Files

Advanced CIFAR-100

  • Intro and Demo (3:18)
  • Tasks List (3:11)
  • Inputting and Formatting Data (10:50)
  • Building the Model (10:19)
  • Training, Testing, and Freezing Model (10:57)
  • Setting up Android Project (10:24)
  • Building UI (8:09)
  • Loading and Displaying Images (7:03)
  • Converting Image Data & Running Inference (7:37)
  • Summary and Outro (9:29)
  • Source Code

Image Recognition in iOS

  • Introduction and Demo (3:20)
  • Project Setup (6:59)
  • Displaying and Resizing Images (9:42)
  • Converting Image to Pixel Buffer (14:06)
  • Summary and Outro (8:07)
  • Source Code

Intro to iOS

  • Introduction (2:15)
  • Source Code

Xcode Intro

  • Downloading and Installing (6:22)
  • Exploring XCode’s Interface (15:40)

Swift Language Basics

  • Variables Intro (7:57)
  • Variable Operations (10:43)
  • Collections (8:57)
  • Control Flow (10:18)
  • Functions (5:28)
  • Classes and Objects (9:55)

iOS App Development Intro

  • Building App From Start to Finish (12:46)

Intro to CoreML

  • Introduction to CoreML (9:08)

iOS Tensorflow Model Import

  • Introduction (4:35)
  • Converting pb to mlmodel
  • File & Setting up Project (7:34)
  • Running Inference Through Model (9:58)
  • Testing and Summary (3:55)

Learn now in our master course with app developer Nimish Narang! 90% off today with this coupon: https://training.mammothinteractive.com/p/mobilemachinelearning/?product_id=509994&coupon_code=SPECIALBLOGCOUPON

Learn to make games without coding in Construct 3

Get an epic 6-level course that will take you from newbie to pro at making your own computer games.

Coding is the hardest part of making a game. What if you could build a game without coding? We are here to show you how to do just that.

*While supplies last* get a coupon

Enroll now to build your own huge portfolio of simple 2D games. You will learn game development, design and mechanics with no coding.

What you’ll get at a glance

  • Get something no one can take away from you: an education
  • Become a technical and creative thinker
  • Make games that work well and look good
  • Learn anywhere and build anytime
  • Get lifetime access to a course
  • Avoid common mistakes that cost beginners a fortune
  • Solve problems with a smart and fun mindset
  • And much more!

What Is Construct 3?

Construct 3 is software that lets you make your own computer games. This revolutionary platform makes game making easier, more convenient and fun.

This MEGABUNDLE has 6 easy to follow levels:

Introducing the Construct 3 Ecosystem

 You can choose how you learn to make the course the most intuitive and enjoyable for you.

Make Your Game Work with Cool Features

 You don’t need to install or set up anything. All you need is a computer with an Internet connection. Once loaded Construct 3 will work even offline.

Construct 3 will run on all iOS 11+ AND Android based mobile devices.

Make Your Own Pixel Art in Construct 3 from Scratch

We’ll take you through each step on our screen over-the-shoulder style. You’ll get to see how the experts do their work.

Add More Details to Games You Make

You will learn to make a visually appealing product. Get a sense of aesthetic to make your game stand out from others on the App Store.

Make More Challenging Game Type

We’ve taken the time to curate a curriculum tailored for people of all levels.

This course is beginner-friendly. If you have game development experience already, join to broaden your skills. You will get exposed through our courses to making many types of games.

Pledge now to also get free reign to build on these games to make your own creations.

Do you have an idea for a game you want to see that no one else is making?

If you like playing games, make the jump from consumer to creator with this course. This is a one-stop shop.

Continue Your Journey as an Experienced Developer

 Why should you learn Construct 3?
  • Make games without coding
  • Easiest engine to use for 2D games
  • Can make games up to 10 times faster than other engines

Why should you make a game?

  • Making a game allows you to learn a new skill. It’s important to keep learning.
  • Making a game allows you to be technical and creative. Learning game development will teach you how to find connection between different ideas. Innovation sparks when two unrelated disciplines come together.
  • You can make money. As a game developer, you’ll have control over your job and will get your name out there.

Construct 3 will be your new favorite engine

  • Like most game engine platforms, Construct 3 has free and paid versions. We use the free version for the 6 levels of this course. We will use the more advanced paid version of Construct 3 in bonus sections.
  • Uses events and actions to create games
  • Has robust features
  • Can export to many platforms such as iOS, Android, PC, Mac and Consoles. *must have the paid version to do this.
  • Paid version costs only 30 cents USD per day.
  • What we don’t do: take you from just A to B. We go in depth. You will learn game building secrets only taught the most prestigious colleges.
  • Build 10 games from scratch

Design AND Develop

Not only will you learn to make games that work well and have different features, you will learn to make all art yourself.

 You will learn to make all art for games from scratch. No more need to outsource artists or use basic stock footage!

Our History

Make your next game, website, app or data science project with Mammoth Interactive! Over 400’000 students have enrolled in this online school since its founding in 2008.

Targeting beginners, a Mammoth Interactive curriculum takes you step by step through a project from start to finish. Learn by doing in your next Mammoth Interactive course!

You will get hours of content.

Show people what you can do by learning in-demand skills in a hot field of technology.

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Who is the target audience?

  • Suitable for all ages
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  • People who already have experience and want to learn the new Construct 3 software
  • Join the hype of online learning
  • Anyone who wants to add more skills to their resume
  • Students who to make a portfolio of projects ready for displaying by the end of the course

This sale won’t last forever. Make a game now!

What are neural networks?

Neural networks explained

Read below a sneak peek of the articles in our Mobile Machine Learning course with Nimish Narang.

Enroll in the course for 90% off

Neural networks are a network of interconnected nodes. Each node has weights and biases associated with it. Data flows through the network from the input to the output nodes.

Each neural network contains layers of nodes.

Each layer is connected to all the nodes in the next layer. Every input will travel through the network until it reaches the very end. It will contain certain values along the way that determine how important that particular pathway is.

In more general terms, neural networks are a set of algorithms designed to recognize patterns.

This makes them great at image classification and image recognition. To the machine or model that you build, and image is just a set of data, an array of values typically between 0 and 255 that represent pixel values. If it can represent certain patterns associated with certain outcomes, it can learn to solve a problem.

The name ‘neural network’ is derived from their structure of a network of nodes. It’s modeled around the human brain, which contains many different neurons each connected to each other and requiring activation to produce results.

Each network contains several layers. Each layer contains operations to either process inputs or map pathways through the network. These pathways produce specific outputs.

Each layer is a mini-network itself of many interconnected nodes. The number of nodes depends on the complexity of the problem you’re trying to solve. Each node is assigned a weight and a bias.

Weights are assigned an initial value that changes over time. When you train your model, you alter the weights. The weights are assigned specifically to a connection between nodes rather than one node.

A bias is a constant value assigned to each node.

How is the weight useful to us?

The weight determines which path to take once a node receives an input. More specifically: how important that path is. This repeats until we reach the end of the network.

At each layer an activation function will be attached to each node at the layer. Certain activation functions are better for certain tasks. An activation function is a way to sum up all the inputs of a layer. It will transform the sum depending on the function you use and will produce an output.

The output will propagate throughout the network until the end. At the end, you can sum all the input values, perform a function, and produce a meaningful output.

Machine learning example

For example, suppose the end sum of your activation function produces an output of 0.1 on the inputs. Suppose in this example, we’re using a neural network to determine if an image is of a face. The closer to 1 that the output value is, the more likelihood it is that the image has a face.

Based on this example, an output of 0.1 means we don’t have a face. If the sum of the weights after the activation function is 0.8, you probably have an image of a face.

For each node in the middle of the network, the input of a node is the output to another node. During training, when you receive inputs and feed in a bunch of data to a network over and over again, the weights will be adjusted in such a way that it starts to learn certain patterns and associate them with outputs.

The network adjusts the weights accordingly so that if you get a face image, the network will know to take certain pathways. Whereas if you get a non-face image, the network will know to take different pathways to the weights we’ve trained. The data runs through one side to the other until a pattern is recognized and we get similar data mapping to similar pathways.

Learn more with Nimish in Mobile Machine Learning: The Complete Masterclass

Machine learning concepts explained

Read below a sneak peek of the articles in our Mobile Machine Learning course with Nimish Narang.

Enroll in the course for 90% off

What is machine learning?

Machine learning is a machine’s ability to make decisions or predictions based on previous exposure to data and extensive training. In other words, if a machine (program, app, etc.) improves its prediction accuracy through training, it has “learned.”

Machine learning is not quite artificial intelligence (AI) because a machine with AI can reason and think and act intelligently.

However, no AI can make decisions based on training and previous exposure to data. AI uses machine learning, but machine learning doesn’t necessarily use AI.

How machine learning models work

Computational graphs consist of a network of connected nodes (often called neurons). Each of these nodes typically has a weight and a bias that helps determine, given an input, which path is the most likely.

This enables the model to make intelligent decisions based on previously learned pathways. Outputs of one node are inputs into another. Nodes can be the results of operations or just be there to hold a value.

Building a machine learning program

There are 4 main components to building a machine learning program: data gathering and formatting, model building, training, and testing and evaluating

Data gathering and formatting

Here you want to gather a lot of data for your model to learn from. This means getting a variety of examples to expose the model to many variations and to get lots of it (thousands).

All data should be formatted pretty much the same (images same size, same color scheme, etc.) and should be labelled. You should also divide data into mutually exclusive training and testing sets.

Model building

Figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you’re trying to solve. Break the bigger goal into smaller tasks and solve each of them at a time. Decide which optimizer, activation, and loss functions you want to use to that best fit the data you enter in for training. This can include connections, nodes, neurons, and more.

Training a machine learning model

Here a model assigns weights and biases to neurons. This helps the model make decisions and choose from a few outputs given a specific set of inputs. The model can choose paths through the neural network or computational graph based upon the inputs for a particular run, as well as the weights and biases of neurons in the network.

In supervised learning, we show the model what the correct outputs are for a given set of inputs, and the model alters the weights and biases of neurons to minimize the difference between its output and the correct answer.

Testing and evaluating machine learning models

After the model has been trained and the weights and biases of each neuron or node are fixed, we feed in a new set of data to see how well the model performs and how accurate the predictions are. Although it depends on the task at hand, good models will be > 90% accurate and very good models > 98% accurate.

Learn more with Nimish in Mobile Machine Learning: The Complete Masterclass