Student Showcase: Seahunter

Thanks to our game development student Andreas for sharing this screenshot of your Seahunter Unity project.

We’re glad our Legend of Zenda course helped you bring an idea to life!

We hope this inspires you to continue with your course or start a new game with Mammoth Interactive. If we’ve helped you make a project, let us know on Twitter/Facebook. We’d love to feature you!

Click here to watch a demo of Andreas’ Seahunter game

Stay curious!

Your instructors,

Mammoth Interactive

Updates to Unreal Course – Unreal Engine 4

Learn Unreal and C++ as part of our epic bundle Build 177 Games: The Complete 2D, 3D and VR Bundle

1. Receiving errors about Box Component?

Add the line below to the top of the cpp file that is producing errors. #include "Components/BoxComponent.h"

This line creates a reference to access components in the BoxComponent.h script. (Unreal Engine 4.15 requires separate includes.)

2. To get UGameplayStatics to compile you need to add:

#include "Kismet/GameplayStatics.h"

3. I’m getting errors in Visual Studio. What do they mean?

Many times you can ignore Visual Studio’s error messages.

If your project builds successfully in Unreal, you are good to go.

Visual Studio can mark things in the source code as an error even though it’s not.

This occurs because when generating new source files, Unreal can get out of sync with Visual Studio.

To sync Visual Studio with Unreal, go File -> Refresh Visual Studio Project.

Thanks to Mammoth Interactive students James and Chris for helping with this question!

Learn Unreal and C++ as part of our epic bundle Build 177 Games: The Complete 2D, 3D and VR Bundle

What It Took To Adapt Roald Dahl’s Book

Dear Mammoth Interactive students,

We want to share this awesome article From Book To Animation: What It Took To Adapt Roald Dahl’s ‘Revolting Rhymes’​ ​by Ian Failes​.

Most notable is “Blender was used for modelling along with ZBrush and Mudbox for the sculpting and texturing. The chipped-off paint textures on the stage sets were done in Photoshop.”

How exciting! We hope this inspires you to explore more of Blender’s capabilitiesEven if you’re a programmer, learning design will always make your product that much better.

​PS. If we helped you learn Blender, please share your creations with us, post a review, or invite your friends to join you in a Blender course​!

Mammoth Interactive

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