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

 

Mastery of Artificial Intelligence in Unity for 2D and 3D Games

Do you want to learn how to use AI in games? Learn now with our beginner courses. Anyone can become a next generation game developer with these powerful tools!

All are 98% off today!

1. Learn Unity AI by Making a Tank Game​

Learn artificial intelligence, use the a star algorithm, & code in C#. Make an awesome 2D tank game.

Make a Pathfinding Game in Unity with A* AI

Welcome to Mammoth Interactive’s A* course with Glauco Pires. You will learn how to make a game that uses artificial intelligence.

Get a coupon

Take your first steps in AI here.

You will make a path-finding algorithm called ‘A star.’ You can use A* in many different platforms, programming languages and more.

This course’s topic is bulletproof knowledge.

You will learn how to use the A* algorithm to make a 2D game in Unity. A Super Tank on a maze will find the best way to go to a point you click. The tank will collect objects along its path.

Meet your teacher Glauco Pires

Glauco has a decade’s experience in game development. He makes games in UnityUnreal, and HTML languages. He works with languages like C#, C++, and JavaScript.

  • straightforward coding skills
  • clean development techniques
  • thoughtful developer advice

With Glauco you will learn to make games in the most efficient and cleanest way possible.

Get a coupon

Why you need artificial intelligence in games

With artificial intelligence, you can make your games more…

  • complex
  • random
  • interesting
  • valuable

…without putting in more effort thanks to algorithms.

Old games – the very first computer games – were simple and straightforward.

These days, you must make more complex games. Players want to believe they are playing against something complex, something lifelike.

The power of the A* algorithm

The A* is the base algorithm for path finding. A* is artificial intelligence that will find a path. This algorithm has existed for decades.

A* gets one agent (intelligent being) and takes it from point A to Point B. A* finds an optimal way to move. In real life, this power is useful for airplanes and cars.

A* is also important to avoid dangers like a cliff while getting to a destination. As well – suppose a game’s level has two paths. You can program your artificial intelligence player to think on its own. It can choose a better path to avoid monsters and other obstacles.

You must learn to use the A* algorithm. You will become a better game developer.

Requirements

Enroll now

The power of this algorithm will push your games to the next level.

Learn to be a technical and creative thinker. Glauco is an innovative instructor who gets great reviews.

This offer won’t last forever – sign up now to meet Glauco

2. Make a Starship Unity Game Powered by Artificial Intelligence

Mine a unique world using NavMesh AI. Make a 3D pathfinding game with C#.

Welcome to our Steering Behaviors course for Unity game development.

Implement realistic agent movement while making a 2D Unity game! In this course you will learn one of many aspects of artificial intelligence.

Get a coupon

This course was funded by a wildly successful Kickstarter

You will learn to make game elements behave like real-life beings. Your on-screen agents will move more smoothly than ever before.

Make and code a better game.

How can you make game characters move with realism, intelligence and little effort from you? Enroll in this course to learn everything you need to know to start using Steering Behaviors in your own games.

With Steering Behaviors enemies and players alike will follow automatic paths or seek pre-set positions in a smooth manner. These cool game behaviors allow characters to take smooth turns, slow down, speed up – you name it. Unity Steering Behaviors handle steering and movement.

Any game with motion needs this behavior to become better and stand out from competition.

We will use this behavior by making a minimalistic game where you must dodge enemies for as long as you can. Sign up now for this course.

Learn by doing in this practical course.

You will make a colorful 2D space dodger game where you play as a simple spaceship gliding around a level. Explore path following behaviors for the Unity game engine. Steer, flee, avoid obstacles, follow the leader and more.

Get a coupon

The possibilities are endless.

Enroll now

3. Make a Starship Unity Game Powered by Artificial Intelligence

Mine a unique world using NavMesh AI. Make a 3D pathfinding game with C#.

Explore with a smart character. Learn to build a spaceship on a planet.

Welcome to Mammoth Interactive’s NavMesh course with Glauco. In this course you will program a spaceship in a three-dimensional game.

Get a coupon

With artificial intelligence your ship character will learn to explore a planet. The ship will travel around craters, rocks, aliens and buildings in a 3D world.

You will make a good pathfinding system to find the best path for the player to navigate to wherever you click.

A navmesh is the perfect solution for navigating any space. You will use a navigation mesh or ‘navmesh’ to add artificial intelligence to your game. Your characters will move intelligently through levels and scenes.

Push your games to stand out.

Pathfinding logic makes a game looks real. You must learn to use NavMeshes. You will become a better game developer.

4. Unity Machine Learning with Python

Teach a sled controlled by artificial intelligence to catch falling Christmas presents!

Learn to work in an exciting area of computer science and artificial intelligence.

In this course we will train an artificial brain to make the game work. No matter where the present falls, the computer will know exactly how get it.

Click to get a coupon

Make an AI Christmas game!

Our Unity game will have a holiday setting featuring a sled. Presents will fall from the sky. The goal will be to move the sled to catch the falling present. The game will need no human interaction.

Enroll now to study with 5/5 star-rated instructor Glauco.

To catch a present present in the game, you as a player will not have to do any work! We will teach the computer to recognize the present’s location.

While making a simple scene, we will learn many settings and adjust programs. The result will be fantastic.

Get a coupon

Get rewards by supporting our Kickstarter.

2018 is the year you finally crush your New Year’s resolution and learn to code.

Start off 2018 on the right foot by learning how to code. If you pledge for this Kickstarter you will not only learn to code, you will learn how to build machine learning models from scratch. This bundle of courses is a must have if you want to keep your skills and knowledge relevant in today’s fast moving world.

This year is your year. Crush your New Year’s resolution by pledging today. There are limited seats so don’t miss out!

PLEDGE NOW

Self-driving cars. Artificial intelligence. Genomics. And you.

What do these have in common? They use machine learning, the technology shaping our future. Machine learning isn’t a magical concept exclusive to top-level researchers – you can learn it, too!

Machine learning is changing the world around us. It’s bringing us self-driving cars, facial recognition and artificial intelligence. ML began on computers, but the next big wave is machine learning for mobile. Have you ever thought: why can’t my mobile device do more?

We from Mammoth Interactive are here to tell you that your Android and iOS apps can become smarter, stronger and more convenient thanks to machine learning. Better yet, we’ll show you how to build your very own intelligent software that grows with you.

Pledge today to get a massive online course on machine learning (ML), a subfield of Artificial Intelligence (AI) on how computers learn from experience to improve how they react in the future.

PLEDGE NOW

Anyone – yes, including you – can learn to create data analysis that allows computers to learn from data and get smarter without extra programming.

Who is this for?

If you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you.

No prior knowledge is required. We will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners.

PLEDGE NOW

Insights into the future of the workplace.

Read below some key takeaways from a recent global survey from Dimension Data

The Digital Workplace Report looks into how organisations are evolving from a traditional office environment to a digital workplace. They surveyed 850 business and IT leaders who are driving changes within their companies around the world.

¼ of businesses predict workspace analytics tools, augmented reality tools, and micro-training will have a role in the office environment within the year.

62% expect virtual advisors to have a place in their companies in the next 2 years. These virtual assistants are bots in applications that use artificial intelligence and machine learning.

of organisations need support from external partners for workplace technology solutions. require significant support, where third-party partners hold a central role.

53% of organisations report that smart meeting rooms that provide workers with access to conferencing technologies are central to significantly improving business processes.

On the list of tech trends important in determining workspace strategy, #1 is “Embracing consumerisation of IT for collaboration and productivity apps.

IT directors are closely involved in business strategy. They have the most influence in driving workstyle changing in ¼ of surveyed organisations.

The traditional office is not dead. 63% of enterprises indicate that most or all of their employees work in a standard office. This is barely expected to change in the next 2 years.

Open offices are a trend, which include unassigned seating, open-plan design, and informal meeting spaces.

To read more, grab a copy of the report here

Want to learn AI for yourself? Check out our Core Image course:

Mastering Core Image: XCode’s Image Framework