Artificial Intelligence II Hands-On Neural Networks (Java)

Artificial Intelligence II – Hands-On Neural Networks (Java), available at $59.99, has an average rating of 4.35, with 56 lectures, 2 quizzes, based on 498 reviews, and has 5289 subscribers.

You will learn about Basics of neural networks Hopfield networks Concrete implementation of neural networks Backpropagation Optical character recognition This course is ideal for individuals who are This course is recommended for students who are interested in artificial intelligence focusing on neural networks It is particularly useful for This course is recommended for students who are interested in artificial intelligence focusing on neural networks.

Enroll now: Artificial Intelligence II – Hands-On Neural Networks (Java)

Summary

Title: Artificial Intelligence II – Hands-On Neural Networks (Java)

Price: $59.99

Average Rating: 4.35

Number of Lectures: 56

Number of Quizzes: 2

Number of Published Lectures: 55

Number of Published Quizzes: 2

Number of Curriculum Items: 58

Number of Published Curriculum Objects: 57

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of neural networks

Hopfield networks

Concrete implementation of neural networks

Backpropagation

Optical character recognition

Who Should Attend

This course is recommended for students who are interested in artificial intelligence focusing on neural networks

Target Audiences

This course is recommended for students who are interested in artificial intelligence focusing on neural networks

This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21th century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.

Section 1:

what are neural networks

modeling the human brain

the big picture

Section 2:

Hopfield neural networks

how to construct an autoassociative memory with neural networks

Section 3:

what is back-propagation

feedforward neural networks

optimizing the cost function

error calculation

backpropagation and gradient descent

Section 4:

the single perceptron model

solving linear classification problems

logical operators (AND and XOR operation)

Section 5:

applications of neural networks

clustering

classification (Iris-dataset)

optical character recognition (OCR)

smile-detector application from scratch

In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them.

If you are keen on learning methods, let’s get started!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Artificial Intelligence Basics

Lecture 1: Why to learn artificial intelligence and machine learning?

Lecture 2: Types of artificial intelligence learning methods

Chapter 3: Hopfield Neural Network Theory

Lecture 1: Hopfield neural network introduction

Lecture 2: Hopfield network – weights

Lecture 3: Hopfield neural network – Hebbian learning

Lecture 4: Hopfield neural network – energy

Lecture 5: Measuring the energy of the network

Lecture 6: Hopfield neural network example

Chapter 4: Hopfield Neural Network Implementation

Lecture 1: Hopfield network implementation – utils

Lecture 2: Hopfield network implementation – matrix operations

Lecture 3: Hopfield network implementation – network

Lecture 4: Hopfield network implementation – running the algorithm

Chapter 5: Neural Networks With Backpropagation Theory

Lecture 1: Artificial neural networks – inspiration

Lecture 2: Artificial neural networks – layers

Lecture 3: Artificial neural networks – the model

Lecture 4: Why to use activation functions?

Lecture 5: Neural networks – the big picture

Lecture 6: Using bias nodes in the neural network

Lecture 7: How to measure the error of the network?

Lecture 8: Optimization with gradient descent

Lecture 9: Gradient descent with backpropagation

Lecture 10: Backpropagation explained

Lecture 11: Applications of neural networks I – character recognition

Lecture 12: Applications of neural networks II – stock market forecast

Lecture 13: Deep learning

Lecture 14: Types of neural networks

Chapter 6: Single Perceptron Model

Lecture 1: Perceptron model training

Lecture 2: Perceptron model implementation I

Lecture 3: Perceptron model implementation II

Lecture 4: Perceptron model implementation III

Lecture 5: Trying to solve XOR problem

Lecture 6: Conclusion: linearity and hidden layers

Chapter 7: Backpropagation Implementation

Lecture 1: Structure of the feedforward network

Lecture 2: Backpropagation implementation I – activation function

Lecture 3: Backpropagation implementation II – NeuralNetwork

Lecture 4: Backpropagation implementation III – Layer

Lecture 5: Backpropagation implementation IV – run

Lecture 6: Backpropagation implementation V – train

Chapter 8: Logical Operators

Lecture 1: Logical operators introduction

Lecture 2: Running the neural network: AND

Lecture 3: Running the neural network: OR

Lecture 4: Running the neural network: XOR

Chapter 9: Clustering

Lecture 1: Clustering with neural networks I

Lecture 2: Clustering with neural networks II

Chapter 10: Classification – Iris Dataset

Lecture 1: About the Iris dataset

Lecture 2: Constructing the neural network

Lecture 3: Testing the neural network

Lecture 4: Calculating the accuracy of the model

Chapter 11: Optical Character Recognition (OCR)

Lecture 1: Optical character recognition theory

Lecture 2: Installing paint.net

Lecture 3: Transform an image into numerical data

Lecture 4: Creating the datasets

Lecture 5: OCR with neural network

Chapter 12: Course Materials (DOWNLOADS)

Lecture 1: Course materials

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 8 votes

2 stars: 14 votes

3 stars: 61 votes

4 stars: 187 votes

5 stars: 228 votes

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