Artificial Intelligence III Deep Learning in Java

Artificial Intelligence III – Deep Learning in Java, available at $64.99, has an average rating of 4.75, with 46 lectures, 3 quizzes, based on 219 reviews, and has 3435 subscribers.

You will learn about Understands deep learning fundamentals Understand convolutional neural networks (CNNs) Implement convolutional neural networks with DL4J library in Java Understand recurrent neural networks (RNNs) Understand the word2vec approach This course is ideal for individuals who are Anyone who wants to understand deep learning, convolutional neural networks and recurrent neural networks in Java It is particularly useful for Anyone who wants to understand deep learning, convolutional neural networks and recurrent neural networks in Java.

Enroll now: Artificial Intelligence III – Deep Learning in Java

Summary

Title: Artificial Intelligence III – Deep Learning in Java

Price: $64.99

Average Rating: 4.75

Number of Lectures: 46

Number of Quizzes: 3

Number of Published Lectures: 46

Number of Published Quizzes: 3

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understands deep learning fundamentals

Understand convolutional neural networks (CNNs)

Implement convolutional neural networks with DL4J library in Java

Understand recurrent neural networks (RNNs)

Understand the word2vec approach

Who Should Attend

Anyone who wants to understand deep learning, convolutional neural networks and recurrent neural networks in Java

Target Audiences

Anyone who wants to understand deep learning, convolutional neural networks and recurrent neural networks in Java

This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Javawith the deeplearning4j library. The last chapters are about recurrent neural networks and the applications – natural language processing and sentiment analysis!

So you’ll learn about the following topics:

Section #1:

multi-layer neural networks and deep learning theory

activtion functions (ReLU and many more)

deep neural networks implementation

how to use deeplearning4j (DL4J)

Section #2:

convolutional neural networks (CNNs) theory and implementation

what are kernels (feature detectors)?

pooling layers and flattening layers

using convolutional neural networks (CNNs) for optical character recognition (OCR)

using convolutional neural networks (CNNs) for smile detection

emoji detector application from scratch

Section #3:

recurrent neural networks (RNNs) theory

using recurrent neural netoworks (RNNs) for natural language processing (NLP)

using recurrent neural networks (RNNs) for sentiment analysis

These are the topics we’ll consider on a one by one basis.

You will get lifetime access to over 40+ lectures!

This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you’ll get your money back. 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

Chapter 3: Installing Deep Learning Library

Lecture 1: Installing Java

Lecture 2: Installing Eclipse

Lecture 3: Installing Maven

Lecture 4: Cloning the libraries from Github

Chapter 4: Deep Neural Networks Theory

Lecture 1: Deep neural networks

Lecture 2: Activation functions

Lecture 3: Loss functions

Lecture 4: Gradient descent / stochastic gradient descent

Lecture 5: Hyperparameters

Lecture 6: Mathematical formulation of deep neural networks

Chapter 5: Deep Neural Networks Implementation

Lecture 1: Deep neural network implementation – XOR problem

Lecture 2: Deep neural network implementation – XOR problem II

Lecture 3: Deep neural network implementation – iris dataset

Lecture 4: Deep neural network implementation – iris dataset II

Chapter 6: Convolutional Neural Networks (CNNs) Theory

Lecture 1: Convolutional neural networks basics

Lecture 2: Feature selection

Lecture 3: Convolutional neural networks – kernel

Lecture 4: Convolutional neural networks – kernel II

Lecture 5: Convolutional neural networks – pooling

Lecture 6: Convolutional neural networks – flattening

Lecture 7: Convolutional neural networks – illustration

Lecture 8: Mathematical formulation of convolution neural networks

Chapter 7: Convolutional Neural Networks (CNNs) Implementation – Digit Classification

Lecture 1: CNN implementation I – digit classification

Lecture 2: CNN implementation II – digit classification

Lecture 3: CNN implementation III – digit classification

Chapter 8: Convolutional Neural Networks (CNNs) Implementation – Smile Detect

Lecture 1: Emoji classification I – handling custom datasets

Lecture 2: Emoji classification II – the dataset

Lecture 3: Emoji classification III – convolutional network

Lecture 4: Emoji classification IV – test

Chapter 9: Recurrent Neural Networks (RNNs) Theory

Lecture 1: Why do recurrent neural networks are important?

Lecture 2: Recurrent neural networks basics

Lecture 3: Vanishing and exploding gradients problem

Lecture 4: Long-short term memory (LSTM) model

Lecture 5: Gated recurrent units (GRUs)

Lecture 6: Mathematical formulation of recurrent neural networks

Chapter 10: Recurrent Neural Networks (RNNs) Implementation

Lecture 1: Googles approach: word2vec method

Lecture 2: Skip-Gram model fundamentals

Lecture 3: Text classification implementation – similar words

Lecture 4: Sentiment analysis implementation I

Lecture 5: Sentiment analysis implementation II

Lecture 6: Sentiment analysis implementation III

Lecture 7: Sentiment analysis implementation IV

Chapter 11: Course Materials (DOWNLOADS)

Lecture 1: Course materials

Instructors

Holczer Balazs
Software Engineer

Rating Distribution

1 stars: 2 votes

2 stars: 6 votes

3 stars: 23 votes

4 stars: 88 votes

5 stars: 100 votes

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