Artificial Intelligence III - Deep Learning in Java

Deep Learning Fundamentals, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) + LSTM, GRUs

4.47 (206 reviews)
Udemy
platform
English
language
Data Science
category
instructor
3,262
students
4 hours
content
Dec 2021
last update
$64.99
regular price

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

Description

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 Java with 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!

Content

Introduction

Introduction
Neural networks and applications

Installing Deep Learning Library

Installing Java
Installing Eclipse
Installing Maven
Cloning the libraries from Github

Multi-Layer Neural Networks

Deep neural networks
Activation functions
Loss functions
Gradient descent / stochastic gradient descent
Hyperparameters

Deep Neural Networks Implementation

Deep neural network implementation - XOR problem
Deep neural network implementation - XOR problem II
Deep neural network implementation - iris dataset
Deep neural network implementation - iris dataset II
ARTICLE: Optimizers Explained (SGD, ADAGrad, ADAM...)

Convolutional Neural Networks Theory

Convolutional neural networks basics
Feature selection
Convolutional neural networks - kernel
Convolutional neural networks - kernel II
Convolutional neural networks - pooling
Convolutional neural networks - flattening
Convolutional neural networks - illustration

Convolutional Neural Networks Implementation

----- DIGIT CLASSIFICATION -----
CNN implementation I - digit classification
CNN implementation II - digit classification
CNN implementation III - digit classification
----- CUSTOM DATASET - FACIAL EXPRESSION -----
Emoji classification I - handling custom datasets
Emoji classification II - the dataset
Emoji classification III - convolutional network
Emoji classification IV - test
ARTICLE: Regularization (L1, L2 and dropout)

Recurrent Neural Networks Theory

Why do recurrent neural networks are important?
Recurrent neural networks basics
Vanishing and exploding gradients problem
Long-short term memory (LTSM) model
Gated recurrent units (GRUs)

Recurrent Neural Networks Implementation

----- TEXT CLASSIFICATION -----
Google's approach: word2vec method
Skip-Gram model fundamentals
Text classification implementation - similar words
----- NLP: SENTIMENT ANALYSIS -----
Sentiment analysis implementation I
Sentiment analysis implementation II
Sentiment analysis implementation III
Sentiment analysis implementation IV

Course Materials (DOWNLOADS)

Course materials

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Screenshots

Artificial Intelligence III - Deep Learning in Java - Screenshot_01Artificial Intelligence III - Deep Learning in Java - Screenshot_02Artificial Intelligence III - Deep Learning in Java - Screenshot_03Artificial Intelligence III - Deep Learning in Java - Screenshot_04

Reviews

SANJEEV
September 23, 2023
Very good course. One of the best courses on deep learning with java. Would like to see more such courses with DJL library.
Matthias
August 4, 2022
The courses by the Holczer Balacz are superb. I started with his course on cryptocurrency and anded up with is lecures on AI - time well spent!
Aldo
May 28, 2022
En cuanto a la teoría, me gustó que menciona/explica los conceptos y/o las matemáticas que se aplican sin ahondar demasiado en ellas. Me hubiera gustado un ejemplo de face recognition real, tendré que hacer el mío basado en el ejemplo del emoji. Creo que es porque no es el propósito del curso conocer a fondo la librería DL4J pero al menos agregaría un par de clases respecto a cargar redes de keras, guardar la red neuronal... No hay muchos cursos DL4J por ahí y no me gustaría ver toda la documentación en la librería por mi mismo para aprender redes neuronales en java así que agradezco que existe este curso al alcance en udemy.
Michael
October 1, 2021
The class is teaching me some good information although I wanted a little more in-depth information about some of the networks
Conor
April 15, 2021
Informative, though I don't think Java is the best language to demonstrate the application of this sort of material
HWANG
January 20, 2021
This course is very useful to understand from theory to implementation. The examples are great and the explanation is very clear. I recommend this course!
R
May 19, 2020
The concepts regarding CNN and RNN have been explained well in this course with pointers to reference material. Explanation of code snippets and the configuration is very good. Overall an excellent course.
Francesco
December 14, 2019
Quite good overall learning experience. Good and clear explanation. Sometimes the same concept (and related slide) is explained multiple times among videos. The described Java library has been updated on the online repository and the course is not too useful in relation to provided examples, anymore; fortunately the used library is available in Course Materials. I sincerely expected videos about unsupervised learning, also. "lots of lots of ... " repeated too many times. A video title has to be corrected: 'Intalling Java'. Wrongly spoken english sometimes (words with 'w' letter, 'conjunction', 'stochastic').
Inspiros
January 27, 2019
I need more courses like this. You have not mentioned complex and novel structures. Give me Artificial Intelligence V, VI, VII, VIII, ... 4 is definitely not enough to build Google AlphaGo.
Avishai
April 16, 2018
interesting for students.I would suggest to lay emphasis on preparing a portfolio to help students create their brand in DL, AI and machine learning. Industrial application also can make this course get more value and recognized by the industry. But nice work from the instructor
Michael
February 14, 2018
I have taken numerous courses offered by Holczer. Every course I learn more than I would expect. His lectures are unique and have an idiosyncratic tone to it. Highly recommend this instructor to anyone willing to learn.

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1462912
udemy ID
12/8/2017
course created date
11/21/2019
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