AI and deep learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. It is the technology behind self-driven cars, intelligent personal assistant computers, and decision support systems. Deep learning algorithms are being used across a broad range of industries. As the fundamental driver of AI, being able to tackle deep learning with Java is going to be a vital and valuable skill, not only within the tech world, but also for the wider global economy that depends upon knowledge and insight for growth and success.
You will learn how to install the environment, where Git is used as version control, Eclipse or IntelliJ as an IDE, and mostly Gradle with a little bit of Maven as a build tool. You will learn how to use the DL4J and apply deep learning to a range of real-world use cases. You will then be introduced to Neural networks and later you will learn how to implement them. You will also be given an insight about various deep learning algorithms. You will then be trained to tune Apache Spark.
By the end of the video course, you’ll be ready to tackle deep learning with Java. Wherever you’ve come from—whether you’re a data scientist or Java developer—you will become a part of the deep learning revolution!
About the Author
Sercan Karaoglu gained his BSc in Mathematics Engineering at Istanbul Technical University. Karaoglu also completed a Research and Development project at age 23, at Foreks, in collaboration with The Scientific and Technological Research Council of Turkey(TUBITAK). This project was related to the application of Artificial Neural Networks in Financial Trading Decision Support Systems and Market Simulation for Intraday and Daily Trading.
Currently, he develops High Throughput-Low Latency Reactive Microservices and Reactive Stream applications at work and researches the topics of Deep Learning and Machine Learning. He is Java Software Engineer at the Dissemination Department of Foreks Information Systems, which is one of the leading IT companies in Turkey’s financial sector. It has specialized in software that is directly integrated with financial professionals and Istanbul Stock Market for over 26 years.
He is currently studying for his MSc in Computer Engineering at Bahcesehir University in the field of Big Data Analytics and Management.
The aim of this video is to set up your environment for the course. Therefore we are going to install necessary software such as JDK, Gradle, Git, and IntelliJ.
The aim of this video is to show how we can manage dependencies before creating the project, and take advantage of both GPU and CPU.
The aim of this video to show how to take advantage of GPUs for deep learning algorithms.
This video shows you what classification and clustering are and how they can be implemented in Java using industry standard framework deeplearning4j.
Learn one of the most important building block in Neural Networks, which is Activation Functions, in specific Softmax Function.
This problem occurs when we need to build a model where there are continuous to continuous variables such as when we have a bunch of housing data, which includes room size location and its price tag, in this case, there is nothing to classify instead predicting the true price for the house based on its features.
This video deals with binary classification problem such as identifying if something is right or wrong, 1 or 0, yes or no, and so on. It is a simple decision making process.
This video tells you about one of the most important building block neural networks, which is optimizers, in specific, Gradient Descent. Because Neural Networks are not just black boxes and one cannot just take and use it without understanding the underlying concept it is very important for you to watch and understand fundamental concepts.
Up to now, we talked about some linear models. These are the simplest models and works okay for most cases. However, sometimes there is a nonlinear relationship between features and the target values. In this section, we are going to introduce how we solve when nonlinear relationship is seen in the data.
This video explains the first and simplest type of neural net that is used for a lot of classification task. It is an abstraction for both the single layer perceptron and multilayer perceptron.
Recurrent Neural Networks are solution for predicting the flowing data like time series, sound, natural language, movie frame etc.
Recurrent Neural Networks are good at sequence modelling; however, sometimes they tend to remember only recent events and forget about the past events. So, in this case, we are going to use Long Short Term Memory (LSTM).
Convolutional Neural Networks are useful where we want to train a network to recognize patterns that aren't tied to specific location in the image. This also allows us to save a lot of parameters compared to the fully connected layer and helps to reduce over fitting.
This video introduces a neural network that is used for dimension reduction, feature selection, and feature extraction.
The aim of this video is to introduce you to one of the popular deep learning algorithms that is used in the recommender systems.
Neural networks have something called as the hyper-parameter space, which means they have a lot of parameters to tune, which affect the model dramatically. Therefore in this video the goal is to give you some tips and tricks about parameter tuning.
Neural networks have something called hyper parameter space which means they have a lot of parameters to tune which affect the model dramatically. Therefore, in this video the goal is to give you some tips and tricks about parameter tuning.
Understand regularization method called early stopping that prevents neural network from over training.
The aim of this video is to show how to test and evaluate models and how to do that using deeplearning4j.
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