Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data.
This tutorial will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. You will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN’s and RNN’s. You will deep dive into Convolutional Neural Networks and Unsupervised Learning. You will also learn about the applications of Deep Learning in various fields and understand the practical implementations of Scalability, HPC and Feature Engineering.
Starting out at a basic level, users will be learning how to develop and implement Deep Learning algorithms using R in real world scenarios.
About the Author
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: A comparison between CNNs and HTMs on object recognition tasks".
The main objective is to understand the fundamental concepts and key features that make it so special and different from the classical Machine Learning approach.
The goal of this video is to learn more about Artificial Neural Networks and their vast world of variations, explore the basic architectures of ANNs in detail and talk about their possible implementations in R.
Applying what you have learned about the Multilayer Perceptron algorithm to a real-world application, which classifies handwritten digits in images.
To get probabilistic predictions using Artificial Neural Networks and specifically in the context of a multi-class classification problem.
To add multiple hidden layers to the basic Multilayers Perceptron algorithm in order to build more complex models of the world and increase the accuracy of our predictions.
The goal of this video is to learn the best practices for tuning the hyper-parameters of an ANN and being able to generalize well on the data we have never seen before. This would be the latest essential skill to acquire in order to get the best out of our ANN solution.
The goal of this video is to learn more about Multi-hidden-layer Neural Networks and how to use them in order to solve the practical problem of classifying handwritten digits within the R language.
The goal of this video is to apply what we have learned about Multi-hidden-layer ANNs to a new real-world problem and get more confidence in the use of the H2O package.
To explore with more details, the most common algorithm to minimize the loss function called Stochastic Gradient Descent.
The goal of this video is to understand how to actually learn the weights of our Deep Learning model using Stochastic Gradient Descent through Backpropagation, the standard way of computing the gradient for Artificial Neural Networks.
To get to tune the hyper-parameters automatically in order to minimize the error on the validation set.
This first video, will be an introduction to the fundamental concepts behind Convolutional Neural Networks. The main objective of this video is to motivate their use highlighting the differences from classical feed-forward neural networks.
The goal of this video is to learn more about Convolutional Neural Networks, concluding our dissertation on the layer-wise structure of a CNN and understand how to design architecture suitable for your specific problem.
The aim of this video is to understand how to actually implement CNNs in R, and use it to solve real-world problems.
The goal of this video is to learn about the concept of transfer Learning, and how we can use and exchange DL pre-trained models to solve even new tasks with a very tiny computational overhead.
The aim of this video is to introduce the fundamental concepts behind Recurrent Neural Networks. The main objective is to underline their main differences from classical feed-forward neural networks and CNNs.
The aim of this video is to learn more about a specific type of Recurrent Neural Networks, called Long Short-Term Memories, a natural extension of classical RNNs for dealing with long-term dependencies.
The aim of this video is to understand how to actually implement RNNs in R, and use it to solve real-world problems.
The aim of this video is to learn how to train and use an LSTM to solve a complex problem like predicting the next character in a sentence given the occurrences of the previous characters.
The aim of this video is to understand the main differences from classical supervised learning and how they can be combined together.
In this video, you will learn more about a specific unsupervised learning algorithm called Autoencoders. This type of Artificial Neural Networks are simple and effective solutions for learning efficient representation of data without any supervision.
The aim of this video is to get a quick picture on the main approaches for solving reinforcement learning tasks with Deep Learning.
The aim of this video is to learn how to train and use an Autoencoders in R with the H2O package, for solving a real-world anomaly detection task.
The aim of this video is to spark new inspiration for creatively applying Deep Learning techniques to real-world problems in Computer Vision.
The aim of this video is to creatively apply Deep Learning techniques to real-world problems in Natural Language Processing (NLP).
In this video, we'll creatively apply Deep Learning techniques to real-world problems in ASP.
The aim of this video is to introduce some of the most successful applications of Deep Learning for complex multimodal tasks.
In this video, let's take a look at some of the most successful applications in other fields we didn't mention before.
The aim of this first video is to learn how to deal with models which do not behave as they should.
In this video, you will learn how to speed-up the training and deploy complex DL models.
The aim of this video is to present a complete overview on every available R package for Deep Learning and Neural Networks.
In this video, you will learn about the most interesting research directions and open question for the long-term developments of Deep Learning toward truly intelligent agents.
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