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Optimize Algorithms and achieve greater levels of accuracy with Deep learning.

Course Description

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 course 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.

Learning Outcomes

  • Learn the basics of Deep Learning and Artificial Neural Networks
  • Understand classification and probabilistic predictions with Single-hidden-layer Neural Networks
  • Increase your expertise by covering intermediate and advanced Artificial and Recurrent Neural Networks
  • Get to grips with Convolutional and Deep Belief Networks
  • Learn practical applications of Deep Learning
  • Learn about Feature Engineering and Multicore/Cluster Computing


Familiarity with the theoretical underpinnings of neutral networks is highly useful, this course is appropriate for anyone with prior experience in R and a general familiarity with predictive models.

Who is this course intended for?

This course is for anyone with an interest in creating cutting-edge deep learning models in R.

Your Instructor

Packt Publishing

Packt has been committed to developer learning since 2004. A lot has changed in software since then – but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content – more than 4000 books and video courses -Packt’s mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages to cutting-edge data analytics, and DevOps, Packt takes software professionals in every field to what’s important to them now.

From skills that will help you to develop and future-proof your career to immediate solutions to everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Course Curriculum

Introduction to Deep Learning
The Course Overview 00:00:00
Fundamental Concepts in Deep Learning 00:00:00
Introduction to Artificial Neural Networks 00:00:00
Classification with Two-Layers Artificial Neural Networks 00:00:00
Probabilistic Predictions with Two-Layer ANNs 00:00:00
Working with Neural Network Architectures
Introduction to Multi-hidden-layer Architectures 00:00:00
Tuning ANNs Hyper-Parameters and Best Practices 00:00:00
Neural Network Architectures 00:00:00
Neural Network Architectures (Continued) 00:00:00
Advanced Artificial Neural Networks
The LearningProcess 00:00:00
Optimization Algorithms and Stochastic Gradient Descent 00:00:00
Backpropagation 00:00:00
Hyper-Parameters Optimization 00:00:00
Convolutional Neural Networks
Introduction to Convolutional Neural Networks 00:00:00
Introduction to Convolutional Neural Networks (Continued) 00:00:00
CNNs in R 00:00:00
Classifying Real-World Images with Pre-Trained Models 00:00:00
Recurrent Neural Networks
Introduction to Recurrent Neural Networks 00:00:00
Introduction to Long Short-Term Memory 00:00:00
RNNs in R 00:00:00
Use-Case – Learning How to Spell English Words from Scratch 00:00:00
Towards Unsupervised and Reinforcement Learning
Introduction to Unsupervised and Reinforcement Learning 00:00:00
Autoencoders 00:00:00
Restricted Boltzmann Machines and Deep Belief Networks 00:00:00
Reinforcement Learning with ANNs 00:00:00
Use-Case – Anomaly Detection through Denoising Autoencoders 00:00:00
Applications of Deep Learning
Deep Learning for Computer Vision 00:00:00
Deep Learning for Natural Language Processings 00:00:00
Deep Learning for Audio Signal Processing 00:00:00
Deep Learning for Complex Multimodal Tasks 00:00:00
Other Important Applications of Deep Learning 00:00:00
Advanced Topics
Debugging Deep Learning Systems 00:00:00
GPU and MGPU Computing for Deep Learning 00:00:00
A Complete Comparison of Every DL Packages in R 00:00:00
Research Directions and Open Questions 00:00:00

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