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Optimize Algorithms and achieve greater levels of accuracy with Deep learning

Deep Learning with R

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Optimize Algorithms and achieve greater levels of accuracy with Deep learning

  • Video Duration:04 Hours
  • Cost: $ 124.99

Course Details

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.

Who all can attend

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

What you will learn from this course

  • 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

Course Content

  1. Introduction to Deep Learning
    • The Course Overview
    • Fundamental Concepts in Deep Learning
    • Introduction to Artificial Neural Networks
    • Classification with Two-Layers Artificial Neural Networks
    • Probabilistic Predictions with Two-Layer ANNs

  2. Working with Neural Network Architectures
    • Introduction to Multi-hidden-layer Architectures
    • Tuning ANNs Hyper-Parameters and Best Practices
    • Neural Network Architectures
    • Neural Network Architectures (Continued)

  3. Advanced Artificial Neural Networks
    • The LearningProcess
    • Optimization Algorithms and Stochastic Gradient Descent
    • Backpropagation
    • Hyper-Parameters Optimization

  4. Convolutional Neural Networks
    • Introduction to Convolutional Neural Networks
    • Introduction to Convolutional Neural Networks (Continued)
    • CNNs in R
    • Classifying Real-World Images with Pre-Trained Models

  5. Recurrent Neural Networks
    • Introduction to Recurrent Neural Networks
    • Introduction to Long Short-Term Memory
    • RNNs in R
    • Use-Case – Learning How to Spell English Words from Scratch

  6. Towards Unsupervised and Reinforcement Learning
    • Introduction to Unsupervised and Reinforcement Learning
    • Autoencoders
    • Restricted Boltzmann Machines and Deep Belief Networks
    • Reinforcement Learning with ANNs
    • Use-Case – Anomaly Detection through Denoising Autoencoders

  7. Applications of Deep Learning
    • Deep Learning for Computer Vision
    • Deep Learning for Natural Language Processings
    • Deep Learning for Audio Signal Processing
    • Deep Learning for Complex Multimodal Tasks
    • Other Important Applications of Deep Learning

  8. Advanced Topics
    • Debugging Deep Learning Systems
    • GPU and MGPU Computing for Deep Learning
    • A Complete Comparison of Every DL Packages in R
    • Research Directions and Open Questions

Contact Us

Instructor-led online training is also available for the same course.

For more details, write to us at :

Emailinfo@multisoftvirtualacademy.com

Call(+91) 8130666206/209

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