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Deep Learning with R

Video Description

Optimize Algorithms and achieve greater levels of accuracy with Deep learning

About This Video

  • Explore and create intelligent systems using Deep learning techniques

  • Understand the usage of multiple applications like Natural Language Processing, Bioinformatics, Recommendation Engines, etc. where deep learning models are implemented

  • Get hands on with various Deep Learning scenarios and get mind blowing insights from your data

  • In Detail

    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.

    Table of Contents

    1. Chapter 1 : Introduction to Deep Learning
      1. The Course Overview 00:05:22
      2. Fundamental Concepts in Deep Learning 00:07:43
      3. Introduction to Artificial Neural Networks 00:07:58
      4. Classification with Two-Layers Artificial Neural Networks 00:08:03
      5. Probabilistic Predictions with Two-Layer ANNs 00:06:33
    2. Chapter 2 : Working with Neural Network Architectures
      1. Introduction to Multi-hidden-layer Architectures 00:04:31
      2. Tuning ANNs Hyper-Parameters and Best Practices 00:06:12
      3. Neural Network Architectures 00:04:58
      4. Neural Network Architectures Continued 00:08:02
    3. Chapter 3 : Advanced Artificial Neural Networks
      1. The LearningProcess 00:05:36
      2. Optimization Algorithms and Stochastic Gradient Descent 00:08:11
      3. Backpropagation 00:06:44
      4. Hyper-Parameters Optimization 00:07:18
    4. Chapter 4 : Convolutional Neural Networks
      1. Introduction to Convolutional Neural Networks 00:09:57
      2. Introduction to Convolutional Neural Networks Continued 00:10:36
      3. CNNs in R 00:10:41
      4. Classifying Real-World Images with Pre-Trained Models 00:08:29
    5. Chapter 5 : Recurrent Neural Networks
      1. Introduction to Recurrent Neural Networks 00:11:58
      2. Introduction to Long Short-Term Memory 00:08:08
      3. RNNs in R 00:08:55
      4. Use-Case – Learning How to Spell English Words from Scratch 00:06:35
    6. Chapter 6 : Towards Unsupervised and Reinforcement Learning
      1. Introduction to Unsupervised and Reinforcement Learning 00:06:45
      2. Autoencoders 00:04:57
      3. Restricted Boltzmann Machines and Deep Belief Networks 00:07:45
      4. Reinforcement Learning with ANNs 00:07:23
      5. Use-Case – Anomaly Detection through Denoising Autoencoders 00:06:53
    7. Chapter 7 : Applications of Deep Learning
      1. Deep Learning for Computer Vision 00:07:20
      2. Deep Learning for Natural Language Processing 00:06:05
      3. Deep Learning for Audio Signal Processing 00:05:02
      4. Deep Learning for Complex Multimodal Tasks 00:04:32
      5. Other Important Applications of Deep Learning 00:05:24
    8. Chapter 8 : Advanced Topics
      1. Debugging Deep Learning Systems 00:05:56
      2. GPU and MGPU Computing for Deep Learning 00:04:57
      3. A Complete Comparison of Every DL Packages in R 00:04:41
      4. Research Directions and Open Questions 00:04:48