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R Deep Learning Solutions

Video Description

Powerful, independent videos to build deep learning models in different application areas using R libraries

About This Video

  • Master the intricacies of R deep learning packages such as TensorFlow
  • Learn deep learning in different domains using practical examples from text, image, and speech
  • A guide to set up deep learning models using CPU and GPU

In Detail

Deep learning is the next big thing. It’s a part of machine learning. Its favorable results in applications with huge and complex data is remarkable. R programming language is very popular among data miners and statisticians.

This course will help you resolve problems during the execution of different tasks in deep learning, neural networks, and advanced machine learning techniques. We start with different packages in deep learning, neural networks, and structures. We’ll also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R.

By the end of the course, you’ll have an understanding of deep learning and different deep learning packages so you have the most appropriate solutions for your problems.

Table of Contents

  1. Chapter 1 : Getting Started with Deep Learning
    1. The Course Overview 00:03:10
    2. Installing R and Jupyter 00:06:09
    3. Basics of Machine Learning in R 00:04:17
    4. Installing TensorFlow and H2O in R 00:05:09
  2. Chapter 2 : Deep Learning with R
    1. Performing Logistic Regression Using H2O 00:04:18
    2. Performing Logistic Regression Using TensorFlow 00:03:41
    3. Setting Up a Neural Network Using H2O 00:02:37
    4. Tuning Hyper-Parameters Using Grid Searches in H2O 00:02:49
    5. Setting Up a Neural Network Using TensorFlow 00:04:38
  3. Chapter 3 : Convolution Neural Network
    1. Downloading and Configuring an Image Dataset 00:02:44
    2. Learning the Architecture of a CNN Classifier 00:02:55
    3. Creating New Layers 00:10:01
  4. Chapter 4 : Data Representation Using Autoencoders
    1. Setting Up Autoencoders 00:06:17
    2. Building and Comparing Stochastic Encoders and Decoders 00:04:45
    3. Evaluating the Sparse Decomposition 00:02:52
    4. Learning Manifolds from Autoencoders 00:02:46
    5. Setting Up Denoising Autoencoders 00:03:43
    6. Setting Up Stacked Auto encoders 00:01:45
  5. Chapter 5 : Generative Models in Deep Learning
    1. Comparing PCA with the RBM 00:03:41
    2. Setting Up an RBM for Bernoulli Distribution 00:03:59
    3. Initializing and Starting a New TensorFlow Session 00:02:45
    4. Setting Up an RBM for Collaborative Filtering 00:04:23
    5. Setting Up a Deep Belief Network 00:04:08
    6. Setting Up a Deep Restricted Boltzmann Machine 00:03:28
    7. Implementing a Feed-Forward Backpropagation Neural Network 00:04:11
  6. Chapter 6 : Recurrent Neural Networks
    1. Setting Up a Basic Recurrent Neural Network 00:04:36
    2. Setting Up a Bidirectional RNN Model 00:02:34
    3. Setting Up a Deep RNN Model 00:02:13
    4. Setting Up a Long Short-Term Memory Based Sequence Model 00:03:58
  7. Chapter 7 : Reinforcement Leaning
    1. Setting Up a Markov Decision Process 00:04:07
    2. Performing Model-Based Learning 00:02:26
    3. Performing Model-Free Learning 00:03:10
  8. Chapter 8 : Application of Deep Learning in Text Mining
    1. Preprocessing of Textual Data and Extraction of Sentiments 00:05:01
    2. Analyzing Documents Using tf-idf 00:01:57
    3. Performing Sentiment Prediction Using LSTM Network 00:04:43