Summary
2. Distributed Deep Learning for Large-Scale Data
Deep learning for massive amounts of data
Challenges of deep learning for big data
Challenges of deep learning due to massive volumes of data
(first V)
Challenges of deep learning from a high variety of data
(second V)
Challenges of deep learning from a high velocity of data (third
V)
Challenges of deep learning to maintain the veracity of data
(fourth V)
Distributed deep learning and Hadoop
Map-Reduce
Iterative Map-Reduce
Yet Another Resource Negotiator (YARN)
Important characteristics for distributed deep learning design
Deeplearning4j - an open source distributed framework for deep
learning
Major features of Deeplearning4j
Summary of functionalities of Deeplearning4j
Setting up Deeplearning4j on Hadoop YARN
Getting familiar with Deeplearning4j
Integration of Hadoop YARN and Spark for distributed deep
learning
Rules to configure memory allocation for Spark on Hadoop
YARN
Summary
3. Convolutional Neural Network
Understanding convolution
Background of a CNN
Architecture overview
Basic layers of CNN
Importance of depth in a CNN
Convolutional layer
Sparse connectivity
Improved time complexity
Parameter sharing
Improved space complexity
Equivariant representations
