1. Introduction To Artificial Neural Network By Zurada Pdf Files Online
2. Introduction To Artificial Neural Network By Zurada Pdf Files Free

Getting Started

Introduction

In the 1980's, the field of artificial neural networks (NN's) [2] was reborn - largely through the promotion of Hopfield and the popularization of backpropagation to train multilayer perceptrons. NN's can be categorized as artificial intelligence.

KANN is a standalone and lightweight library in C for constructing and trainingsmall to medium artificial neural networks such as multi-layerperceptrons, convolutional neural networks and recurrent neuralnetworks (including LSTM and GRU). It implementsgraph-based reverse-mode automatic differentiation and allows to buildtopologically complex neural networks with recurrence, shared weights andmultiple inputs/outputs/costs. In comparison to mainstream deep learningframeworks such as TensorFlow, KANN is not as scalable, but it is closein flexibility, has a much smaller code base and only depends on the standard Clibrary. In comparison to other lightweight frameworks such as tiny-dnn,KANN is still smaller, times faster and much more versatile, supporting RNN,VAE and non-standard neural networks that may fail these lightweightframeworks.

KANN could be potentially useful when you want to experiment small to mediumneural networks in C/C++, to deploy no-so-large models without worrying aboutdependency hell, or to learn the internals of deep learning libraries.

Features

• Flexible. Model construction by building a computational graph withoperators. Support RNNs, weight sharing and multiple inputs/outputs.

• Efficient. Reasonably optimized matrix product and convolution. Supportmini-batching and effective multi-threading. Sometimes faster than mainstreamframeworks in their CPU-only mode.

• Small and portable. As of now, KANN has less than 4000 lines of code in foursource code files, with no non-standard dependencies by default. Compatible withANSI C compilers.

Limitations

• CPU only. As such, KANN is not intended for training huge neuralnetworks.

• Lack of some common operators and architectures such as batch normalization.

• Verbose APIs for training RNNs.

Installation

The KANN library is composed of four files: kautodiff.{h,c} and kann.{h,c}.You are encouraged to include these files in your source code tree. Noinstallation is needed. To compile examples:

This generates a few executables in the examples directory.

Documentations

Comments in the header files briefly explain the APIs. More documentations canbe found in the doc directory. Examples using the library are in theexamples directory.

A tour of basic KANN APIs

Working with neural networks usually involves three steps: model construction,training and prediction. We can use layer APIs to build a simple model:

For this simple feedforward model with one input and one output, we can trainit with:

We can save the model to a file with kann_save() or use it to classify aMNIST image:

Working with complex models requires to use low-level APIs. Please see01user.md for details.

A complete example

This example learns to count the number of '1' bits in an integer (i.e.popcount):

Benchmarks

Introduction To Artificial Neural Network By Zurada Pdf Files Online

• First of all, this benchmark only evaluates relatively small networks, butin practice, it is huge networks on GPUs that really demonstrate the truepower of mainstream deep learning frameworks. Please don't read too much intothe table.

• 'Linux' has 48 cores on two Xeno E5-2697 CPUs at 2.7GHz. MKL, NumPy-1.12.0and Theano-0.8.2 were installed with Conda; Keras-1.2.2 installed with pip.The official TensorFlow-1.0.0 wheel does not work with Cent OS 6 on thismachine, due to glibc. This machine has one Tesla K40c GPU installed. We areusing by CUDA-7.0 and cuDNN-4.0 for training on GPU.

• 'Mac' has 4 cores on a Core i7-3667U CPU at 2GHz. MKL, NumPy and Theano camewith Conda, too. Keras-1.2.2 and Tensorflow-1.0.0 were installed with pip. Onboth machines, Tiny-DNN was acquired from github on March 1st, 2017.

• mnist-mlp implements a simple MLP with one layer of 64 hidden neurons.mnist-cnn applies two convolutional layers with 32 3-by-3 kernels and ReLUactivation, followed by 2-by-2 max pooling and one 128-neuron dense layer.mul100-rnn uses two GRUs of size 160. Both input and output are 2-Dbinary arrays of shape (14,2) -- 28 GRU operations for each of the 30000training samples.

Introduction To Artificial Neural Network By Zurada Pdf Files Free

• In the single thread mode, Theano is about 50% faster than KANN probably dueto efficient matrix multiplication (aka. sgemm) implemented in MKL. As isshown in a previous micro-benchmark, MKL/OpenBLAS can be twice asfast as the implementation in KANN.

• KANN can optionally use the sgemm routine from a BLAS library (enabled bymacro HAVE_CBLAS). Linked against OpenBLAS-0.2.19, KANN matches thesingle-thread performance of Theano on Mul100-rnn. KANN doesn't reduceconvolution to matrix multiplication, so MNIST-cnn won't benefit fromOpenBLAS. We observed that OpenBLAS is slower than the native KANNimplementation when we use a mini-batch of size 1. The cause is unknown.

• KANN's intra-batch multi-threading model is better than Theano+Keras.However, in its current form, this model probably won't get alone well withGPUs.

Jacek M. Zurada, «Introduction to Artificial Neural Systems»
West Publishing Company | ISBN: 0314933913 | October 1992 | File type: PDF | 758 pages | 33.4 mb
The recent resurgence of interest in neural networks has its roots in the recognition that the brain performs computations in a different manner than do conventional digital computers. Computers are extremely fast and precise at executing sequences of instructions that have been formulated for them. A human information processing system is composed of neurons switching at speeds about a million times slower than computer gates. Yet, humans are more efficient than computers at computationally complex tasks such as speech understanding. Moreover, not only humans, but even animals, can process visual information better than the fastest computers.
The question of whether technology can benefit from emulating the computational capabilities of organisms is a natural one. Unfortunately, the understanding of biological neural systems is not developed enough to address the issues of functional similarity that may exist between the biological and man-made neural systems. As a result, any major potential gains derived from such functional similarity, if they exist, have yet to be exploited.
This book introduces the foundations of artificial neural systems. Much of the inspiration for such systems comes from neuroscience. However, we are not directly concerned with networks of biological neurons in this text. Although the newly developed paradigms of artificial neural networks have strongly contributed to the discovery, understanding, and utilization of potential functional similarities between human and artificial information processing systems, many questions remain open. Intense research interest persists and the area continues to develop. The ultimate research objective is the theory and implementation of massively parallel interconnected systems which could process the information with an efficiency comparable to that of the brain.
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