Made in Malaysia

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WHOIS running the Internet : protocol, policy, and privacy by Bruen, Garth O.

Table of Contents: “…27 -- 1.5.1 Elizabeth “Jake” Feinler 27 -- 1.5.2 The ARPANET Directory as Proto‐WHOIS 27 -- 1.5.3 The Site Status List 28 -- 1.5.4 Distribution of the HOSTS Table 30 -- 1.5.5 Finger 30 -- 1.5.6 Sockets 31 -- 1.5.7 Into the VOID with NLS IDENTFILE 32 -- 1.5.8 NAME/FINGER RFC 742 (1977) 33 -- 1.5.9 Other Early Models 35 -- 1.6 1980s: WHOIS Gets Its Own RFC 36 -- 1.6.1 The DNS 37 -- 1.6.2 WHOIS Updated for Domains (1985) 38 -- 1.6.3 Oops! …”
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Hands-On Convolutional Neural Networks with TensorFlow : Solve Computer Vision Problems with Modeling in TensorFlow and Python. by Zafar, Iffat

Table of Contents: “…The sessionSummary; Chapter 2: Deep Learning and Convolutional Neural Networks; AI and ML; Types of ML; Old versus new ML; Artificial neural networks; Activation functions; The XOR problem; Training neural networks; Backpropagation and the chain rule; Batches; Loss functions; The optimizer and its hyperparameters; Underfitting versus overfitting; Feature scaling; Fully connected layers; A TensorFlow example for the XOR problem; Convolutional neural networks; Convolution; Input padding; Calculating the number of parameters (weights); Calculating the number of operations…”
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Deep Learning with Theano. by Bourez, Christopher

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Marketplace Africa. [277], April 08, 2016

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R Deep Learning Projects : Master the techniques to design and develop neural network models in R. by Liu, Yuxi (Hayden)

Table of Contents: “…; Traffic sign recognition using CNN; Getting started with exploring GTSRB; First solution â#x80;#x93; convolutional neural networks using MXNet; Trying something new â#x80;#x93; CNNs using Keras with TensorFlow; Reducing overfitting with dropout; Dealing with a small training set â#x80;#x93; data augmentation; Reviewing methods to prevent overfitting in CNNs; Summary; Chapter 3: Fraud Detection with Autoencoders; Getting ready.…”
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Hands-On Deep Learning with TensorFlow. by Boxel, Dan Van

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American Television News : the Media Marketplace and the Public Interest. by Barkin, Steve M.

Table of Contents: “…Local News; 11. Network News and the New Environment.…”
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A great big story. The original code talkers

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Deep Learning with TensorFlow. by Zaccone, Giancarlo

Table of Contents: “…Multilayer perceptronDNNs architectures; Convolutional Neural Networks; Restricted Boltzmann Machines; Autoencoders; Recurrent Neural Networks; Deep learning framework comparisons; Summary; Chapter 2: First Look at TensorFlow; General overview; What's new with TensorFlow 1.x?…”
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Advanced Machine Learning with Python. by Hearty, John

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Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music by Babcock, Joseph

Table of Contents: “…-- The promise of deep learning -- Building a better digit classifier -- Generating images -- Style transfer and image transformation -- Fake news and chatbots -- Sound composition -- The rules of the game -- Unique challenges of generative models -- Summary -- References -- Chapter 2: Setting Up a TensorFlow Lab -- Deep neural network development and TensorFlow -- TensorFlow 2.0 -- VSCode -- Docker: A lightweight virtualization solution -- Important Docker commands and syntax -- Connecting Docker containers with docker-compose -- Kubernetes: Robust management of multi-container applications -- Important Kubernetes commands -- Kustomize for configuration management -- Kubeflow: an end-to-end machine learning lab -- Running Kubeflow locally with MiniKF -- Installing Kubeflow in AWS -- Installing Kubeflow in GCP -- Installing Kubeflow on Azure -- Installing Kubeflow using Terraform -- A brief tour of Kubeflow's components -- Kubeflow notebook servers -- Kubeflow pipelines -- Using Kubeflow Katib to optimize model hyperparameters -- Summary -- References -- Chapter 3: Building Blocks of Deep Neural Networks -- Perceptrons -- a brain in a function -- From tissues to TLUs -- From TLUs to tuning perceptrons -- Multi-layer perceptrons and backpropagation -- Backpropagation in practice -- The shortfalls of backpropagation -- Varieties of networks: Convolution and recursive -- Networks for seeing: Convolutional architectures -- Early CNNs -- AlexNet and other CNN innovations -- AlexNet architecture.…”
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Machine learning methods for signal, image and speech processing by Jabbar, M. A.

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