Building Machine Learning Systems with TensorFlow [Video]

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Building Machine Learning Systems with TensorFlow [Video]

Rodolfo Bonnin

Engaging projects that will teach you how complex data can be exploited to gain the most insight

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Video Details

ISBN 139781787281806
Course Length2 hours and 44 minutes

Video Description

This video, with the help of practical projects, highlights how TensorFlow can be used in different scenarios—this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Simply pick a project in line with your environment and get stacks of information on how to implement TensorFlow in production.

Style and Approach

This video is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you can use TensorFlow and shows you how to use it in the context of real-world projects. This will not only give you the upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This course opens the door to second- generation machine learning and numerical computation.

Table of Contents

Exploring and Transforming Data
The course Overview
TensorFlow's Main Data Structure – Tensors
Handling the Computing Workflow – TensorFlow's Data Flow Graph
Basic Tensor Methods
How TensorBoard Works?
Reading Information from Disk
Clustering
Learning from Data –Unsupervised Learning
Mechanics of k-Means
k-Nearest Neighbor
Project 1 – k-Means Clustering on Synthetic Datasets
Project 2 – Nearest Neighbor on Synthetic Datasets
Linear Regression
Univariate Linear Modelling Function
Optimizer Methods in TensorFlow – The Train Module
Univariate Linear Regression
Multivariate Linear Regression
Logistic Regression
Logistic Function Predecessor – The Logit Functions
The Logistic Function
Univariate Logistic Regression
Univariate Logistic Regression with keras
Simple FeedForward Neural Networks
Preliminary Concepts
First Project – Non-Linear Synthetic Function Regression
Second Project – Modeling Cars Fuel Efficiency with Non-Linear Regression
Third Project – Learning to Classify Wines: Multiclass Classification
Convolutional Neural Networks
Origin of Convolutional Neural Networks
Applying Convolution in TensorFlow
Subsampling Operation –Pooling
Improving Efficiency – Dropout Operation
Convolutional Type Layer Building Methods
MNIST Digit Classification
Image Classification with the CIFAR10 Dataset
Recurrent Neural Networks and LSTM
Recurrent Neural Networks
AFundamental Component – Gate Operation and Its Steps
TensorFlow LSTM Useful Classes and Methods
Univariate Time Series Prediction with Energy Consumption Data
Writing Music "a la" Bach
Deep Neural Networks
Deep Neural Network Definition and Architectures Through Time
Alexnet
Inception V3
Residual Networks (ResNet)
Painting with Style – VGG Style Transfer
Library Installation and Additional Tips
Windows Installation
MacOS Installation

What You Will Learn

  • Load, interact, dissect, process, and save complex datasets
  • Solve classification and regression problems using state-of-the-art techniques
  • Predict the outcome of a simple time series using Linear Regression modeling
  • Use a Logistic Regression scheme to predict the future result of a time series
  • Classify images using deep neural network schemes
  • Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer
  • Resolve character-recognition problems using the Recurrent Neural Network (RNN) model

Authors

Table of Contents

Exploring and Transforming Data
The course Overview
TensorFlow's Main Data Structure – Tensors
Handling the Computing Workflow – TensorFlow's Data Flow Graph
Basic Tensor Methods
How TensorBoard Works?
Reading Information from Disk
Clustering
Learning from Data –Unsupervised Learning
Mechanics of k-Means
k-Nearest Neighbor
Project 1 – k-Means Clustering on Synthetic Datasets
Project 2 – Nearest Neighbor on Synthetic Datasets
Linear Regression
Univariate Linear Modelling Function
Optimizer Methods in TensorFlow – The Train Module
Univariate Linear Regression
Multivariate Linear Regression
Logistic Regression
Logistic Function Predecessor – The Logit Functions
The Logistic Function
Univariate Logistic Regression
Univariate Logistic Regression with keras
Simple FeedForward Neural Networks
Preliminary Concepts
First Project – Non-Linear Synthetic Function Regression
Second Project – Modeling Cars Fuel Efficiency with Non-Linear Regression
Third Project – Learning to Classify Wines: Multiclass Classification
Convolutional Neural Networks
Origin of Convolutional Neural Networks
Applying Convolution in TensorFlow
Subsampling Operation –Pooling
Improving Efficiency – Dropout Operation
Convolutional Type Layer Building Methods
MNIST Digit Classification
Image Classification with the CIFAR10 Dataset
Recurrent Neural Networks and LSTM
Recurrent Neural Networks
AFundamental Component – Gate Operation and Its Steps
TensorFlow LSTM Useful Classes and Methods
Univariate Time Series Prediction with Energy Consumption Data
Writing Music "a la" Bach
Deep Neural Networks
Deep Neural Network Definition and Architectures Through Time
Alexnet
Inception V3
Residual Networks (ResNet)
Painting with Style – VGG Style Transfer
Library Installation and Additional Tips
Windows Installation
MacOS Installation

Video Details

ISBN 139781787281806
Course Length2 hours and 44 minutes
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