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Building Machine Learning Systems with TensorFlow

Video Introducing this tutorial

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
Inception V3
Residual Networks (ResNet)
Painting with Style – VGG Style Transfer

Library Installation and Additional Tips :
Linux Installation
Docker Installation Method
Linux Installation from Source
Windows Installation
MacOS X Installation