# Building Machine Learning Systems with TensorFlow English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 44m | 606 MB

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

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.

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
Table of Contents

01 The Course Overview
02 TensorFlow-s Main Data Structure Tensors
03 Handling the Computing Workflow TensorFlow-s Data Flow Graph
04 Basic Tensor Methods
05 How TensorBoard Works
06 Reading Information from Disk
07 Learning from Data Unsupervised Learning
08 Mechanics of k-Means
09 k-Nearest Neighbor
10 Project 1 k-Means Clustering on Synthetic Datasets
11 Project 2 Nearest Neighbor on Synthetic Datasets
12 Univariate Linear Modelling Function
13 Optimizer Methods in TensorFlow The Train Module
14 Univariate Linear Regression
15 Multivariate Linear Regression
16 Logistic Function Predecessor The Logit Functions
17 The Logistic Function
18 Univariate Logistic Regression
19 Univariate Logistic Regression with keras
20 Preliminary Concepts
21 First Project Non-Linear Synthetic Function Regression
22 Second Project Modeling Cars Fuel Efficiency with Non-Linear Regression
23 Third Project Learning to Classify Wines- Multiclass Classification
24 Origin of Convolutional Neural Networks
25 Applying Convolution in TensorFlow
26 Subsampling Operation Pooling
27 Improving Efficiency Dropout Operation
28 Convolutional Type Layer Building Methods
29 MNIST Digit Classification
30 Image Classification with the CIFAR10 Dataset
31 Recurrent Neural Networks
32 A Fundamental Component Gate Operation and Its Steps
33 TensorFlow LSTM Useful Classes and Methods
34 Univariate Time Series Prediction with Energy Consumption Data
35 Writing Music a la Bach
36 Deep Neural Network Definition and Architectures Through Time
37 Alexnet
38 Inception V3
39 Residual Networks (ResNet)
40 Painting with Style VGG Style Transfer
41 Windows Installation
42 mac OS Installation
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