Learning Path: Tensorflow: Machine & Deep Learning Solutions

Harness the power of machine and deep learning of TensorFlow with ease

What you'll learn

Deep diving into training, validating, and monitoring training performance

Set up and run cross-sectional examples (images, time-series, text, audio)

Load, interact, dissect, process, and save complex datasets

Predict the outcome of a simple time series using linear regression modeling

Resolve character-recognition problems using the recurrent neural network model

Work with Docker and Keras

Requirements

This Learning Path takes a step-by-step approach, helping you explore all the functioning of TensorFlow.

Description

Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. So if you’re looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path.


Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.






The highlights of this Learning Path are:






Setting up TensorFlow for actual industrial use, including high-performance setup aspects like multi-GPU support
Embedded with solid projects and examples to teach you how to implement TensorFlow in production
Empower you to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage





Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with TensorBoard – all within an example-rich context using problems from multiple industries. The focus is towards introducing new concepts through problems which are coded and solved over the course of each video. You will then learn how to implement TensorFlow in production. Each project in this Learning Path 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. Finally, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using TensorFlow.
On completion of this Learning Path, you will have gone through the full lifecycle of a TensorFlow solution with a practical demonstration to system setup, training, validation, to creating pipelines for real world data – all the way to deploying solutions into a production settings.

Meet Your Expert:
We have the best works of the following esteemed authors to ensure that your learning journey is smooth:
Shams Ul Azeem is an undergraduate of NUST Islamabad, Pakistan in Electrical Engineering. He has a great interest in computer science field and started his journey from android development. Now he’s pursuing his career in machine learning, particularly in deep learning by doing medical related freelance projects with different companies. He was also a member of RISE lab, NUST and has a publication in IEEE International Conference, ROBIO as a co-author on “Designing of motions for humanoid goal keeper robots”.

Rodolfo Bonnin a systems engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued Parallel Programming and Image Understanding postgraduate courses at Uni Stuttgart, Germany. He has done research on high-performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU and GPU supporting the neural network feedforward stage. More recently he's been working in the field of fraud pattern detection with neural networks, and is currently working on signal classification using ML techniques.


Will Ballard serves as chief technology officer at GLG and is responsible for the Engineering and IT organizations. Prior to joining GLG, Will was the executive vice president of technology and engineering at Demand Media. He graduated Magna Cum Laude with a BS in Mathematics from Claremont McKenna College.


Overview

Section 1: Machine Learning with TensorFlow

Lecture 1 The Course Overview

Lecture 2 Introducing Deep Learning

Lecture 3 Installing TensorFlow on Mac OS X

Lecture 4 Installation on Windows – Pre-Reqeusite Virtual Machine Setup

Lecture 5 Installation on Windows/Linux

Lecture 6 The Hand-Written Letters Dataset

Lecture 7 Automating Data Preparation

Lecture 8 Understanding Matrix Conversions

Lecture 9 The Machine Learning Life Cycle

Lecture 10 Reviewing Outputs and Results

Lecture 11 Getting Started with TensorBoard

Lecture 12 TensorBoard Events and Histograms

Lecture 13 The Graph Explorer

Lecture 14 Our Previous Project on TensorBoard

Lecture 15 Fully Connected Neural Networks

Lecture 16 Convolutional Neural Networks

Lecture 17 Programming a CNN

Lecture 18 Using TensorBoard on Our CNN

Lecture 19 CNN Versus Fully Connected Network Performance

Section 2: Building Machine Learning Systems with TensorFlow

Lecture 20 The Course Overview

Lecture 21 TensorFlow's Main Data Structure – Tensors

Lecture 22 Handling the Computing Workflow – TensorFlow's Data Flow Graph

Lecture 23 Basic Tensor Methods

Lecture 24 How TensorBoard Works?

Lecture 25 Reading Information from Disk

Lecture 26 Learning from Data –Unsupervised Learning

Lecture 27 Mechanics of k-Means

Lecture 28 k-Nearest Neighbor

Lecture 29 Project 1 – k-Means Clustering on Synthetic Datasets

Lecture 30 Project 2 – Nearest Neighbor on Synthetic Datasets

Lecture 31 Univariate Linear Modelling Function

Lecture 32 Optimizer Methods in TensorFlow – The Train Module

Lecture 33 Univariate Linear Regression

Lecture 34 Multivariate Linear Regression

Lecture 35 Logistic Function Predecessor – The Logit Functions

Lecture 36 The Logistic Function

Lecture 37 Univariate Logistic Regression

Lecture 38 Univariate Logistic Regression with skflow

Lecture 39 Preliminary Concepts

Lecture 40 First Project – Non-Linear Synthetic Function Regression

Lecture 41 Second Project – Modeling Cars Fuel Efficiency with Non-Linear Regression

Lecture 42 Third Project – Learning to Classify Wines: Multiclass Classification

Lecture 43 Origin of Convolutional Neural Networks

Lecture 44 Applying Convolution in TensorFlow

Lecture 45 Subsampling Operation –Pooling

Lecture 46 Improving Efficiency – Dropout Operation

Lecture 47 Convolutional Type Layer Building Methods

Lecture 48 MNIST Digit Classification

Lecture 49 Image Classification with the CIFAR10 Dataset

Lecture 50 Recurrent Neural Networks

Lecture 51 A Fundamental Component – Gate Operation and Its Steps

Lecture 52 TensorFlow LSTM Useful Classes and Methods

Lecture 53 Univariate Time Series Prediction with Energy Consumption Data

Lecture 54 Writing Music "a la" Bach

Lecture 55 Deep Neural Network Definition and Architectures Through Time

Lecture 56 Alexnet

Lecture 57 Inception V3

Lecture 58 Residual Networks (ResNet)

Lecture 59 Painting with Style – VGG Style Transfer

Lecture 60 Windows Installation

Lecture 61 MacOS Installation

Section 3: Tensorflow Deep Learning Solutions for Images

Lecture 62 The Course Overview

Lecture 63 Installing Docker

Lecture 64 The Machine Learning Dockerfile

Lecture 65 Sharing Data

Lecture 66 Machine Learning REST Service

Lecture 67 MNIST Digits

Lecture 68 Tensors: Just Multidimensional Arrays

Lecture 69 Turning Images into Tensors

Lecture 70 Turning Categories into Tensors

Lecture 71 Classical/Dense Neural Network

Lecture 72 Activation and Non Linearity

Lecture 73 Softmax

Lecture 74 Training and Testing Data

Lecture 75 Dropout and Flatten

Lecture 76 Solvers

Lecture 77 Hyperparameters

Lecture 78 Grid Search

Lecture 79 Convolutions

Lecture 80 Pooling

Lecture 81 Convolutional Neural Network

Lecture 82 Deep Neural Network

Lecture 83 REST API Definition

Lecture 84 Trained Models in Docker Containers

Lecture 85 Making Predictions

This Learning Path is aimed at data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results using TensorFlow.