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Learning Path Tensorflow Machine & Deep Learning Solutions

Learning Path Tensorflow Machine & Deep Learning Solutions
Last updated 11/2017
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 476.56 MB | Duration: 5h 23m
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 OSX)
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.


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