Last updated 11/2017MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: 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, -series, text, audio) Load, interact, dissect, process, and save complex datasets Predict the outcome of a simple 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 perfog 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 Eeering. 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 eeer and PhD student at Universidad Tecnologica 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 Eeering and IT organizations. Prior to joining GLG, Will was the executive vice president of technology and eeering 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 Series Prediction with Energy Consumption Data Lecture 54 Writing Music "a la" Bach Lecture 55 Deep Neural Network Definition and Architectures Through 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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