Implementing Deep Learning: Optimized Deep Learning Applications

Machine Learning    |    Intermediate
  • 11 videos | 42m 8s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 46 users Rating 4.0 of 46 users (46)
This 11-video course explores the concepts of computational graphics, interfaces for programming graphics processing units (GPUs), and TensorFlow Extended and its pipeline components. Learners discover features and elements that should be considered for machine learning when building deep learning (DL) models, as well as hyperparameters that can be tuned to optimize DL models. Begin by examining the concept of computational graphs and recognize essential computational graph operations used in implementing DL. Then learn to list prominent processors with specialized purpose and architectures used in implementing DL. Recall prominent interfaces for programming GPUs with focus on Compute Unified Device Architecture (CUDA) and OpenCL, and then take a look at TensorFlow Extended (TFX) and TFX pipeline components for machine learning pipelines. Discover how to setup the TFX environment; use the ExampleGen and StatisticsGen TFX pipeline components to build pipelines; work with TensorFlow Model analysis; and explore the practical considerations for DL build and train. Finally, recall essential hyperparameters of DL algorithms that can be tuned to optimize DL models. The concluding exercise involves optimizing DL applications.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Define the concept of computational graphs and recognize the essential computational graph operations that are used in implementing deep learning
    List the prominent processors with specialized purpose and architectures that are used in implementing deep learning
    Recall the prominent interfaces for programming gpus with focus on cuda and opencl
    Define the concept of tensorflow extended and list the essential tfx pipeline components that can be used to implement machine learning pipelines
    Setup the tensorflow extended environment to build deep learning pipelines
  • Demonstrate how to use the examplegen and statisticsgen tfx pipeline components to build pipelines
    Work with tensorflow model analysis to investigate and visualize the characteristics of datasets and the performances of models
    Recognize the practical features and elements that should be considered when building deep learning models, with focus on baseline model and optimization
    Recall the essential hyperparameters of deep learning algorithms that can be tuned to optimize deep learning models
    Identify components of a computation graph, common gpu frameworks, and tasks that can improve performance with data

IN THIS COURSE

  • 1m 1s
  • 5m 25s
    Learn how to define the concept of computational graphs and recognize the essential computational graph operations that are used to implement deep learning. FREE ACCESS
  • Locked
    3.  Accelerating Architectures
    3m 40s
    Upon completion of this video, you will be able to list the prominent processors with specialized purpose and architectures that are used in implementing deep learning. FREE ACCESS
  • Locked
    4.  GPU Interfaces
    6m 31s
    Upon completion of this video, you will be able to recall the prominent interfaces for programming GPUs with a focus on CUDA and OpenCL. FREE ACCESS
  • Locked
    5.  TFX and Pipeline Components for ML Pipelines
    3m 44s
    In this video, you will learn how to define the concept of TensorFlow Extended and list the essential TFX pipeline components that can be used to implement machine learning pipelines. FREE ACCESS
  • Locked
    6.  Setting up TFX
    3m 53s
    During this video, you will learn how to set up the TensorFlow Extended environment to build deep learning pipelines. FREE ACCESS
  • Locked
    7.  Build TFX Pipeline
    2m 21s
    In this video, you will learn how to use the ExampleGen and StatisticsGen TFX pipeline components to build pipelines. FREE ACCESS
  • Locked
    8.  Using TFMA
    4m 28s
    In this video, you will learn how to work with TensorFlow Model Analysis to investigate and visualize the characteristics of datasets and the performances of models. FREE ACCESS
  • Locked
    9.  Practical Consideration for DL Build and Train
    7m 20s
    After completing this video, you will be able to recognize the practical features and elements that should be considered when building deep learning models, with a focus on the baseline model and optimization. FREE ACCESS
  • Locked
    10.  Deep Learning Parameters
    2m 32s
    After completing this video, you will be able to recall the essential hyperparameters of deep learning algorithms so that you can optimize deep learning models. FREE ACCESS
  • Locked
    11.  Exercise: Optimizing Deep Learning Applications
    1m 14s
    In this video, you will learn how to identify components of a computation graph, common GPU frameworks, and tasks that can improve performance with data. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Prompt Engineering for Machine Learning
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Book Ensemble Methods for Machine Learning
Book Designing Deep Learning Systems: A Guide for Software Engineers

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Fundamentals of AI & ML: Introduction to Artificial Intelligence
Rating 4.4 of 2070 users Rating 4.4 of 2070 users (2070)
Course Excel with Python: Constructing Data Visualizations
Rating 4.2 of 31 users Rating 4.2 of 31 users (31)
Course Developing an AI/ML Data Strategy: The Data Analytics Maturity Model
Rating 4.6 of 260 users Rating 4.6 of 260 users (260)