SkillSoft Explore Course – Lambers, Inc.

BUY NOW! STARTING AT ONLY $299!

IT Professional Curricula Software Development Solution Area Software Development Principles AI Development with TensorFlow

Explore the concept of machine learning in TensorFlow, including TensorFlow installation and configuration, the use of the TensorFlow computation graph, and working with building blocks.

Asset ID	it_sdaidt_01_enus
Course Type	Video Course
Course Category	Software Development Solution Area

Objectives
TensorFlow: Introduction to Machine Learning Course Overview describe kinds of machine learning algorithms and their use cases define the training and prediction phases in machine learning define the conceptual differences between traditional machine learning and deep learning compare and contrast supervised and unsupervised techniques in machine learning define the advantages and challenges in using TensorFlow for machine learning distinguish data and computations as distinct building blocks of a TensorFlow computation graph choose the right way to install TensorFlow based on the user's environment install TensorFlow and work with Jupyter Notebooks specify constants and build and run a computation graph use TensorBoard to visualize the computation graph build and execute a computation graph with variables and placeholders visualize variables and placeholders on TensorBoard recognize how variables are trainable parameters and can be updated within a session work with feed dictionaries to input data to placeholders during training use named scopes to group computations specify and work with eager execution for prototyping and development recall basic concepts of machine learning and TensorFlow build and execute computation graphs with computation nodes and data

Objectives

TensorFlow: Introduction to Machine Learning

Course Overview
describe kinds of machine learning algorithms and their use cases
define the training and prediction phases in machine learning
define the conceptual differences between traditional machine learning and deep learning
compare and contrast supervised and unsupervised techniques in machine learning
define the advantages and challenges in using TensorFlow for machine learning
distinguish data and computations as distinct building blocks of a TensorFlow computation graph
choose the right way to install TensorFlow based on the user's environment
install TensorFlow and work with Jupyter Notebooks
specify constants and build and run a computation graph
use TensorBoard to visualize the computation graph
build and execute a computation graph with variables and placeholders
visualize variables and placeholders on TensorBoard
recognize how variables are trainable parameters and can be updated within a session
work with feed dictionaries to input data to placeholders during training
use named scopes to group computations
specify and work with eager execution for prototyping and development
recall basic concepts of machine learning and TensorFlow
build and execute computation graphs with computation nodes and data