Building Neural Networks: Artificial Neural Networks Using Frameworks

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

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Overview/Description

This 13-video course helps learners discover how to implement various neural networks scenarios by using Python, Keras, and TensorFlow for machine learning. Learn how to optimize, tune, and speed up the processes of artificial neural networks (ANN) and how to implement predictions with ANN is also covered. You will begin with a look at prominent building blocks involved in building a neural network, then recalling the concept and characteristics of evolutionary algorithms, gradient descent, and genetic algorithms. Learn how to build neural networks with Python and Keras for classification with Tensorflow as the backend. Discover how to build neural networks by using PyTorch; implement object image classification using neural network algorithms; and define and illustrate the use of learning rates to optimize deep learning. Examine various parameters and approaches of optimizing neural network speed; learn how to select hyperparameters and tune for dense networks by using Hyperas; and build linear models with estimators by using the capabilities of TensorFlow. Explore predicting with neural networks, temporal prediction optimization, and heterogenous prediction optimization. The concluding exercise involves building neural networks.

Expected Duration (hours)
1.9

Lesson Objectives

Building Neural Networks: Artificial Neural Networks Using Frameworks

identify the key subject areas covered in this course

list the prominent building blocks involved in building a neural network

recall the concept and characteristics of evolutionary algorithms, gradient descent, and genetic algorithms

build neural networks using Python and Keras for classification with Tensorflow as the backend

build neural networks using PyTorch

implement object image classification using neural network algorithms

define and illustrate the use of learning rates to optimize deep learning

describe the various parameters and approaches of optimizing neural network speed

demonstrate how to select hyperparameters and tune for dense networks using Hyperas

build linear models with estimators using the capabilities of TensorFlow

specify approaches that can be used to implement predictions with neural networks

describe the temporal and heterogenous approaches of optimizing predictions

build a neural network using Python and Keras, tune dense networks using Hyperas, and build a linear model with TensorFlow

Course Number:
it_mlbdnndj_02_enus

Expertise Level
Intermediate