Course details

Building Neural Networks: Artificial Neural Networks Using Frameworks

Building Neural Networks: Artificial Neural Networks Using Frameworks


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



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