Building Neural Networks: Artificial Neural Networks Using Frameworks

Neural Networks    |    Intermediate
  • 13 videos | 1h 54m 46s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 31 users Rating 4.5 of 31 users (31)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 1m 40s
  • 8m 2s
    Upon completion of this video, you will be able to list the prominent building blocks involved in building a neural network. FREE ACCESS
  • Locked
    3.  Evolutionary Algorithms and Gradient Descent
    7m 15s
    After completing this video, you will be able to recall the concept and characteristics of evolutionary algorithms, gradient descent, and genetic algorithms. FREE ACCESS
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    4.  Build Neural Networks
    10m 46s
    Learn how to build neural networks using Python and Keras for classification with Tensorflow as the backend. FREE ACCESS
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    5.  Building Neural Networks with PyTorch
    12m 56s
    Find out how to build neural networks using PyTorch. FREE ACCESS
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    6.  Object Image Classification
    8m 32s
    In this video, you will learn how to implement object image classification using neural network algorithms. FREE ACCESS
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    7.  Learning Rates and Deep Learning Optimization
    8m 31s
    Learn how to define and illustrate the use of learning rates to optimize deep learning. FREE ACCESS
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    8.  Optimizing Speed
    8m 4s
    Upon completion of this video, you will be able to describe the various parameters and approaches of optimizing neural network speed. FREE ACCESS
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    9.  Dense Network Tuning Using Hyperas
    14m 18s
    In this video, you will learn how to select hyperparameters and tune for dense networks using Hyperas. FREE ACCESS
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    10.  Linear Model with Estimators
    7m 5s
    In this video, you will build linear models using estimators with the capabilities of TensorFlow. FREE ACCESS
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    11.  Neural Network for Predictions
    7m 19s
    After completing this video, you will be able to specify approaches to implement predictions with neural networks. FREE ACCESS
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    12.  Optimization Approach for Predictions
    3m 43s
    Upon completion of this video, you will be able to describe the temporal and heterogeneous approaches to optimizing predictions. FREE ACCESS
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    13.  Exercise: Build Neural Networks
    16m 35s
    During this video, you will learn how to build a neural network using Python and Keras, tune dense networks using Hyperas, and build a linear model with TensorFlow. FREE ACCESS

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