Building Neural Networks: Artificial Neural Networks Using Frameworks
Neural Networks
| Intermediate
- 13 Videos | 2h 16s
- Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview1m 40sUP NEXT
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2.Neural Network Building Components8m 2s
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3.Evolutionary Algorithms and Gradient Descent7m 15s
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4.Build Neural Networks10m 46s
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5.Building Neural Networks with PyTorch12m 56s
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6.Object Image Classification8m 32s
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7.Learning Rates and Deep Learning Optimization8m 31s
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8.Optimizing Speed8m 4s
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9.Dense Network Tuning Using Hyperas14m 18s
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10.Linear Model with Estimators7m 5s
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11.Neural Network for Predictions7m 19s
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12.Optimization Approach for Predictions3m 43s
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13.Exercise: Build Neural Networks16m 35s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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