TensorFlow: Deep Neural Networks & Image Classification Using Estimators
TensorFlow
| Intermediate
- 15 Videos | 1h 11m 50s
- Includes Assessment
- Earns a Badge
Discover how to apply deep learning techniques to images, and how to leverage TensorFlow estimators in building image classification models.
WHAT YOU WILL LEARN
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distinguish between traditional machine learning and deep learningrecognize the architecture and design of a neural networkidentify what is meant by model weights or model parametersidentify the precise operations performed by a neuronrecognize gradient descent as the training process in a neural networkdistinguish between the operations in the forward and backward passes during trainingdescribe how images are fed into a machine learning algorithm
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configure TensorFlow and use Jupyter notebooksload and explore the MNIST dataset for image classificationtrain a deep neural network estimator for image classificationuse an estimator to predict image labelsdescribe why deep neural networks don't work well with images"define how neural networks work "recall basics of image classification using neural networks
IN THIS COURSE
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1.Course Overview1m 52sUP NEXT
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2.Neural Networks and Deep Learning5m 38s
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3.Basic Structure of a Neural Network4m 45s
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4.The Mathematical Function Applied By a Neuron3m 28s
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5.Linear Transformation and Activation Functions6m 51s
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6.Training a Neural Network Using Gradient Descent5m 44s
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7.Forward Pass and Backward Pass3m 28s
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8.Image Representations in Machine Learning6m 50s
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9.Set Up TensorFlow and Use Jupyter Notebooks3m 17s
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10.The MNIST Dataset5m 21s
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11.Training an Estimator for Image Classification7m 27s
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12.Predicting Image Labels4m 30s
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13.Drawbacks of Deep Neural Networks for Images2m 24s
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14.Exercise: Working with Neural Networks6m 27s
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15.Exercise: Working with Image Classification3m 49s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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