TensorFlow: Simple Regression & Classification Models
TensorFlow
| Intermediate
- 19 Videos | 1h 36m 57s
- Includes Assessment
- Earns a Badge
Explore how to how to build and train the two most versatile and ubiquitous types of deep learning models in TensorFlow.
WHAT YOU WILL LEARN
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recognize linear regression problems and extend that to general machine learning problemsrecognize how model parameter training happens via gradient descent to find minimum lossload a dataset and explore its features and labelschoose the right form of data to feed into the linear regression modelbuild a base model for comparison with scikit-learncreate placeholders, training variables, and instantiate optimizers to use with regressiontrain model parameters using a session and the training dataset, and visualize the result with Matplotlibdemonstrate how to interpret the loss and summaries on TensorBoardchoose the high-level Estimator API for common use cases
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train a regression model using the high-level Estimator APIevaluate and predict housing prices using estimatorsidentify classification problems and recall logistic regression for classificationrecognize cross entropy as the loss function for classification problems and use softmax for n-category classificationidentify data as being a continuous range or comprised of categorical valueswork with training and test data to predict heart diseasetrain the high-level estimator for classification and use it for predictiondescribe basic concepts of the linear regression machine learning modeldescribe basic concepts of the binary classification machine learning model
IN THIS COURSE
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1.Course Overview1m 52sUP NEXT
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2.Understanding Linear Regression8m 27s
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3.Gradient Descent and Optimizers4m 42s
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4.Explore the Boston Housing Prices Dataset5m 15s
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5.Creating Training and Test Datasets for Regression8m 13s
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6.Base Model with scikit-learn3m 19s
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7.Setting up the Linear Regression Computation Graph7m 21s
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8.Train and Visualize the Linear Regression Model7m 26s
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9.Visualize the Model with TensorBoard2m 56s
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10.The High-Level Estimator API2m 16s
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11.Linear Regression with Estimators8m 45s
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12.Prediction Using Estimators4m 12s
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13.Understanding Binary Classification3m 58s
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14.The Cross Entropy Loss Function and Softmax4m 13s
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15.Continuous and Categorical Data2m 9s
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16.Creating Training & Test Datasets for Classification8m 25s
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17.Binary Classification Using Estimators4m 42s
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18.Exercise: Working with Linear Regression4m 3s
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19.Exercise: Working with Binary Classification4m 45s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform
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