Search Results - university of boston
Suggested Topics within your search.
Suggested Topics within your search.
- Education 6
- General 4
- Administration 3
- Organization & management of education 3
- Research 3
- Education, Higher 2
- English language 2
- Ethics 2
- Fizik 2
- Medical 2
- Physics 2
- School management and organization 2
- Social Science 2
- Social sciences 2
- Academic freedom 1
- Admission 1
- Affirmative action programs 1
- African Americans 1
- Bio-ethics 1
- Bioethics 1
- Child development 1
- China 1
- Climate Sciences 1
- Climatology 1
- Colleges of higher education 1
- Communicable diseases 1
- Conservatism 1
- Design 1
- Early childhood education 1
- Earth Sciences 1
-
1
-
2
-
3
-
4
-
5
-
6
-
7
-
8
-
9
-
10
Gender and the local-global nexus theory, research, and action /
Published 2006Full-text access
View in OPAC
e-Book -
11
Taking life and death seriously bioethics from Japan /
Published 2005Full-text access
View in OPAC
e-Book -
12
-
13
-
14
Emancipatory Climate Actions Strategies from histories /
Published 2019Full-text access
View in OPAC
e-Book -
15
-
16
-
17
-
18
-
19
Deep Learning with Python, Second Edition.
Published 2021Table of Contents: “…4.1.5 Using a trained model to generate predictions on new data -- 4.1.6 Further experiments -- 4.1.7 Wrapping up -- 4.2 Classifying newswires: A multiclass classification example -- 4.2.1 The Reuters dataset -- 4.2.2 Preparing the data -- 4.2.3 Building your model -- 4.2.4 Validating your approach -- 4.2.5 Generating predictions on new data -- 4.2.6 A different way to handle the labels and the loss -- 4.2.7 The importance of having sufficiently large intermediate layers -- 4.2.8 Further experiments -- 4.2.9 Wrapping up -- 4.3 Predicting house prices: A regression example -- 4.3.1 The Boston housing price dataset -- 4.3.2 Preparing the data -- 4.3.3 Building your model -- 4.3.4 Validating your approach using K-fold validation -- 4.3.5 Generating predictions on new data -- 4.3.6 Wrapping up -- Summary -- 5 Fundamentals of machine learning -- 5.1 Generalization: The goal of machine learning -- 5.1.1 Underfitting and overfitting -- 5.1.2 The nature of generalization in deep learning -- 5.2 Evaluating machine learning models -- 5.2.1 Training, validation, and test sets -- 5.2.2 Beating a common-sense baseline -- 5.2.3 Things to keep in mind about model evaluation -- 5.3 Improving model fit -- 5.3.1 Tuning key gradient descent parameters -- 5.3.2 Leveraging better architecture priors -- 5.3.3 Increasing model capacity -- 5.4 Improving generalization -- 5.4.1 Dataset curation -- 5.4.2 Feature engineering -- 5.4.3 Using early stopping -- 5.4.4 Regularizing your model -- Summary -- 6 The universal workflow of machine learning -- 6.1 Define the task -- 6.1.1 Frame the problem -- 6.1.2 Collect a dataset -- 6.1.3 Understand your data -- 6.1.4 Choose a measure of success -- 6.2 Develop a model -- 6.2.1 Prepare the data -- 6.2.2 Choose an evaluation protocol -- 6.2.3 Beat a baseline -- 6.2.4 Scale up: Develop a model that overfits.…”
Full-text access
View in OPAC
e-Book