Search Results - university of boston

A stranger at green knowe / by Boston, Lucky M.

Sears and Zemansky's university physics : with modern physics / by Young, Hugh D.

Trends and issues in instructional design and technology /

Encyclopedia of volcanoes /

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Autonomy in social science research the view from United Kingdom and Australian universities /

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Access and exclusion

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Sears ve Zemansky'nin üniversite fiziği / by Young, Hugh D.

İnterchange : student's book 2 / by Richards, Jack C.

Higher education in a global society achieving diversity, equity and excellence /

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Gender and the local-global nexus theory, research, and action /

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Taking life and death seriously bioethics from Japan /

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Ethics and epidemics

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African American education race, community, inequality and achievement ; a tribute to Edgar G. Epps /

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Emancipatory Climate Actions Strategies from histories / by Delina, Laurence L.

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Bridging the gap between theory, research, and practice the role of child development laboratory programs in early childhood education /

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Administering special education in pursuit of dignity and autonomy /

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Nine Chapters on Mathematical Modernity Essays on the Global Historical Entanglements of the Science of Numbers in China / by Bréard, Andrea

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Teaching leaders to lead teachers educational administration in the era of constant crisis /

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Deep Learning with Python, Second Edition. by Chollet, Francois

Table 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.…”
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