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Literacy and learning
Published 2010Table of Contents: “…Boelé, Elizabeth Swanson -- Persuading students with emotional disabilities to write : a design study -- Margo A. Mastropieri, Thomas E. …”
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What's hot in literacy : exemplar models of effective practice /
Published 2020Table of Contents: “…-- Chapter 8: Sequencing Instruction in Writing Assignments to Build Purpose and Promote Student Engagement -- Chapter 9: Social, Emotional and Cultural Learning in Literacy -- Chapter 10: Re-Conceptualizing Literacy Instruction to Accelerate -- Chapter 11: What's Hot in Literacy? …”
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Cognition and learning in diverse settings
Published 2005Table of Contents: “…Lee Swanson -- Mathematical vs. reading and writing disabilities in deaf children: a pilot study on the development of numerical knowledge / Elisabetta Genovese, Rosalia Galizia, Marco Gubernale, Edoardo Arslan, Daniela Lucangeli -- Instructional support employing spatial abilities: using complimentary cognitive pathways to support learning in students with achievement deficits / William E. …”
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English for Academic CVs, Resumes, and Online Profiles
Published 2019Full-text access
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Data and Decision Sciences in Action Proceedings of the Australian Society for Operations Research Conference 2016 /
Published 2018Table of Contents: “…What Latin Hypercube Is Not -- A BDI-Based Methodology for Eliciting Tactical Decision-Making Expertise -- Analysis of Demand and Operations of Inter-modal Terminals -- Efficient Models, Formulations and Algorithms for Some variants of Fixed Interval Scheduling Problems -- The Value of Flexible Road Designs Through Ecologically Sensitive Areas -- Local Cuts for 0-1 Multidimensional Knapsack Problems -- An Exact Algorithm for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Three-dimensional Loading Constraints -- Automated Techniques for Generating Behavioural Models for Constructive Combat Simulations -- Analytic and Probabilistic Techniques for the Determination of Surface Spray Patterns from Air Bursting Munitions -- Reformulations and Computational Results for the Uncapacitated Single Allocation Hub Covering Problem -- Search Strategies for Problems with Detectable Boundaries and Restricted Level Sets -- Alternative Passenger Cars for the Australian Market: A Cost-Benefit Analysis -- A Quick Practical Guide to Polyhedral Analysis in Integer Programming -- Towards a Feasible Design Space for Proximity Alerts Between Two Aircraft in the Conflict Plane -- Constructing a Feasible Design Space for Multiple-Cluster Conflict and Task-load Assessment -- Open Pit Mine Production Planning and Scheduling: A Research Agenda -- A Comparative Study of Different Integer Linear Programming Approaches for Resource Constrained Project Scheduling Problems -- A Recovery Model for Sudden Supply Delay with Demand Uncertainty and Safety Stock -- Applying Action Research to Strategic Thinking Modelling -- Applying Action Research to Strategic Thinking Modelling -- SimR: Automating Combat Simulation Database Generation -- Battlespace Mobile/Ad Hoc Communication Networks: Performance, Vulnerability and Resilience -- Using Multi-Agent Simulation to Assess the Future Sustainability of Capability -- Application of Field Anomaly Relaxation to Battlefield Casualties and Treatment: A Formal Approach to Consolidating Large Morphological Spaces -- Network Analysis of Decision Loops in Operational Command and Control Arrangements -- Impact of Initial Level and Growth Rate in Multiplicative HW Model on Bullwhip Effect in a Supply Chain -- The P-Median Problem and Health Facilities: Cost Saving and Improvement in Healthcare Delivery through Facility Location -- A Bi-level Mixed Integer Programming Model to Solve the Multi- Servicing Facility Location Problem, Minimising Negative Impacts Due to Existing Semi-Obnoxious Facility -- Can Three Pronouns Discriminate Identity in Writing?.…”
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Deep Learning with Python, Second Edition.
Published 2021Table of Contents: “…6.2.5 Regularize and tune your model -- 6.3 Deploy the model -- 6.3.1 Explain your work to stakeholders and set expectations -- 6.3.2 Ship an inference model -- 6.3.3 Monitor your model in the wild -- 6.3.4 Maintain your model -- Summary -- 7 Working with Keras: A deep dive -- 7.1 A spectrum of workflows -- 7.2 Different ways to build Keras models -- 7.2.1 The Sequential model -- 7.2.2 The Functional API -- 7.2.3 Subclassing the Model class -- 7.2.4 Mixing and matching different components -- 7.2.5 Remember: Use the right tool for the job -- 7.3 Using built-in training and evaluation loops -- 7.3.1 Writing your own metrics -- 7.3.2 Using callbacks -- 7.3.3 Writing your own callbacks -- 7.3.4 Monitoring and visualization with TensorBoard -- 7.4 Writing your own training and evaluation loops -- 7.4.1 Training versus inference -- 7.4.2 Low-level usage of metrics -- 7.4.3 A complete training and evaluation loop -- 7.4.4 Make it fast with tf.function -- 7.4.5 Leveraging fit() with a custom training loop -- Summary -- 8 Introduction to deep learning for computer vision -- 8.1 Introduction to convnets -- 8.1.1 The convolution operation -- 8.1.2 The max-pooling operation -- 8.2 Training a convnet from scratch on a small dataset -- 8.2.1 The relevance of deep learning for small-data problems -- 8.2.2 Downloading the data -- 8.2.3 Building the model -- 8.2.4 Data preprocessing -- 8.2.5 Using data augmentation -- 8.3 Leveraging a pretrained model -- 8.3.1 Feature extraction with a pretrained model -- 8.3.2 Fine-tuning a pretrained model -- Summary -- 9 Advanced deep learning for computer vision -- 9.1 Three essential computer vision tasks -- 9.2 An image segmentation example -- 9.3 Modern convnet architecture patterns -- 9.3.1 Modularity, hierarchy, and reuse -- 9.3.2 Residual connections -- 9.3.3 Batch normalization -- 9.3.4 Depthwise separable convolutions.…”
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