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Our new report offers schools and districts a set of concrete recommendations for designing family engagement strategies that impact student learning and well-being. It is based on six years of our surveys with families and educators, well-established research on effective practice, and case studies from schools and districts across the country.

The Deep Learning textbook is a resource intended to help studentsand practitioners enter the field of machine learning in generaland deep learning in particular.The online version of the book is now complete and will remainavailable online for free.

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If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to thewebsite, do not hesitate to contact the authors directly by e-mailat: feedback@deeplearningbook.org

The UDL Guidelines are a tool used in the implementation of Universal Design for Learning. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

The UDL Guidelines are a tool used in the implementation of Universal Design for Learning, a framework to improve and optimize teaching and learning for all people based on scientific insights into how humans learn. Learn more about the Universal Design for Learning framework from CAST. The UDL Guidelines can be used by educators, curriculum developers, researchers, parents, and anyone else who wants to implement the UDL framework in a learning environment. These guidelines offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.

This update will focus specifically on addressing systemic barriers that result in inequitable learning opportunities and outcomes. CAST aims to develop a transparent, inclusive, and community-driven process. If you are interested in collaborating and staying updated on our progress, we invite you to complete a brief survey.

TLA works to create equitable access to, accelerated adoption of, and system-level learning about innovative learning strategies and models. In addition to offering implementation resources for educators, TLA creates and openly offers:

More and more learning materials are being released under Creative Commons licenses, which allows them to be shared, used, and remixed freely. These Open Educational Resources (OER) now feature an increasing number of digital curricula.

Students work harder and dream bigger when their learning connects with them and connects them to the world. Linked Learning is a proven approach that engages youth, transforms systems, and advances equity.

Whether you're looking to start a small business or expand your current one, SBA's digital learning platform has everything you need to educate yourself on entrepreneurial best practices and available financing options.

Through the EDUCAUSE Teaching and Learning Program, you can find the tools, resources, and peer connections you need to advance learning through the innovative application of technology across your campus.

To better serve our teaching and learning community in 2024 and beyond, EDUCAUSE is making some exciting changes to our related programming and events. Read this letter from Kathe Pelletier, Director of the EDUCAUSE Teaching and Learning Program, to learn more about what's to come. Read on to learn abou the changes >>

Establishing effective principles and practices for teaching and learning can help your college or university anticipate and adjust to the unique needs of your students, faculty, and staff; support learners and successful learning; and transform student learning and engagement.

In turn, establishing these principles and practices can help you determine the evidence that supports continuous improvement of teaching and learning, identify barriers to student success, and discover uses of learning technology that are replicable across institutions and disciplines.

Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools. This trusted AI learning platform is designed for responsible AI applications in machine learning.

Streamline the entire large language model lifecycle and model management with native MLOps capabilities. Securely run machine learning anywhere with enterprise-grade security. Mitigate model biases and evaluate models with the Responsible AI dashboard.

Build deep-learning models in tools such as Visual Studio Code and Jupyter Notebooks, using flexible frameworks such as PyTorch or TensorFlow. Azure Machine Learning is compatible with ONNX Runtime and DeepSpeed to optimize training and inference.

Streamline the deployment and management of thousands of models in multiple environments using MLOps. Deploy and score ML models faster with fully managed endpoints for batch and real-time predictions. Use repeatable pipelines to automate workflows for continuous integration and continuous delivery (CI/CD). Share and discover machine learning artifacts across multiple teams for cross-workspace collaboration using registries and managed feature store. Continuously monitor model performance metrics, detect data drift, and trigger retraining to improve model performance.

Put security first across the machine learning lifecycle using the built-in data governance in Microsoft Purview. Take advantage of the comprehensive security capabilities spanning identity, data, networking, monitoring, and compliance, all tested and validated by Microsoft. Secure solutions using custom role-based access control, virtual networks, data encryption, private endpoints, and private IP addresses. Train and deploy models anywhere, from on premises to multicloud, to meet data sovereignty requirements. Govern with confidence using built-in policies and compliance with 60 certifications, including FedRAMP High and HIPAA.

Evaluate machine learning models with reproducible and automated workflows to assess model fairness, explainability, error analysis, causal analysis, model performance, and exploratory data analysis. Make real-life interventions with causal analysis in the Responsible AI dashboard and generate a scorecard at deployment time. Contextualize responsible AI metrics for both technical and non-technical audiences to involve stakeholders and streamline compliance review.

Learn more about machine learning on Azure and participate in hands-on tutorials with a 30-day learning journey. By the end, you'll be prepared to take the Azure Data Scientist Associate Certification.

Azure Machine Learning studio is the top-level resource for Machine Learning. This capability provides a centralized place for data scientists and developers to work with all the artifacts for building, training, and deploying machine learning models.

The Learning Tools Interoperability (LTI) specification allows learning management systems (LMS) or platforms to integrate remote tools and content in a standard way. LTI 1.3 builds on previous versions by improving the authentication security model.

The term "active learning" is often used to describe an interactive process, such as doing a hands-on experiment to learn a concept rather than reading about it. But "passive learning" (reading a text, listening to a lecture, watching a movie) is still learning, and can be effective.

Support learning and memory by getting enough sleep. Research shows that sleep helps the brain consolidate information, so make it a priority to practice good sleep hygiene for a healthy body and brain.

Cellini N, Torre J, Stegagno L, Sarlo M. Sleep before and after learning promotes the consolidation of both neutral and emotional information regardless of REM presence. Neurobiol Learn Mem. 2016;133:136-144. doi:10.1016/j.nlm.2016.06.015

Connected learning combines personal interests, supportive relationships, and opportunities. It is learning in an age of abundant access to information and social connection that embraces the diverse backgrounds and interests of all young people.

During the summer, low-income students lose ground compared to their wealthier peers. But summer can also be a time to help level the playing field through high-quality, summer learning programs that research shows produce measurable benefits in math, reading and social and emotional learning.

While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and machine learning. You'll see how these two technologies work, with useful examples and a few funny asides.

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, affordable data storage.

Get in-depth instruction and free access to SAS software to build your machine learning skills. Courses include: 14 hours of course time, 90 days of free software access in the cloud and a flexible e-learning format, with no programming skills required.

Underlying flawed assumptions can lead to poor choices and mistakes, especially with sophisticated methods like machine learning. Skip others' mistakes with this advice from a machine learning expert.

Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data and for fraud prevention and detection. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud.