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Join me for a FREE Webinar: Training Tips that Work for Nonprofits on Jan.29th I’ll be sharing my best tips and secrets for designing and delivering training for nonprofit professionals that get results. ” ADDIE is an instructional design method that stands for Analysis, Design, Development, Implementation, and Evaluation.
I’m co-facilitating a session on Nonprofit Training Design and Delivery with colleagues John Kenyon, Andrea Berry, and Cindy Leonard at the NTEN Nonprofit Technology Conference on Friday March 14th at 10:30 am! There are two different methods to evaluate your training. Use LearningTheory. Formative Evaluation.
As a trainer and facilitator who works with nonprofit organizations and staffers, you have to be obsessed with learningtheory to design and deliver effective instruction, have productive meetings, or embark on your own self-directed learning path. Here’s some examples.
Personalized learning experiences Personalized and adaptive learning is gaining traction in the L&D space, which is a welcome change from the traditional ‘one-size-fits-all’ approach. Video-based micro-learning Video learning has been one of the most impactful ways to deliver online training.
Personalized learning experiences Personalized and adaptive learning is gaining traction in the L&D space, which is a welcome change from the traditional ‘one-size-fits-all’ approach. Video-based micro-learning Video learning has been one of the most impactful ways to deliver online training.
Personalized learning experiences Personalized and adaptive learning is gaining traction in the L&D space, which is a welcome change from the traditional ‘one-size-fits-all’ approach. Video-based micro-learning Video learning has been one of the most impactful ways to deliver online training.
Concretely, this research agenda involves answering questions such as: What is the right method for expressing goals and instructions to AI systems? Which specification learning algorithms are guaranteed to converge to a good specification? What kinds of training data do we need, and how much data do we need? And so on.
Kochenderfer Contact : philhc@stanford.edu Links: Paper Keywords : deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Authors : Jeff Z.
The third and final class of tasks I look at in this paper is a new category of objectives that I refer to as modal objectives, where the agent is given an instruction expressed not just in terms of what does happen along a given trajectory, but also in terms of what could happen.
*Backdoors and other alignment stress tests: Past research has implanted backdoors in safety-trained LLMs and tested whether standard alignment techniques are capable of catching or removing them. Were interested in techniques like latent adversarial training and circuit breaking that might succeed where standard adversarial training falters.
Beth has over 30 years working in the nonprofit sector in technology, training, capacity building, evaluation, fundraising and marketing. Beth is an expert in facilitating online and offline peer learning, curriculum development based on traditional adult learningtheory and other instructional approaches.
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