A Deep Learning practitioner is seeking resources such as papers, courses, and tutorials to learn about AI for Materials Science. The goal is to gain sufficient knowledge to conduct meaningful research in the area and contribute to the community, with a UChicago course already identified as a benchmark.
TechAtlas is a tech learning platform featuring an AI chatbot named Atlas for instant coding queries and multilingual support. It emphasizes Generative AI and agentic workflows, offering a simple, distraction-free environment with AI tutoring.
The text describes the "whiteboard test," a rigorous methodology applied in top-tier ML/AI interviews and for self-assessment, ensuring a deep understanding of mathematical concepts. It emphasizes the need to derive concepts from scratch to prove genuine understanding, rather than merely parroting memorized explanations.
EaseLearn AI, an EdTech startup, is backed by major tech companies like AWS, Google, and NVIDIA, along with government entities such as MeitY. Their mission is to provide quality AI education to Indian students through a coalition of partners.
Anthropic is launching five free AI courses on Coursera to help learners worldwide build practical AI skills. This initiative aims to foster AI fluency through accessible educational resources.
The Natural Language Processing Specialization by DeepLearning.AI provides a comprehensive learning path to develop skills in NLP. It covers advanced techniques and practical applications in the field of artificial intelligence.
fast.ai is joining Answer.AI and announcing a new educational experience titled 'How To Solve It With Code'. This initiative aims to provide a novel format for learning programming and AI.
Coursera has launched five new Professional Certificates and expanded its offerings in generative AI and human skills learning, driven by rising demand. The new programs address the growing need for skills such as AI agents, AI-assisted design, and critical thinking, as highlighted in 2026 trends.
This post is a submission for the DEV Education Track, detailing the process of building apps with Google AI Studio. It includes sections on what was built, a demonstration, and the author's experience.
The article critiques common AI education approaches regarding math, advocating for conceptual understanding of how math functions within AI models rather than skipping it or delving into exhaustive proofs. It emphasizes grasping the core mathematical concepts essential for AI, likening it to a plumber understanding water flow without needing deep physics.
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