Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta and Josh Reini. A data agent extracts data from sources such as files or databases, and then analyzes it and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free! https://hubs.la/Q03KP1KH0
DeepLearning.AI
Software Development
Mountain View, California 1,264,576 followers
Making world-class AI education accessible to everyone
About us
DeepLearning.AI is making a world-class AI education accessible to people around the globe. DeepLearning.AI was founded by Andrew Ng, a global leader in AI.
- Website
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http://DeepLearning.AI
External link for DeepLearning.AI
- Industry
- Software Development
- Company size
- 11-50 employees
- Headquarters
- Mountain View, California
- Type
- Privately Held
- Founded
- 2017
- Specialties
- Artificial Intelligence, Deep Learning, and Machine Learning
Products
DeepLearning.AI
Online Course Platforms
Learn the skills to start or advance your AI career | World-class education | Hands-on training | Collaborative community of peers and mentors.
Locations
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Primary
400 Castro St
Ste 600
Mountain View, California 94041, US
Employees at DeepLearning.AI
Updates
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Prototyping with GenAI doesn’t need weeks of planning. In just 48 hours, you can go from a blank notebook to a working demo that generates real feedback. In this article, we share a playbook for building fast prototypes with Streamlit and Snowflake, based on lessons from our course taught by Chanin Nantasenamat. 👉 Read the blog: https://hubs.la/Q03K_s9g0"
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Illinois became the second U.S. state, after Nevada, to ban AI apps from administering psychotherapy without a doctor’s direct participation. The Wellness and Oversight for Psychological Resources Act prohibits marketing chatbots as therapeutic tools, bars clinicians from using AI to make treatment decisions or assess patients’ mental state, requires informed consent for recorded or transcribed sessions, and restricts AI use to administrative tasks only. Violations can draw fines up to $10,000 per use. Learn more in The Batch: https://hubs.la/Q03KRXDY0
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Alibaba released Qwen3-Next-80B-A3B in Base, Instruct, and Thinking variants under an open-weights Apache 2.0 license, targeting faster long-context inference. The 80-billion-parameter mixture-of-experts design swaps most vanilla attention layers for Gated DeltaNet ones and the rest for gated attention. The models are trained on a 15 trillion token subset of the Qwen3 dataset and fine-tuned with GSPO. They offer multi-token prediction and support inputs up to 262,144 tokens (or longer with modifications). Learn more in The Batch: https://hubs.la/Q03KsQf90
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A private school in Austin replaced the 6-hour teaching day with 2 hours of personalized, AI-guided lessons. Alpha School plans to open more classrooms in 12 cities. Its proprietary platform sequences mastery-based exercises in math, science, reading, and language skills. The pedagogy avoids chatbots, tracks engagement via camera, targets 70–95 percent performance, and integrates education content from apps like IXL, Khan Academy, and Trilogy Software. Learn more in The Batch: https://hubs.la/Q03J-2-Z0
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On November 14, 2025, join Andrew Ng and DeepLearning.AI in New York City for the second edition of the AI Developer Conference, built by and for developers. Learn directly from technical teams at top-tier companies like Anthropic, Amazon Web Services (AWS), Genspark, Snowflake, Arm, Neo4j, and CodeRabbit. Check out the full list of partners and speakers here: https://hubs.la/Q03K747x0 More to be announced! 🎫 Limited regular-priced tickets remaining. Reserve yours today before pricing moves to the next tier! Purchase link in the comments.
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DeepLearning.AI reposted this
I'll be speaking at this year's AI Dev 25 x NYC on November 14. Hope to see some of you there for some amazing speakers, talks, and panels. https://lnkd.in/g4mC_mPC #deeplearningai #aitalks #aidev25 #coderabbit
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This week, in The Batch, Andrew Ng discusses how automated software testing works when using AI coding agents, which can introduce subtle bugs that are hard to detect, especially in back-end infrastructure code Plus: ⚡ Alibaba updates Qwen3 with faster 80B MoE models 🧠 U.S. states ban AI-only psychotherapy 🔄 Energy-Based Transformers (EBTs) refine each token step by step Read The Batch: https://hubs.la/Q03K6qy20
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In a long-awaited antitrust ruling, a U.S. federal judge ordered Google to give eligible U.S.-based AI and search rivals a one-time copy of its web index and to syndicate its search results on existing partner terms. The court declined to order divestitures or otherwise break up Google. Google will keep Chrome and Android and may continue paying Apple and other partners for default search placement, but cannot require exclusivity. Learn more in The Batch: https://hubs.la/Q03JZQj80
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DeepLearning.AI reposted this
Enterprise data extraction just got exponentially simpler. Most organizations process unstructured documents the hard way: custom OCR scripts, complex prompts, and one-off integrations for every file type. Each new data source means more engineering time, more maintenance, and more fragility. Get the latest insights from Box CTO Ben Kus on this DeepLearning.AI course with Andrew Ng. Here's what changed: Box's Model Context Protocol server now handles the heavy lifting. Instead of building custom connectors, AI models can directly access, understand, and extract from any document format—without downloading files or writing extraction logic. The implications are significant. A recent project transformed invoice processing from a 3-week custom development cycle into a 2-hour configuration task. Same accuracy, fraction of the complexity. This reflects a broader shift toward protocol-based AI integration. Rather than the traditional M×N integration problem (every model needs a custom connector to every tool), we're moving to M+N standardization where tools speak a common language. We explore this architectural evolution in depth in our new course with DeepLearning.AI, including hands-on implementation of multi-agent systems that leverage these protocols for enterprise-scale document processing. For organizations still building point-to-point data extraction solutions, consider how standardized protocols might simplify your architecture. The engineering hours saved often justify the platform investment within the first quarter. How is your organization thinking about standardizing AI tool integration? Are you seeing similar complexity challenges with unstructured data workflows? Sign up today: https://lnkd.in/gpfmt679