Announcing my new course: Agentic AI! Building AI agents is one of the most in-demand skills in the job market. This course, available now at deeplearning.ai, teaches you how. You'll learn to implement four key agentic design patterns: - Reflection, in which an agent examines its own output and figures out how to improve it - Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc. - Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and - Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns. But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents. Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices. This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps. Come join me, and let's build some agentic AI systems! Sign up to get started: deeplearning.ai/courses/agen…

AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful. This is why I increasingly try to select software that lets me control my own data, so I can make it available to my AI agents. Because of AI’s growing capabilities, the value you can now create from “connecting the dots” between different pieces of data is higher than ever. For example, if an email click is logged in one vendor’s system and a subsequent online purchase is logged in a different one, then it is valuable to build agents that can access both of these data sources to see how they correlate to make better decisions. Unfortunately, many SaaS vendors try to create a data silo in their customer’s business. By making it hard for you to extract your data, they create high switching costs. This also allows them to steer you to buy their AI agent services — sometimes at high expense and/or of low quality — rather than build your own or buy from a different vendor. Unfortunately, some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you (and your AI agents) to efficiently access it. One of my teams just told me that a SaaS vendor we have been using to store our customer data wants to charge over $20,000 for an API key to get at our data. This high cost — no doubt intentionally designed to make it hard for customers to get their data out — is adding a barrier to implementing agentic workflows that take advantage of that data. Through AI Aspire (an AI advisory firm), I advise a number of businesses on their AI strategies. When it comes to buying SaaS, I often advise them to try to control their own data (which, sadly, some vendors mightily resist). This way, you can hire a SaaS vendor to record and operate on your data, but ultimately you decide how to route it to the appropriate human or AI system for processing. Over the past decade, a lot of work has gone into organizing businesses’ structured data. Because AI can now process unstructured data much better than before, the value of organizing your unstructured data (including PDF files, which LandingAI’s Agentic Document Extraction specializes in!) is higher than ever before. In the era of generative AI, businesses and individuals have important work ahead to organize their data to be AI-ready. P.S. As an individual, my favorite note-taking app is Obsidian. I am happy to “hire” Obsidian to operate on my notes files. And, all my notes are saved as Markdown files in my file system, and I have built AI agents that read from or write to my Obsidian files. This is a small example of how controlling my own notes data lets me do more with AI agents! [Original text: deeplearning.ai/the-batch/is… ]

.@roelofbotha is one of the investors I've come to most respect, and I learned something new every time I spoke with him. Even beyond his leadership of @sequoia, his influence on how investors think is hard to overstate. His passing on the baton is the end of an era!

Wrong tag - I’m so sorry. This is co-taught with the wonderful Brian Granger @ellisonbg !

AI coding just arrived in Jupyter notebooks - and @brganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free): deeplearning.ai/short-course…

Haha! Good one.

DeepLearning.AI Pro is now generally available -- this is the one membership that keeps you at the forefront of AI. Please join! There has never been a moment when the distance between having an idea and building it has been smaller. Things that required months of work for teams can now be built by individuals using AI, in days. This is why we built DeepLearning.AI Pro. I'm personally working hard on this membership program to help you to build applications that can launch or accelerate your career, and shape the future of AI. DeepLearning.AI Pro gives you full access to 150+ programs, including my recently launched Agentic AI course, the new Post-Training and PyTorch courses by Sharon Zhou and Laurence Moroney (just released this week), and all of DeepLearning.AI's top courses and professional certificates. All course videos remain free. Pro membership adds hands-on learning: labs to build working systems, practice questions to hone your understanding, and certificates to share your skills. I'm also building new tools to help you create AI applications and grow your career (and have fun doing so!). Many will be available first to Pro members. Try out DeepLearning.AI Pro free, and let me know what you build! Join here: learn.deeplearning.ai/member…

In hindsight the importance of GPUs for AI was something we really got right!

An exciting new professional certificate: PyTorch for Deep Learning taught by @lmoroney is now available at DeepLearning.AI. This is the definitive program for learning PyTorch, which is one of the main frameworks researchers use to build breakthrough AI systems. If you want to understand how modern deep learning models work—or build your own custom architectures—PyTorch gives you direct control over the key aspects of model development. This three-course professional certificate takes you from fundamentals through advanced architectures and deployment: Course 1: PyTorch: Fundamentals - Learn how PyTorch represents data with tensors and how datasets fit into the training process. You'll build and train neural networks step by step, monitor training progress, and evaluate performance. By the end, you'll understand PyTorch's workflow and be ready to design, train, and test your own models. Course 2: PyTorch: Techniques and Ecosystem Tools - Master hyperparameter optimization, model profiling, and workflow efficiency. You'll use learning rate schedulers, tackle overfitting, and apply automated tuning with Optuna. Work with TorchVision for visual AI and Hugging Face for NLP. Learn transfer learning and fine-tune pretrained models for new problems. Course 3: PyTorch: Advanced Architectures and Deployment - Build sophisticated architectures including Siamese Networks, ResNet, DenseNet, and Transformers. Learn how attention mechanisms power modern language models and how diffusion models generate images. Prepare models for deployment with ONNX, MLflow, pruning, and quantization. Skills you'll gain: - Build and optimize neural networks in PyTorch—the framework researchers use to create breakthrough models - Fine-tune pretrained models for computer vision and NLP tasks—adapting existing models to solve your specific problems - Implement transformer architectures and work with diffusion models, the core technologies behind ChatGPT and modern image generation - Optimize models with quantization and pruning to make them fast and efficient for real-world deployment Whether you want to use pre-existing models, build your own custom models, or just understand what's happening under the hood of the systems you use, this specialization will give you that foundation. Start learning PyTorch: deeplearning.ai/courses/pyto…

An exciting new course: Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training, taught by @realSharonZhou, VP of AI at @AMD. Available now at DeepLearning.AI. Post-training is the key technique used by frontier labs to turn a base LLM--a model trained on massive unlabeled text to predict the next word/token--into a helpful, reliable assistant that can follow instructions. I've also seen many applications where post-training is what turns a demo application that works only 80% of the time into a reliable system that consistently performs. This course will teach you the most important post-training techniques! In this 5 module course, Sharon walks you through the complete post-training pipeline: supervised fine-tuning, reward modeling, RLHF, and techniques like PPO and GRPO. You'll also learn to use LoRA for efficient training, and to design evals that catch problems before and after deployment. Skills you'll gain: - Apply supervised fine-tuning and reinforcement learning (RLHF, PPO, GRPO) to align models to desired behaviors - Use LoRA for efficient fine-tuning without retraining entire models - Prepare datasets and generate synthetic data for post-training - Understand how to operate LLM production pipelines, with go/no-go decision points and feedback loops These advanced methods aren’t limited to frontier AI labs anymore, and you can now use them in your own applications. Learn here: deeplearning.ai/courses/fine…

The AI Financial Hackathon Championship is officially live! 🚀 We’re hosting this in collaboration with @awscloud (AWS), bringing together builders who are pushing the limits of intelligent document processing. This is your chance to build and design solutions that turn complex financial documents into structured, actionable data using Agentic Document Extraction (ADE). To participate: • Register before November 9 at 11:59 PM ET and start building right away • Submit your project by November 10 for a chance to qualify for the live finale in New York on November 15 • The top 10 teams will be invited to present their projects in person 💰 Prize Pool 1st Place: $5,000 ($3K cash + $2K AWS credits) 2nd Place: $1,000 Best Online-Only App: Surprise prize shipped to the team 👉 Register here: luma.com/jme15h1t

Hanging out with Project Jupyter co-founder @ellisonbg. If not for him and @fperez_org we wouldn’t have the coding notebooks we use daily in AI and Data Science. Very grateful to him and the whole Jupyter team for this wonderful open-source work!

The full agenda for AI Dev 25 x NYC is ready. Developers from Google, AWS, Vercel, Groq, Mistral AI, SAP, and other exciting companies will share what they've learned building production AI systems. Here's what we'll cover: Agentic Architecture: When orchestration frameworks help versus when they accumulate errors. How model-driven agents and autonomous planning handle edge cases. Context Engineering: Why retrieval fails for complex reasoning tasks. How knowledge graphs connect information that vector search misses. Building memory systems that preserve relationships. Infrastructure: Where hardware, models, and applications create scaling bottlenecks. Semantic caching strategies that cut costs and latency. How inference speed enables better orchestration. Production Readiness: Moving from informal evaluation to systematic agent testing. Translating AI governance into engineering practice. Building under regulatory constraints. Tooling: MCP implementations that work. Context-rich code review systems. Working demos you can adapt for your applications. I'll share my perspective on where AI development is heading. Looking forward to seeing you there! ai-dev.deeplearning.ai/

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with @Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here: deeplearning.ai/short-course…

Fun breakfast with @ylecun. We chatted about open science and open source (grateful for his tireless advocacy of these for decades), JEPA and where AI research and models might go next!

Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals (measuring the system’s performance) and error analysis (identifying the causes of errors). It’s tempting to shortcut these processes and to quickly attempt fixes to mistakes rather than slowing down to identify the root causes. But evals and error analysis can lead to much faster progress. In this first of a two-part letter, I’ll share some best practices for finding and addressing issues in agentic systems. Even though error analysis has long been an important part of building supervised learning systems, it is still underappreciated compared to, say, using the latest and buzziest tools. Identifying the root causes of particular kinds of errors might seem “boring,” but it pays off! If you are not yet persuaded that error analysis is important, permit me to point out: - To master a composition on a musical instrument, you don’t only play the same piece from start to end. Instead, you identify where you’re stumbling and practice those parts more. - To be healthy, you don’t just build your diet around the latest nutrition fads. You also ask your doctor about your bloodwork to see if anything is amiss. (I did this last month and am happy to report I’m in good health! 😃) - To improve your sports team’s performance, you don’t just practice trick shots. Instead, you review game films to spot gaps and then address them. To improve your agentic AI system, don’t just stack up the latest buzzy techniques that just went viral on social media (though I find it fun to experiment with buzzy AI techniques as much as the next person!). Instead, use error analysis to figure out where it’s falling short, and focus on that. Before analyzing errors, we first have to decide what is an error. So the first step is to put in evals. I’ll focus on that for the remainder of this letter and discuss error analysis next week. If you are using supervised learning to train a binary classifier, the number of ways the algorithm could make a mistake is limited. It could output 0 instead of 1, or vice versa. There is also a handful of standard metrics like accuracy, precision, recall, F1, ROC, etc. that apply to many problems. So as long as you know the test distribution, evals are relatively straightforward, and much of the work of error analysis lies in identifying what types of input an algorithm fails on, which also leads to data-centric AI techniques for acquiring more data to augment the algorithm in areas where it’s weak. With generative AI, a lot of intuitions from evals and error analysis of supervised learning carry over — history doesn’t repeat itself, but it rhymes — and developers who are already familiar with machine learning and deep learning often adapt to generative AI faster than people who are starting from scratch. But one new challenge is that the space of outputs is much richer, so there are many more ways an algorithm’s output might be wrong. Take the example of automated processing of financial invoices where we use an agentic workflow to populate a financial database with information from received invoices. Will the algorithm incorrectly extract the invoice due date? Or the final amount? Or mistake the payer address for the biller address? Or get the financial currency wrong? Or make the wrong API call so the verification process fails? Because the output space is much larger, the number of failure modes is also much larger. Rather than defining an error metric ahead of time, it is therefore typically more effective to first quickly build a prototype, then manually examine a handful of agent outputs to see where it performs well and where it stumbles. This allows you to focus on building datasets and error metrics — sometimes objective metrics implemented in code, and sometimes subjective metrics using LLM-as-judge — to check the system’s performance in the dimensions you are most concerned about. In supervised learning, we sometimes tune the error metric to better reflect what humans care about. With agentic workflows, I find tuning evals to be even more iterative, with more frequent tweaks to the evals to capture the wider range of things that can go wrong. I discuss this and other best practices in detail in Module 4 of the Agentic AI course on deeplearning.ai that we announced last week. After building evals, you now have a measurement of your system’s performance, which provides a foundation for trying different modifications to your agent, as you can now measure what makes a difference. The next step is then to perform error analysis to pinpoint what changes to focus your development efforts on. I’ll discuss this further next week. [Original text: deeplearning.ai/the-batch/is… ]

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s @lavinigam and @sitalakshmi_s. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here: deeplearning.ai/short-course…

Announcing a significant upgrade to Agentic Document Extraction! LandingAI's new DPT (Document Pre-trained Transformer) accurately extracts even from complex docs. For example, from large, complex tables, which is important for many finance and healthcare applications. And a new SDK makes using it require only 3 simple lines of code. Please see the video for technical details. I hope this unlocks a lot of value from the "dark data" currently stuck in PDF files, and that you'll build something cool with this!

Last week, China barred its major tech companies from buying Nvidia chips. This move received only modest attention in the media, but has implications beyond what’s widely appreciated. Specifically, it signals that China has progressed sufficiently in semiconductors to break away from dependence on advanced chips designed in the U.S., the vast majority of which are manufactured in Taiwan. It also highlights the U.S. vulnerability to possible disruptions in Taiwan at a moment when China is becoming less vulnerable. After the U.S. started restricting AI chip sales to China, China dramatically ramped up its semiconductor research and investment to move toward self-sufficiency. These efforts are starting to bear fruit, and China’s willingness to cut off Nvidia is a strong sign of its faith in its domestic capabilities. For example, the new DeepSeek-R1-Safe model was trained on 1000 Huawei Ascend chips. While individual Ascend chips are significantly less powerful than individual Nvidia or AMD chips, Huawei’s system-level design approach to orchestrating how a much larger number of chips work together seems to be paying off. For example, Huawei’s CloudMatrix 384 system of 384 chips aims to compete with Nvidia’s GB200, which uses 72 higher-capability chips. Today, U.S. access to advanced semiconductors is heavily dependent on Taiwan’s TSMC, which manufactures the vast majority of the most advanced chips. Unfortunately, U.S. efforts to ramp up domestic semiconductor manufacturing have been slow. I am encouraged that one fab at the TSMC Arizona facility is now operating, but issues of workforce training, culture, licensing and permitting, and the supply chain are still being addressed, and there is still a long road ahead for the U.S. facility to be a viable substitute for manufacturing in Taiwan. If China gains independence from Taiwan manufacturing significantly faster than the U.S., this would leave the U.S. much more vulnerable to possible disruptions in Taiwan, whether through natural disasters or man-made events. If manufacturing in Taiwan is disrupted for any reason and Chinese companies end up accounting for a large fraction of global semiconductor manufacturing capabilities, that would also help China gain tremendous geopolitical influence. Despite occasional moments of heightened tensions and large-scale military exercises, Taiwan has been mostly peaceful since the 1960s. This peace has helped the people of Taiwan to prosper and allowed AI to make tremendous advances, built on top of chips made by TSMC. I hope we will find a path to maintaining peace for many decades more. But hope is not a plan. In addition to working to ensure peace, practical work lies ahead to multi-source, build more chip fabs in more nations, and enhance the resilience of the semiconductor supply chain. Dependence on any single manufacturer invites shortages, price spikes, and stalled innovation the moment something goes sideways. [Original text: deeplearning.ai/the-batch/is… ]

When data agents fail, they often fail silently - giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure. "Building and Evaluating Data Agents" is a new short course created with @Snowflake and taught by @datta_cs and @_jreini that teaches you to build data agents with comprehensive evaluation built in. Skills you'll gain: - Build reliable LLM data agents using the Goal-Plan-Action framework and runtime evaluations that catch failures mid-execution - Use OpenTelemetry tracing and evaluation infrastructure to diagnose exactly where agents fail and systematically improve performance - Orchestrate multi-step workflows across web search, SQL, and document retrieval in LangGraph-based agents The result: visibility into every step of your agent's reasoning, so if something breaks, you have a systematic approach to fix it. Sign up to get started: deeplearning.ai/short-course…