DeepLearning.AI
@DeepLearningAI • 335,528 subscribers
We are an education technology company with the mission to grow and connect the global AI community.
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📢 New short course in collaboration with Google: Build and Train an LLM with JAX. In this course, you’ll implement and train a 20M-parameter MiniGPT-style language model from scratch using JAX, the open-source library behind Gemini. You’ll build the model architecture, load and preprocess training data, implement the training loop, save checkpoints, and generate text through a chat interface. Taught by Chris Achard, Developer Relations Engineer on Google’s TPU Software team. Enroll now:
DeepLearning.AI46,065 Aufrufe • vor 3 Monaten
You don't need to know how to code to build apps anymore. Andrew Ng's new course shows how to turn an idea into a working web app simply by describing what you want to AI. You'll build something real in under 30 minutes. "Build with Andrew" is our most beginner-friendly course yet. If you know someone who has ideas but thinks building is too complicated—friends, parents, coworkers—please share this with them. Start building today:
DeepLearning.AI45,651 Aufrufe • vor 4 Monaten
Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, 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!
DeepLearning.AI40,745 Aufrufe • vor 8 Monaten
🚨 New course alert! Fast Prototyping of GenAI Apps with Streamlit, built in partnership with Snowflake, is live. Traditional, months-long planning cycles don’t fit the software development landscape of today. New capabilities surface every week, and ideas lose momentum unless you ship a demo quickly. This course, led by Chanin Nantasenamat (Chanin Nantasenamat), gives you a repeatable workflow to go from concept to share-ready demo in days, not months. What you’ll do: - Build an interactive Streamlit app on a dataset inside your own Snowflake account (120-day trial available) - Improve response quality with prompt engineering and RAG, grounded in your data - Run your prototype in Snowflake or publish to Streamlit Community Cloud to collect feedback and iterate with an MVP workflow Get all the details and enroll now:
DeepLearning.AI45,584 Aufrufe • vor 9 Monaten
New course added! We’re launching “Knowledge Graphs for AI Agent API Discovery,” built in collaboration with SAP and taught by Pavithra G K and Lars Heling. When agents call APIs in the wrong sequence, entire workflows collapse. The challenge? Most agents lack the contextual understanding of which endpoints depend on others and why order matters in real business processes. This course teaches you to: ✅Construct a knowledge graph from API specifications ✅ Extend it with business-process data so dependencies and order are explicit ✅ Use semantic retrieval and process edges to find required APIs and their sequence, then build an agent to execute them Enroll now!
DeepLearning.AI41,683 Aufrufe • vor 8 Monaten
New course in collaboration with CrewAI, and taught by its Co-Founder and CEO, João Moura! In "Design, Develop, and Deploy Multi-Agent Systems" you'll go deep into building teams of AI agents that collaborate to handle complex, end-to-end workflows. You’ll design systems that plan, reason, and coordinate, with tools, memory, and guardrails that make them reliable and production-ready. Learn more and enroll now 👉 Featuring insights from Weaviate AI Database, Snyk, Exa, and AB InBev, whose work with CrewAI demonstrates how multi-agent systems are being applied in the field today.
DeepLearning.AI33,808 Aufrufe • vor 6 Monaten
OCR can process characters but it doesn’t understand pixels. OCR has no way to reason about the headers, totals, or checkboxes found in tables, invoices, or forms. In our course with LandingAI, "Document AI: From OCR to Agentic Doc Extraction," we build agents to address these failure modes by breaking documents into pieces, applying the right tools, and mapping information to expected formats. Learn more and enroll today:
DeepLearning.AI21,490 Aufrufe • vor 4 Monaten
Our course recommendation of the day is “Post-training of LLMs, ” where you’ll learn how to customize pre-trained language models using Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL). You'll learn when to use each method, how to curate training data, and implement them in code to shape model behavior effectively. Enroll here:
DeepLearning.AI29,369 Aufrufe • vor 8 Monaten
Production-ready RAG systems need observability. From tracking latency and throughput to evaluating response quality with human feedback or LLM-as-a-judge, robust observability gives you visibility into both system performance and output quality, on both a component and system-wide level. This lesson from our Retrieval Augmented Generation course breaks down the core components of an effective eval system and how to balance cost, automation, and accuracy when choosing your metrics. 📚 Learn more in the full course:
DeepLearning.AI18,793 Aufrufe • vor 4 Monaten
CEOs are wasting millions on AI. Here's why. 💸 CEOs are pouring millions into AI, but simply swapping a human for an AI agent in the middle of a broken process won't change your business. True AI transformation requires an end-to-end workflow redesign. Don't just use AI to make the old way faster—use it to build a completely new experience. Ready to build AI workflows that actually move the needle? DeepLearning.AI offers free short courses to help you master agentic workflows and the latest AI tools. Start learning today: Subscribe to The Batch newsletter for weekly AI insights:
DeepLearning.AI11,368 Aufrufe • vor 2 Monaten
Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing, but aligning your model with real-world business metrics? That’s where the real challenge begins. Learn how to bridge the gap between models and impact:
DeepLearning.AI45,248 Aufrufe • vor 1 Jahr
A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:
DeepLearning.AI32,513 Aufrufe • vor 10 Monaten
Last week, we launched "Attention in Transformers: Concepts and Code in PyTorch" instructed by Joshua Starmer! In this course, you'll: ✅ Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings. ✅ Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention. ✅ Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm. 🔗 Enroll for free:
DeepLearning.AI36,832 Aufrufe • vor 1 Jahr
In case you missed it, earlier this week we launched "MCP: Build Rich-Context AI Apps with Anthropic." Enroll now and: ✅ Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files). ✅ Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them. ✅ Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server. Join in for free:
DeepLearning.AI23,829 Aufrufe • vor 1 Jahr
In case you missed it—we launched Vibe Coding 101 with Replit ⠕ this week, and thousands of learners are already: 👉 Building real web apps with help from an AI coding agent 👉 Using wireframes, product requirements, and clear prompts to guide development 👉 Learning how to debug, customize, and deploy applications in Replit It’s free to join! Jump in:
DeepLearning.AI23,815 Aufrufe • vor 1 Jahr
AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:
DeepLearning.AI23,184 Aufrufe • vor 1 Jahr
In case you missed it, we recently launched "Post-training of LLMs," a short course where you'll: ✅ Understand when and why to use post-training methods like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning. ✅ Learn the concepts underlying the three post-training methods of SFT, DPO, and Online RL, their common use-cases, and how to curate high-quality data to effectively train a model using each method. ✅ Download a pre-trained model and implement post-training pipelines to turn a base model into an instruct model, change the identity of a chat assistant, and improve a model’s math capabilities. Learn more and enroll for free:
DeepLearning.AI16,746 Aufrufe • vor 10 Monaten