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DeepLearning.AI

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We are an education technology company with the mission to grow and connect the global AI community.

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💻Learn by building with an AI coding agent, right in your browser. 👀Live preview, nothing to install. 📚Try new agentic coding lab in our course, Building AI Assistants with On-Device Memory. 👉Enroll for free: #AI #AIAgents #AIEngineering

💻Learn by building with an AI coding agent, right in your browser. 👀Live preview, nothing to install. 📚Try new agentic coding lab in our course, Building AI Assistants with On-Device Memory. 👉Enroll for free: #AI #AIAgents #AIEngineering

14,468 views

In Knowledge Graphs for RAG, our latest short course, you’ll explore how knowledge graphs work, how to build with them, and create better retrieval augmented generation applications. Start today for free:

In Knowledge Graphs for RAG, our latest short course, you’ll explore how knowledge graphs work, how to build with them, and create better retrieval augmented generation applications. Start today for free:

13,988 views

In the short course “Getting Started With Mistral,” taught by Sophia Yang, you’ll access Mistral AI's collection of open source and commercial models, including the latest Mixtral 8x22B. Check it out and share it with a friend! 👉

In the short course “Getting Started With Mistral,” taught by Sophia Yang, you’ll access Mistral AI's collection of open source and commercial models, including the latest Mixtral 8x22B. Check it out and share it with a friend! 👉

13,598 views

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🚀Build your AI engineering skills at AI Dev NYC: knowledge graphs, agent memory, harnesses, self-improving agents, inference and security. Plus a fireside chat with Andrew Ng and Yann LeCun on world models. 📅Nov 30–Dec 1 · Chelsea Piers, NYC

🚀Build your AI engineering skills at AI Dev NYC: knowledge graphs, agent memory, harnesses, self-improving agents, inference and security. Plus a fireside chat with Andrew Ng and Yann LeCun on world models. 📅Nov 30–Dec 1 · Chelsea Piers, NYC

DeepLearning.AI

46,006 views • 3 days ago

Building Systems with the ChatGPT API is live! In this short course, you’ll learn how to break a complex task down to be carried out via multiple API calls to an LLM. Join for free:

Building Systems with the ChatGPT API is live! In this short course, you’ll learn how to break a complex task down to be carried out via multiple API calls to an LLM. Join for free:

DeepLearning.AI

576,232 views • 3 years ago

AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with Qodo and taught by Nnenna, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free:

AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with Qodo and taught by Nnenna, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free:

DeepLearning.AI

56,896 views • 2 months ago

Build an assistant that can store and search what it sees, hears, and is told, all without leaving the device. In Building AI Assistants with On-Device Memory, you'll build a local memory system that recalls text, voice, and images, and teach it to recognize something new from a few photos. Built in partnership with Qdrant and taught by Dylan Couzon, Developer Experience Engineer at Qdrant. This course also includes our new AI coding lab, so you can practice building with an AI coding agent from the mobile app. Enroll for free:

Build an assistant that can store and search what it sees, hears, and is told, all without leaving the device. In Building AI Assistants with On-Device Memory, you'll build a local memory system that recalls text, voice, and images, and teach it to recognize something new from a few photos. Built in partnership with Qdrant and taught by Dylan Couzon, Developer Experience Engineer at Qdrant. This course also includes our new AI coding lab, so you can practice building with an AI coding agent from the mobile app. Enroll for free:

DeepLearning.AI

17,175 views • 1 month ago

Build coding agents that learn from experience. In Building Adaptive AI Agents, you’ll turn an agent’s own traces into reusable skills and build a code knowledge graph that finds the right context where keyword search misses. Built in partnership with Oracle and taught by Nacho Martínez (wanderer) and Casius Lee. Enroll for free:

Build coding agents that learn from experience. In Building Adaptive AI Agents, you’ll turn an agent’s own traces into reusable skills and build a code knowledge graph that finds the right context where keyword search misses. Built in partnership with Oracle and taught by Nacho Martínez (wanderer) and Casius Lee. Enroll for free:

DeepLearning.AI

22,772 views • 1 month ago

Your AI coding agent comes with defaults: which model runs, what you pay, and what leaves your machine. You can turn those defaults into choices. In our new short course, AI Coding Workflows: From Cloud to Local, built in partnership with JetBrains and taught by Paul Everitt | @pauleveritt@fosstodon.org, Developer Advocate at JetBrains, you'll rebuild the same app across cloud, hybrid, and fully local setups. Along the way, you'll split work across subagents, put cheaper models on the routine tasks, and finish with models running on your own machine. Enroll for free:

Your AI coding agent comes with defaults: which model runs, what you pay, and what leaves your machine. You can turn those defaults into choices. In our new short course, AI Coding Workflows: From Cloud to Local, built in partnership with JetBrains and taught by Paul Everitt | @[email protected], Developer Advocate at JetBrains, you'll rebuild the same app across cloud, hybrid, and fully local setups. Along the way, you'll split work across subagents, put cheaper models on the routine tasks, and finish with models running on your own machine. Enroll for free:

DeepLearning.AI

18,765 views • 2 months ago

📢 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:

📢 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.AI

46,359 views • 7 months ago

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:

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.AI

45,879 views • 9 months ago

Agentic AI, taught by Andrew Ng, is now available worldwide. Learn to design and evaluate AI systems that plan, reflect, and collaborate across multiple steps, built in raw Python. Available exclusively at DeepLearning.AI. Enroll now 👉

Agentic AI, taught by Andrew Ng, is now available worldwide. Learn to design and evaluate AI systems that plan, reflect, and collaborate across multiple steps, built in raw Python. Available exclusively at DeepLearning.AI. Enroll now 👉

DeepLearning.AI

47,666 views • 1 year ago

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!

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.AI

40,878 views • 1 year ago

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.

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.AI

33,857 views • 11 months ago

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:

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.AI

45,348 views • 1 year ago

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:

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.AI

21,554 views • 9 months ago

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:

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.AI

36,832 views • 1 year ago

Vibe coding works… until your agent confidently builds the wrong thing. Write specs first. Keep your agent aligned with what you actually want to build. Enroll in our Spec-Driven Development course to learn a more intentional way to build with coding agents:

Vibe coding works… until your agent confidently builds the wrong thing. Write specs first. Keep your agent aligned with what you actually want to build. Enroll in our Spec-Driven Development course to learn a more intentional way to build with coding agents:

DeepLearning.AI

14,291 views • 5 months ago

In our new short course you’ll learn exactly how algorithms like DALL·E and Stable Diffusion work. Dive into the fascinating world of diffusion models and build your own model from scratch with Sharon Zhou ✈️ ICML in "How Diffusion Models Work." Sign up:

In our new short course you’ll learn exactly how algorithms like DALL·E and Stable Diffusion work. Dive into the fascinating world of diffusion models and build your own model from scratch with Sharon Zhou ✈️ ICML in "How Diffusion Models Work." Sign up:

DeepLearning.AI

56,075 views • 3 years ago