Learn to build effective Agentic AI systems with Andrew Ng
Learn more: https://bit.ly/4mVUPyF
Building effective Agentic AI systems is one of the most valuable skills in AI today.
Introducing Agentic AI, a new course from Andrew Ng, now available on DeepLearning.AI.
While most developers build AI that just responds to prompts, the most productive teams are building AI that executes multi-step workflows autonomously.
In this course, you'll learn how to build 4 key agentic workflows using raw Python so that you'll see how each step actually works.
Skills you’ll gain:
- Build agentic design patterns: reflection, tool use, planning, and multi-agent workflows
- Integrate AI with external tools: databases, APIs, web search, and code execution
- Evaluate and optimize AI systems: performance metrics, error analysis, and production deployment
Build everything from scratch in Python—no frameworks, no black boxes.
Agentic AI is available exclusively on DeepLearning.AI. With a Pro membership you get access to quizzes, code labs, and Professional Certifications along with early access to our available catalog of advanced AI courses.
Enroll in Agentic AI on DeepLearning.AI: https://bit.ly/4mVUPyF
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Forward and Backward Propagation (C1W4L06)
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deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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Using an Appropriate Scale (C2W3L02)
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Gradient Checking (C2W1L13)
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Gradient Checking Implementation Notes (C2W1L14)
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Learning Rate Decay (C2W2L09)
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Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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Mini Batch Gradient Descent (C2W2L01)
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The Problem of Local Optima (C2W3L10)
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Exponentially Weighted Averages (C2W2L03)
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Tuning Process (C2W3L01)
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Understanding Exponentially Weighted Averages (C2W2L04)
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Bias Correction of Exponentially Weighted Averages (C2W2L05)
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Gradient Descent With Momentum (C2W2L06)
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Normalizing Activations in a Network (C2W3L04)
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Hyperparameter Tuning in Practice (C2W3L03)
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Adam Optimization Algorithm (C2W2L08)
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RMSProp (C2W2L07)
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Fitting Batch Norm Into Neural Networks (C2W3L05)
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Why Does Batch Norm Work? (C2W3L06)
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Batch Norm At Test Time (C2W3L07)
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Softmax Regression (C2W3L08)
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Deep Learning Frameworks (C2W3L10)
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Neural Network Overview (C1W3L01)
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Training Softmax Classifier (C2W3L09)
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Why Deep Representations? (C1W4L04)
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Gradient Descent For Neural Networks (C1W3L09)
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Neural Network Representations (C1W3L02)
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TensorFlow (C2W3L11)
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Activation Functions (C1W3L06)
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Explanation For Vectorized Implementation (C1W3L05)
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Getting Matrix Dimensions Right (C1W4L03)
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Understanding Dropout (C2W1L07)
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Building Blocks of a Deep Neural Network (C1W4L05)
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Why Non-linear Activation Functions (C1W3L07)
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Computing Neural Network Output (C1W3L03)
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Backpropagation Intuition (C1W3L10)
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Train/Dev/Test Sets (C2W1L01)
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Deep L-Layer Neural Network (C1W4L01)
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Random Initialization (C1W3L11)
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Other Regularization Methods (C2W1L08)
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Normalizing Inputs (C2W1L09)
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Derivatives Of Activation Functions (C1W3L08)
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Parameters vs Hyperparameters (C1W4L07)
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Vectorizing Across Multiple Examples (C1W3L04)
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What does this have to do with the brain? (C1W4L08)
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Dropout Regularization (C2W1L06)
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Vanishing/Exploding Gradients (C2W1L10)
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Basic Recipe for Machine Learning (C2W1L03)
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Bias/Variance (C2W1L02)
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Forward Propagation in a Deep Network (C1W4L02)
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Weight Initialization in a Deep Network (C2W1L11)
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Numerical Approximations of Gradients (C2W1L12)
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Regularization (C2W1L04)
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Why Regularization Reduces Overfitting (C2W1L05)
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