New course with crewAI! Join Practical Multi AI Agents and Advanced Use Cases
Enroll for free: https://bit.ly/3Nvw1hy
We’re excited to introduce "Practical Multi AI Agents and Advanced Use Cases with crewAI," taught by João Moura, the founder of crewAI. This course is designed for those who want to build advanced agentic workflows that deliver measurable value through automation and collaboration.
João is back to help you explore how multi-agent systems are used in real businesses today and how to improve them through performance testing and human feedback.
You'll create crews, organize them into workflows, and build pipelines for more efficient automation. The course covers practical applications like project planning, lead scoring, and content creation at scale.
You'll work on several practical use cases, including:
- An automated project planning crew that breaks down projects, estimates time, and allocates resources.
- A progress report generator that interacts with project management tools like Trello.
- A sales pipeline agent that gets and enriches lead information, scores them, and drafts personalized emails for qualified leads.
- A customer support analysis pipeline that creates issue reports and visualizations.
- A content creation crew that researches online, uses RAG to generate content, refines it, and creates social media posts.
Whether you’ve taken the previous crewAI course or are new to multi-agent systems, this course offers a hands-on, practical approach to building solutions that work in real-world settings.
Learn more: https://bit.ly/3Nvw1hy
Watch on YouTube ↗
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Forward and Backward Propagation (C1W4L06)
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Using an Appropriate Scale (C2W3L02)
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Gradient Checking (C2W1L13)
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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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Parameters vs Hyperparameters (C1W4L07)
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Dropout Regularization (C2W1L06)
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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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