Learn Multi AI Agent Systems with crewAI: Lesson 1
Skills:
Multi-Agent Systems90%
Enroll in the full course ๐ https://bit.ly/3K9y1u4
Multi AI Agent Workflows with CrewAI is taught by Joรฃo Moura, founder and CEO of crewAI, and it will teach you key principles of designing effective AI agents and how to organize a team of agents to perform complex, multi-step tasks.
Explore key components of multi-agent systems:
- Role-playing: Assign specialized roles to agents
- Memory: Provide agents with short-term, long-term, and shared memory
- Tools: Assign pre-built and custom tools to each agent (e.g. for web search)
- Focus: Break down the tasks, goals, and tools and assign multiple AI agents for better performance
- Guardrails: Effectively handle errors, hallucinations, and infinite loops
- Cooperation: Perform tasks in series, in parallel, and hierarchically
Work with crewAI, an open source library designed for building multi-agent systems, and get hands-on by building agent crews that execute common business processes, such as:
- Tailor resumes and interview prep for job applications
- Research, write, and edit technical articles
- Automate customer support inquiries
- Conduct customer outreach campaigns
- Plan and execute events
- Perform financial analysis
If you've taken some prompt engineering courses and want to incorporate LLMs in your professional work, then this course is designed for you.
Enroll in the full course ๐ https://bit.ly/3K9y1u4
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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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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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