Build Memory-Aware Agents

DeepLearning.AI · Intermediate ·🤖 AI Agents & Automation ·1w ago
Learn more: https://bit.ly/4slpBEM New short course built in collaboration with Oracle: Agent Memory: Building Memory-Aware Agents. Many AI agents work well within a single session but lose everything when the session ends. In this course, you’ll learn how to build a memory-first architecture that allows agents to persist knowledge, retrieve relevant context, and update what they know across sessions. Using Oracle AI Database, LangChain, and LLM-powered pipelines, you’ll build a complete agent memory system that turns a stateless agent into one that can learn and improve over time. In this…
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1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
DeepLearningAI
 2 deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
DeepLearningAI
 3 deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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 4 deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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 5 deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
DeepLearningAI
 6 deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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 7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
DeepLearningAI
 8 Using an Appropriate Scale (C2W3L02)
Using an Appropriate Scale (C2W3L02)
DeepLearningAI
 9 Gradient Checking (C2W1L13)
Gradient Checking (C2W1L13)
DeepLearningAI
 10 Gradient Checking Implementation Notes (C2W1L14)
Gradient Checking Implementation Notes (C2W1L14)
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 11 Learning Rate Decay (C2W2L09)
Learning Rate Decay (C2W2L09)
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 12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
DeepLearningAI
 13 Mini Batch Gradient Descent (C2W2L01)
Mini Batch Gradient Descent (C2W2L01)
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 14 The Problem of Local Optima (C2W3L10)
The Problem of Local Optima (C2W3L10)
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 15 Exponentially Weighted Averages (C2W2L03)
Exponentially Weighted Averages (C2W2L03)
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 16 Tuning Process (C2W3L01)
Tuning Process (C2W3L01)
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 17 Understanding Exponentially Weighted Averages (C2W2L04)
Understanding Exponentially Weighted Averages (C2W2L04)
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 18 Bias Correction of Exponentially Weighted Averages (C2W2L05)
Bias Correction of Exponentially Weighted Averages (C2W2L05)
DeepLearningAI
 19 Gradient Descent With Momentum (C2W2L06)
Gradient Descent With Momentum (C2W2L06)
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 20 Normalizing Activations in a Network (C2W3L04)
Normalizing Activations in a Network (C2W3L04)
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 21 Hyperparameter Tuning in Practice (C2W3L03)
Hyperparameter Tuning in Practice (C2W3L03)
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 22 Adam Optimization Algorithm (C2W2L08)
Adam Optimization Algorithm (C2W2L08)
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 23 RMSProp (C2W2L07)
RMSProp (C2W2L07)
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 24 Fitting Batch Norm Into Neural Networks (C2W3L05)
Fitting Batch Norm Into Neural Networks (C2W3L05)
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 25 Why Does Batch Norm Work? (C2W3L06)
Why Does Batch Norm Work? (C2W3L06)
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 26 Batch Norm At Test Time (C2W3L07)
Batch Norm At Test Time (C2W3L07)
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 27 Softmax Regression (C2W3L08)
Softmax Regression (C2W3L08)
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 28 Deep Learning Frameworks (C2W3L10)
Deep Learning Frameworks (C2W3L10)
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 29 Neural Network Overview (C1W3L01)
Neural Network Overview (C1W3L01)
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 30 Training Softmax Classifier (C2W3L09)
Training Softmax Classifier (C2W3L09)
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 31 Why Deep Representations? (C1W4L04)
Why Deep Representations? (C1W4L04)
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 32 Gradient Descent For Neural Networks (C1W3L09)
Gradient Descent For Neural Networks (C1W3L09)
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 33 Neural Network Representations (C1W3L02)
Neural Network Representations (C1W3L02)
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 34 TensorFlow (C2W3L11)
TensorFlow (C2W3L11)
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 35 Activation Functions (C1W3L06)
Activation Functions (C1W3L06)
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 36 Explanation For Vectorized Implementation (C1W3L05)
Explanation For Vectorized Implementation (C1W3L05)
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 37 Getting Matrix Dimensions Right (C1W4L03)
Getting Matrix Dimensions Right (C1W4L03)
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 38 Understanding Dropout (C2W1L07)
Understanding Dropout (C2W1L07)
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 39 Building Blocks of a Deep Neural Network (C1W4L05)
Building Blocks of a Deep Neural Network (C1W4L05)
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 40 Why Non-linear Activation Functions (C1W3L07)
Why Non-linear Activation Functions (C1W3L07)
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 41 Computing Neural Network Output (C1W3L03)
Computing Neural Network Output (C1W3L03)
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 42 Backpropagation Intuition (C1W3L10)
Backpropagation Intuition (C1W3L10)
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 43 Train/Dev/Test Sets (C2W1L01)
Train/Dev/Test Sets (C2W1L01)
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 44 Deep L-Layer Neural Network (C1W4L01)
Deep L-Layer Neural Network (C1W4L01)
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 45 Random Initialization (C1W3L11)
Random Initialization (C1W3L11)
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 46 Other Regularization Methods (C2W1L08)
Other Regularization Methods (C2W1L08)
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 47 Normalizing Inputs (C2W1L09)
Normalizing Inputs (C2W1L09)
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 48 Derivatives Of Activation Functions (C1W3L08)
Derivatives Of Activation Functions (C1W3L08)
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 49 Parameters vs Hyperparameters (C1W4L07)
Parameters vs Hyperparameters (C1W4L07)
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 50 Vectorizing Across Multiple Examples (C1W3L04)
Vectorizing Across Multiple Examples (C1W3L04)
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 51 What does this have to do with the brain? (C1W4L08)
What does this have to do with the brain? (C1W4L08)
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 52 Dropout Regularization (C2W1L06)
Dropout Regularization (C2W1L06)
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 53 Vanishing/Exploding Gradients (C2W1L10)
Vanishing/Exploding Gradients (C2W1L10)
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 54 Basic Recipe for Machine Learning (C2W1L03)
Basic Recipe for Machine Learning (C2W1L03)
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 55 Bias/Variance (C2W1L02)
Bias/Variance (C2W1L02)
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 56 Forward Propagation in a Deep Network (C1W4L02)
Forward Propagation in a Deep Network (C1W4L02)
DeepLearningAI
 57 Weight Initialization in a Deep Network (C2W1L11)
Weight Initialization in a Deep Network (C2W1L11)
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 58 Numerical Approximations of Gradients (C2W1L12)
Numerical Approximations of Gradients (C2W1L12)
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 59 Regularization (C2W1L04)
Regularization (C2W1L04)
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 60 Why Regularization Reduces Overfitting (C2W1L05)
Why Regularization Reduces Overfitting (C2W1L05)
DeepLearningAI
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