AI Dev 25 x NYC | Eric Sondhi: Small AI: The Next Big Thing

DeepLearningAI · Beginner ·🛡️ AI Safety & Ethics ·3mo ago
At AI Dev 25 in New York City, Eric Sondhi, Senior Manager Developer Marketing Strategy at ARM, discussed the two emerging paths for AI: big AI and small AI. Local, power-efficient, open-source models that run directly on your devices are proving to be compelling alternatives to the large, centralized, costly frontier and foundation models that have been at the forefront of the AI landscape to date. Sondhi discussed how Small AI can empower developers everywhere to innovate by overcoming economic, technical, and accessibility limitations of "Big AI" alongside some of the challenges and pote…
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1 Forward and Backward Propagation (C1W4L06)
Forward and Backward Propagation (C1W4L06)
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 2 deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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 7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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 8 Using an Appropriate Scale (C2W3L02)
Using an Appropriate Scale (C2W3L02)
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 9 Gradient Checking (C2W1L13)
Gradient Checking (C2W1L13)
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 10 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)
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 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)
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 19 Gradient Descent With Momentum (C2W2L06)
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 20 Normalizing Activations in a Network (C2W3L04)
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 21 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)
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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)
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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)
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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)
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 37 Getting Matrix Dimensions Right (C1W4L03)
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 38 Understanding Dropout (C2W1L07)
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 39 Building Blocks of a Deep Neural Network (C1W4L05)
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 40 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)
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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)
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 46 Other Regularization Methods (C2W1L08)
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 47 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)
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 51 What does this have to do with the brain? (C1W4L08)
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 52 Dropout Regularization (C2W1L06)
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 53 Vanishing/Exploding Gradients (C2W1L10)
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 54 Basic Recipe for Machine Learning (C2W1L03)
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 55 Bias/Variance (C2W1L02)
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 56 Forward Propagation in a Deep Network (C1W4L02)
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 57 Weight Initialization in a Deep Network (C2W1L11)
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 58 Numerical Approximations of Gradients (C2W1L12)
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 59 Regularization (C2W1L04)
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