The Impact of AI for Social Good
Skills:
AI Ethics & Policy70%
Welcome - Whether you're just starting out or looking to advance your AI skills to make a social change, this event is made for you! In this session, you will meet industry leaders and hear them share their stories about their experiences using Machine Learning and Artificial Intelligence to impact the world for good.
We will be taking questions during the event. Please submit your question or upvote others' here:
https://app.sli.do/event/32aL8uJr17pew2nMyUFdT8/live/questions
Speakers:
- Caroline Lair, Founder & CEO of The Good AI, Cofounder of Women in AI non-profit
https://www.linkedin.com/in/carolinelair/?originalSubdomain=fr
- Lambert Hogenhout, Chief Data, Analytics and Emerging Technologies, United Nations
https://www.linkedin.com/in/lamberthogenhout/
- Sonali Bhavsar, Managing Director, Data and AI, Accenture
https://www.linkedin.com/in/sonalibhavsar/
- Katia Walsh, Chief Global Strategy and AI Officer, Executive Leadership Team, Levi Strauss & Co.
https://www.linkedin.com/in/katiawalsh/
Let us know how we're doing. We will be giving out discount codes for a selected number of people who fill out the survey: https://forms.gle/QqGAPxLB47ac4fVf9
Looking to connect with your peer learners, share projects, and swap advice? Join our AI community:
https://community.deeplearning.ai/invites/ddzDLVa2jv
To learn more about us and signup for future events:
DeepLearning.AI: https://www.deeplearning.ai/
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Forward and Backward Propagation (C1W4L06)
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deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
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deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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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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