Pie & AI: TensorFlow Specialization Launch @ Google HQ

DeepLearningAI · Intermediate ·📰 AI News & Updates ·6y ago
To celebrate the launch of the final course in the deeplearning.ai TensorFlow Specialization, we hosted our third Pie & AI meetup at Google's headquarters in Sunnyvale! Andrew was joined by TensorFlow Specialization instructor Laurence Moroney, TensorFlow Program Manager Alina Shinkarsky, and 175 Bay Area deep learners. Here are the topics Andrew, Laurence, and Alina covered: Fireside chat: 0:04: The state of AI now and in 20 years 5:20: How to address the shortage of AI professionals 10:19: Andrew and Laurence's favorite parts about creating the deeplearning.ai TensorFlow Specialization 14:25: Why the TensorFlow Specialization focuses on Keras 16:02: How someone completely new to AI can break into the field 19:22: Recent advancements in AI that Andrew and Laurence are excited about Audience Q&A: 22:08: RNNs vs. attention models 22:58: What it takes for a small startup to be successful in AI (Editor's note: Unfortunately the video feed quality was reduced for the rest of the video. We apologize for the inconvenience.) 24:37: Knowledge graphs and whether it will bring AI applications to the next level  29:15: How federated learning and differential privacy will emerge in the future 32:37: The skills a successful AI practitioner should develop 37:58: Working in academia vs. industry  Want to build applications with real-world data in TensorFlow? Enroll in the deeplearning.ai TensorFlow Specialization!
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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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3 deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
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4 deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
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5 deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
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6 deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
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7 deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
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8 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)
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12 Understanding Mini-Batch Gradient Dexcent (C2W2L02)
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13 Mini Batch Gradient Descent (C2W2L01)
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14 The Problem of Local Optima (C2W3L10)
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15 Exponentially Weighted Averages (C2W2L03)
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16 Tuning Process (C2W3L01)
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17 Understanding Exponentially Weighted Averages (C2W2L04)
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18 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)
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23 RMSProp (C2W2L07)
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24 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)
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27 Softmax Regression (C2W3L08)
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28 Deep Learning Frameworks (C2W3L10)
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29 Neural Network Overview (C1W3L01)
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30 Training Softmax Classifier (C2W3L09)
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31 Why Deep Representations? (C1W4L04)
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32 Gradient Descent For Neural Networks (C1W3L09)
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33 Neural Network Representations (C1W3L02)
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34 TensorFlow (C2W3L11)
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35 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)
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42 Backpropagation Intuition (C1W3L10)
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43 Train/Dev/Test Sets (C2W1L01)
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44 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)
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49 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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