A new Data Engineering Professional Certificate is joining our catalog!

DeepLearningAI · Intermediate ·📊 Data Analytics & Business Intelligence ·1y ago
Pre-enroll now: https://bit.ly/3A6jQEp Announcing Data Engineering, a new advanced professional certificate offered through Coursera, now available for pre-enrollment! You can’t have good data analysis without data that is effectively ingested, stored, transformed, and served to meet the needs of downstream data stakeholders such as data scientists and machine learning engineers. Data engineering is the foundation that makes data useful in modern organizations, as it involves the development, implementation, and maintenance of systems that transform raw data into high-quality information for analysis and machine learning use cases. It intersects with several undercurrents, such as security, data management, DataOps, data architecture, orchestration, and software engineering. This 4-course program will help you think, act, and function like a data engineer in the real world as you develop core skills to select the right technologies for your data architecture and pipelines. You'll learn about the entire data engineering lifecycle, from data generation to ingestion, transformation, storage, and serving. These fundamental practices will allow you to tackle any data engineering problem systematically and grow your career in a high-demand field. For aspiring data engineers, data engineers looking to refine their skills, and data stakeholders such as data team leads, the lessons in this specialization emphasize hands-on practice with cloud solutions, primarily AWS, and open source tools. Taught by industry expert Joe Reis, co-author of the best-selling book “Fundamentals of Data Engineering,” this professional certificate equips you with the skills and knowledge to excel in a high-demand field. Pre-enroll now and get a 14-day free trial when the course launches, plus, be the first to know when it’s live! Learn more: https://bit.ly/3A6jQEp
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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
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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
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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
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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)
Gradient Checking Implementation Notes (C2W1L14)
DeepLearningAI
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)
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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)
DeepLearningAI
58 Numerical Approximations of Gradients (C2W1L12)
Numerical Approximations of Gradients (C2W1L12)
DeepLearningAI
59 Regularization (C2W1L04)
Regularization (C2W1L04)
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60 Why Regularization Reduces Overfitting (C2W1L05)
Why Regularization Reduces Overfitting (C2W1L05)
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