#13 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 2, Lesson 5]

DeepLearningAI · Beginner ·📐 ML Fundamentals ·4y ago

Skills: Model Deployment90%ML Pipelines80%

The Machine Learning Engineering for Production (MLOps) Specialization teaches you how to conceptualize, build, and maintain integrated systems that continuously operate in production. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology and solve real-world problems. This is a video from Course 1, Week 2, Lesson 5 video on "Tips for getting started". To learn more about this and other topics and access the full course videos and assignments, enroll in the Specialization here:https://bit.ly/3v8pxwA Check out all our programs: https://bit.ly/3L9rnmQ Subscribe to The Batch, our weekly newsletter: https://bit.ly/3vxv52R Follow us: Twitter: https://twitter.com/deeplearningai_ Facebook: https://www.facebook.com/deeplearningHQ/ Linkedin: https://www.linkedin.com/company/deep...

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Forward and Backward Propagation (C1W4L06)

Forward and Backward Propagation (C1W4L06)

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Using an Appropriate Scale (C2W3L02)

Using an Appropriate Scale (C2W3L02)

Gradient Checking (C2W1L13)

Gradient Checking (C2W1L13)

Gradient Checking Implementation Notes (C2W1L14)

Gradient Checking Implementation Notes (C2W1L14)

Learning Rate Decay (C2W2L09)

Learning Rate Decay (C2W2L09)

Understanding Mini-Batch Gradient Dexcent (C2W2L02)

Understanding Mini-Batch Gradient Dexcent (C2W2L02)

Mini Batch Gradient Descent (C2W2L01)

Mini Batch Gradient Descent (C2W2L01)

The Problem of Local Optima (C2W3L10)

The Problem of Local Optima (C2W3L10)

Exponentially Weighted Averages (C2W2L03)

Exponentially Weighted Averages (C2W2L03)

Tuning Process (C2W3L01)

Tuning Process (C2W3L01)

Understanding Exponentially Weighted Averages (C2W2L04)

Understanding Exponentially Weighted Averages (C2W2L04)

Bias Correction of Exponentially Weighted Averages (C2W2L05)

Bias Correction of Exponentially Weighted Averages (C2W2L05)

Gradient Descent With Momentum (C2W2L06)

Gradient Descent With Momentum (C2W2L06)

Normalizing Activations in a Network (C2W3L04)

Normalizing Activations in a Network (C2W3L04)

Hyperparameter Tuning in Practice (C2W3L03)

Hyperparameter Tuning in Practice (C2W3L03)

Adam Optimization Algorithm (C2W2L08)

Adam Optimization Algorithm (C2W2L08)

RMSProp (C2W2L07)

RMSProp (C2W2L07)

Fitting Batch Norm Into Neural Networks (C2W3L05)

Fitting Batch Norm Into Neural Networks (C2W3L05)

Why Does Batch Norm Work? (C2W3L06)

Why Does Batch Norm Work? (C2W3L06)

Batch Norm At Test Time (C2W3L07)

Batch Norm At Test Time (C2W3L07)

Softmax Regression (C2W3L08)

Softmax Regression (C2W3L08)

Deep Learning Frameworks (C2W3L10)

Deep Learning Frameworks (C2W3L10)

Neural Network Overview (C1W3L01)

Neural Network Overview (C1W3L01)

Training Softmax Classifier (C2W3L09)

Training Softmax Classifier (C2W3L09)

Why Deep Representations? (C1W4L04)

Why Deep Representations? (C1W4L04)

Gradient Descent For Neural Networks (C1W3L09)

Gradient Descent For Neural Networks (C1W3L09)

Neural Network Representations (C1W3L02)

Neural Network Representations (C1W3L02)

TensorFlow (C2W3L11)

TensorFlow (C2W3L11)

Activation Functions (C1W3L06)

Activation Functions (C1W3L06)

Explanation For Vectorized Implementation (C1W3L05)

Explanation For Vectorized Implementation (C1W3L05)

Getting Matrix Dimensions Right (C1W4L03)

Getting Matrix Dimensions Right (C1W4L03)

Understanding Dropout (C2W1L07)

Understanding Dropout (C2W1L07)

Building Blocks of a Deep Neural Network (C1W4L05)

Building Blocks of a Deep Neural Network (C1W4L05)

Why Non-linear Activation Functions (C1W3L07)

Why Non-linear Activation Functions (C1W3L07)

Computing Neural Network Output (C1W3L03)

Computing Neural Network Output (C1W3L03)

Backpropagation Intuition (C1W3L10)

Backpropagation Intuition (C1W3L10)

Train/Dev/Test Sets (C2W1L01)

Train/Dev/Test Sets (C2W1L01)

Deep L-Layer Neural Network (C1W4L01)

Deep L-Layer Neural Network (C1W4L01)

Random Initialization (C1W3L11)

Random Initialization (C1W3L11)

Other Regularization Methods (C2W1L08)

Other Regularization Methods (C2W1L08)

Normalizing Inputs (C2W1L09)

Normalizing Inputs (C2W1L09)

Derivatives Of Activation Functions (C1W3L08)

Derivatives Of Activation Functions (C1W3L08)

Parameters vs Hyperparameters (C1W4L07)

Parameters vs Hyperparameters (C1W4L07)

Vectorizing Across Multiple Examples (C1W3L04)

Vectorizing Across Multiple Examples (C1W3L04)

What does this have to do with the brain? (C1W4L08)

What does this have to do with the brain? (C1W4L08)

Dropout Regularization (C2W1L06)

Dropout Regularization (C2W1L06)

Vanishing/Exploding Gradients (C2W1L10)

Vanishing/Exploding Gradients (C2W1L10)

Basic Recipe for Machine Learning (C2W1L03)

Basic Recipe for Machine Learning (C2W1L03)

Bias/Variance (C2W1L02)

Bias/Variance (C2W1L02)

Forward Propagation in a Deep Network (C1W4L02)

Forward Propagation in a Deep Network (C1W4L02)

Weight Initialization in a Deep Network (C2W1L11)

Weight Initialization in a Deep Network (C2W1L11)

Numerical Approximations of Gradients (C2W1L12)

Numerical Approximations of Gradients (C2W1L12)

Regularization (C2W1L04)

Regularization (C2W1L04)

Why Regularization Reduces Overfitting (C2W1L05)

Why Regularization Reduces Overfitting (C2W1L05)

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