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TensorFlow Essentials
Implementing Machine Learning
Curriculum
3 Sections
17 Lessons
10 Weeks
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Chapter 6: Analyzing Data with Statistical Regression
5
1.1
Analyzing Systems Using Regression
10 mins
1.2
Linear Regression: Fitting Lines to Data
10 mins
1.3
Polynomial Regression: Fitting Polynomials to Data
10 mins
1.4
Binary Logistic Regression: Classifying Data into Two Categories
10 mins
1.5
Multinomial Logistic Regression: Classifying Data into Multiple Categories
10 mins
Chapter 7: Introducing Neural Networks and Deep Learning
8
2.1
From Neurons to Perceptrons
10 mins
2.2
Improving the Model
10 mins
2.3
Layers and Deep Learning
10 mins
2.4
Training with Backpropagation
10 mins
2.5
Implementing Deep Learning
10 mins
2.6
Tuning the Neural Network
10 mins
2.7
Managing Variables with Scope
10 mins
2.8
Improving the Deep Learning Process
10 mins
Chapter 8: Classifying Images with Convolutional Neural Networks (CNNs)
4
3.1
Filtering Images
10 mins
3.2
Convolutional Neural Networks (CNNs)
10 mins
3.3
Putting Theory into Practice
10 mins
3.4
Performing Image Operations
10 mins
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