Computer Science / TensorFlow

Machine Learning Crash Course with TensorFlow APIs

Brought by: Independent

Overview

A self-study guide for aspiring machine learning practitioners. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

Some of the questions answered in this course:

Learn best practices from Google experts on key machine learning concepts.
How does machine learning differ from traditional programming?
What is loss, and how do I measure it?
How does gradient descent work?
How do I determine whether my model is effective?
How do I represent my data so that a program can learn from it?
How do I build a deep neural network?

Syllabus

ML Concepts

Introduction
Framing
Descending into ML
Reducing Loss
First Steps with TF
Generalization
Training and Test Sets
Validation
Representation
Feature Crosses
Regularization: Simplicity
Logistic Regression
Classification
Regularization: Sparsity
Introduction to Neural Nets
Training Neural Nets
Multi-Class Neural Nets
Embeddings

ML Engineering

Production ML Systems
Static vs Dynamic Training
Static vs Dynamic Inference
Data Dependencies

ML Real World Examples

Cancer Prediction
18th Century Literature
Real-World Guidelines

Conclusion

Next Steps

Computer Science / TensorFlow

Machine Learning Crash Course with TensorFlow APIs

Machine Learning Crash Course with TensorFlow APIs

Brought by: Independent

Independent
Free
English
Certificate Not Available
Available at any time
All
N/A

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