Learn the main concepts and techniques used in modern machine learning and deep neural networks through numerous examples written in PyTorch.
Overview:
This course begins with the basic concepts of machine and deep learning. Subsequently, you gain a reasonable familiarity with the main features of PyTorch and learn how it can be applied to some popular problem domains.
Learn How To:
Course Requirements:
Programming experience
Lessons:
Lesson 1: What Is Machine Learning? What Is Deep Learning?
Learning objectives:
1.1 Understand the course at a high level
1.2 Describe the techniques used in machine learning
1.3 Describe the libraries used in machine learning
1.4 Understand the difference between “deep learning” and other ML techniques
1.5 Utilize additional concepts in ML
1.6 Understand the types of network layers and activation functions
1.7 Understand metrics
Lesson 2: Comparing Several Libraries
Learning objectives:
2.1 Perform a task in scikit-learn
2.2 Perform a task in Keras (with TensorFlow)
2.3 Perform a task in PyTorch
2.4 Classify an image with PyTorch
Lesson 3: Understanding PyTorch
Learning objectives:
3.1 Use tensors, autograd, and NumPy interfaces
3.2 Establish a low-level neural network
3.3 Implement a neural network with torch.nn
3.4 Understand why bias is important
3.5 Identify other torch tools
Lesson 4: Tasks with Networks
Learning objectives:
4.1 Create a simple feature classifier—Part 1
4.2 Create a simple feature classifier—Part 2
4.3 Create an image classifier
4.4 Utilize regression prediction
4.5 Do clustering with PyTorch
4.6 Use generative adversarial networks—Part 1
4.7 Use generative adversarial networks—Part 2
4.8 Use a part of speech tagger
Lesson 5: Enhancing an Image Classifier
Learning objectives:
5.1 Start with torchvision.models
5.2 Retrain pretrained models
5.3 Modify network layer
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