Deep Learning by TensorFlow 2.0 Basic to Advance with Python
- 3.3
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Brief Introduction
Become Deep Learning professional by learning from Deep Learning professionalDescription
As a practitioner of Deep Learning, I am trying to bring many relevant topics under one umbrella in the following topics. Deep Learning has been most talked about for the last few years and the knowledge has been spread across multiple places.
1. The content (80% hands-on and 20% theory) will prepare you to work independently on Deep Learning projects
2. Foundation of Deep Learning TensorFlow 2.x
3. Use TensorFlow 2.x for Regression (2 models)
4. Use TensorFlow 2.x for Classifications (2 models)
5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models)
6. CNN with Image Data Generator
7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models)
8. Transfer learning
9. Generative Adversarial Networks (GANs)
10. Hyperparameters Tuning
11. How to avoid Overfitting
12. Best practices for Deep Learning and Award-winning Architectures
Requirements
- Requirements
- Awareness of Machine Learning Concepts using Python
Knowledge
- 1. The content (80% hands on and 20% theory) will prepare you to work independently on Deep Learning projects
- 2. Foundation of Deep Learning TensorFlow 2.x
- 3. Use TensorFlow 2.x for Regression (2 models)
- 4. Use TensorFlow 2.x for Classifications (2 models)
- 5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models)
- 6. CNN with Image Data Generator
- 7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models)
- 8. Transfer learning
- 9. Generative Adversarial Networks (GANs)
- 10. Hyper parameters Tuning
- 11. How to avoid Overfitting
- 12. Best practices for Deep Learning and Award winning Architectures
Shiv Onkar Deepak Kumar
3.3 Raiting
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