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Describing the Structure of Unlabled Data
- Unsupervised Machine Learning
Recognizing, Clustering and Generating Images, Video Sequences and Motion-capture Data
- Deep Belief Networks (DBNs)
Reconstructing the Original Input Data from a Corrupted (Noisy) Version
- Feature Selection and Extraction
- Stacked Denoising Auto-encoders
Analyzing Visual Images
- Convolutional Neural Networks
Gaining a Better Understanding of the Structure of Data
- Semi-Supervised Learning
Understanding Text Data
- Text Feature Extraction
Building Highly Accurate Predictive Models
- Improving Machine Learning Results
- Ensemble Methods
Summary and Conclusion
- Python programming experience
- An understanding of basic principles of machine learning
- Data scientists
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
having access to the notebooks to work through
The trainers knowledge of the topics he was teaching.
Deep Learning for Vision with Caffe
Deep Learning with Keras
Deep Learning for Self Driving Cars
TensorFlow Lite for Embedded Linux
TensorFlow Lite for Android
TensorFlow Lite for iOS
Tensorflow Lite for Microcontrollers