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Course Outline
Supervised learning: classification and regression
- Bias-variance trade-off
- Logistic regression as a classifier
- Measuring classifier performance
- Support vector machines
- Neural networks
- Random forests
Unsupervised learning: clustering, anomaly detection
- Principal component analysis
- Autoencoders
Advanced neural network architectures
- Convolutional neural networks for image analysis
- Recurrent neural networks for time-structured data
- The long short-term memory cell
Practical examples of problems that AI can solve, e.g.
- Image analysis
- Forecasting complex financial series, such as stock prices,
- Complex pattern recognition
- Natural language processing
- Recommender systems
Software platforms used for AI applications:
- TensorFlow, Theano, Caffe and Keras
- AI at scale with Apache Spark: MLlib
Understand limitations of AI methods: modes of failure, costs and common difficulties
- Overfitting
- Biases in observational data
- Missing data
- Neural network poisoning
Requirements
No specific prerequisites are required to attend this course.
28 Hours
Testimonials (2)
The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at
Magdalena - Samsung Electronics Polska Sp. z o.o.
Course - Deep Learning with TensorFlow 2
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
