Figure #1
- How machine learning, AL and Deep Learning are inter-related, The subset representation clearly represents the knowledge boundaries
- Deep Learning frameworks allow developers to iterate quickly, Making algos accessible to practitioners. Deep learning frameworks help to scale machine learning code for millions of users
- Its important to note fundamentals of Machine Learning is important to work with Deep Learning
Figure #2
- In Machine learning, historical data is used to derive learning's / rules from it and apply it for future data predictions
- From the data we need to identify (relevant features / variables), In this process we use different techniques like PCA, Correlation techniques, Derived features to identify relevant feature attributes for model creation
- From the vast amount of data we collect through enterprise applications / systems we need to identify / extract relevant data to build models and validate them. Setting up the data pipeline, training with required dataset becomes key for better / high accuracy models
Figure #3
- High level perspective of Deep Learning, How the nodes are defined, weights computed
- The loss part for each iteration is compared with predictions and sent back to perform weight updates, This iterations we call it as back propagation
- Deep Learning term is because the network are 'deep' - multiple hidden layers involved in computation
Figure #4
- SVM Wide street approach, line that separates two classes
- Allow non-linear decision boundaries
- Each dimension represents feature
- Goal of SVN - Train a model that assigns unseen objects into particular category
- Advantage - High Dimensionality, Memory Efficiency, Versatility
Machine Learning Notes
