Day #70 - Machine Learning - Deep Learning Fundamentals - Machine Learning Notes

Picture is worth 1000 words, Few examples listed in the book are very precise, clear on Machine Learning fundamentals. Below are few of the images on Machine learning / Deep Learning Concepts

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

Happy Learning!!!