Machine Learning Data Lifecycle in Production
Course Link Lecture 2 in MLOps Data Label Collecting Data You need to make sure that your data covers the same region of your feature space as the prediction request that you’ll get your trai
Tag: AI Studies
Introduction to Machine Learning in Production
Course Link Lecture 1 in MLOps Overview the key steps involved in a typical machine learning project. It starts with scoping, where the project goals and variables (X and Y) are defined. Data
Sequence Model
Course Link Lecture 5 in Deep Learning RNN there is one-to-many. So, this was a music generation or sequenced generation as example. And then, there’s many-to-one, that would be an example of sen
Convolutional Neural Networks
Course Link Lecture 4 in Deep Learning CNN By convention, in machine learning, we usually do not bother with this flipping operation. Technically this operation is maybe better called cross-corre
Structuring Machine Learning Projects
Course Lecture 3 in Deep Learning Why ML Strategy F1 score Classifier A is 90% recall: That of all of the images in, say, your dev sets that really are cats, classifier A accurately pulled out 9
Imporoving Deep Neural Networks, Hyper parameter tuning, Regularization and Optimization
Course Lecture 2 in Deep Learning Regularization useful technique for reducing variance. There is a little bit of a bias variance tradeoff when you use regularization. It might increase the bias a li
Neural Networks and Deep Learning
Course Lecture 1 of Deep Learning Course Derivatives https://community.deeplearning.ai/t/derivation-of-dl-dz/165 vectorizing logistic regression Why we like to use that cost function for logistic re
Unsupervised Learning, Recommenders, Reinforcement Learning
Course Lecture 3 in Machine Learning course Unsupervised Learning1. clustering Usage: Grouping similar news, DNA analysis, Astronomical data analysis k-means algorithm local minimum Occurs when
Advanced Learning Algorithms
Course Lecture 2 in Machine Learning Course Neural Networks matrix multiplication: it is a binary operation that takes a pair of matrices and produces another matrix. It is defined as the product of
Supervised Machine Learning, Regression and Classification
Course Lecture 1 in Machine Learning Course Regression with multiple input variables1. vectorization Many CPUs have “vector” or “SIMD” instruction sets which apply the same operation simultaneously