Tag Archives: Machine Learning

Deep Learning Notes – Activation Function

Activation function Sigmoid: Vanishing gradient (but still used in RNN. gradient for multi-D and slope for 1-D) ReLU:sub differential (corner point could be derivative) Tanh: (range from negatives)   Train model and learn parameters to minimize loss   Loss function … Continue reading

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AI Serving

1. Serving End to end system (given input and provide output, accept request and give response) AI serving (using AI technique to provide response for request)   2. Traditional Web Application Whole page reload when direct to other pages   … Continue reading

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Machine Learning Blogs

Machine Learning is Fun! Deep Learning Kevin’s code and idea Donate $5 to me for a coffee with PayPal and read more professional and interesting technical blog articles about web and mobile development. Feel free to visit my web app, … Continue reading

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Java Fundamentals and Machine Learning Summary and Resources

1.Inheritance can be defined as the process where one class acquires the properties (methods and fields) of another. With the use of inheritance the information is made manageable in a hierarchical order.   2. If a class inherits a method … Continue reading

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Machine Learning Podcast Learning Notes Apr 18

1. Support Vector Machine (SVM): draw a fat line, like a wall, to classify data set into different categories, dots are support vector and it is used to solve over-fitting problem (vs logistic regression).   Non-linear data set can be … Continue reading

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Machine Learning Podcast Learning Notes Apr 17

Shallow algorithms Supervised KNN Decision Trees (supervised, classify/regress) Random Forests Unsupervised K-means -> Clustering Apriori -> Association rule learning / Market basket Principle Component Analysis (PCA) -> Dimension Reduction Gradient Boost Buy me a coffee with PayPal and you will have more professional and … Continue reading

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Machine Learning Podcast Learning Notes Apr 13

Steps Data mining -> Data analysis -> Machine Learning   Math Linear Algebra (Matrix) -> Statistics (Probability/inference) -> Calculus Linear Algebra = Matrix (or “Tensor”) math. Wx + b. Chopping in the analogy. Stats = Probability/inference, the heart of machine … Continue reading

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