Math base, linear combination > don't like, just ignore this and go to the bottom of this article and read from bottom to top. Yes, we need to start topic with linear combination 😅, a pity, but definitely not an in-depth explanation, just a quote: 《Deep Learning》Ian Goodfellow Yoshua Bengio Aaron Courville Chapter 2…

Go below and check the notebook directly when need more information. Parameter initialization The lab was done with Pytorch, although Keras is also good. What actually surprises me is not the training performance of both in Google Colab, but in the parameter initialization. …

Introduction to Neural Networks First thing’s first

Use recurrent neural network (RNN) to generate image, simplest image generative model. First try In fact it surprised me how easy it was to do this with RNN to generate images. …

Unsupervised Learning Algorithms

Hierarchical Clustering is not really used much, if you use scikit-learn, the syntax is not much different from other models, except for the arguments to the constructor. Although almost all sklearn models are always used as black boxes (after all, no one always builds a car company on wheels), it…

Use K-Means to find centroids that represent all clusters. Replace the value of color channels of images with cluster representations(centroids), in other word that we can use K-means to reduce the size of high-quality images by just keeping the important information and grouping the colors with the right number of…

Supervised Machine Learning The types of supervised Machine Learning are: Regression, in which the target variable is continuous. For example movie revenue

floor The tool floor returns the floor of the input element-wise. The floor of is the largest integer where I and I≤X. import numpy my_array = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]) print numpy.floor(my_array) #[ 1. 2. 3. 4. 5. 6. 7. 8. 9.] ceil The tool ceil returns the ceiling of the input element-wise. The ceiling of is the smallest integer where I and I≥X. import numpy