cmu machine learning course

methods use optimization algorithms directly or indirectly.According to the function and form similarity of the algorithm, we can classify the algorithm, for example, tree-based algorithm, neural network based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to classify into a certain category. For so

training on the basis of the known data samples, and the classification data model is used to predict the numerical data. Unsupervised learning is the clustering of data. Therefore, the main task of machine learning is classification.What issues do we need to consider when applying machine

of the network is changed appropriately, so that the training process can converge faster and more stably.2.1.1 Increase momentum termFeedforward Network in the course of training, loss function often concussion, resulting in the training process is not convergent, in order to reduce the impact of this problem, you can try to use the smooth loss function of the oscillation curve to speed up the training process, through the design of low-pass filter

Dialogue machine learning Great God Yoshua Bengio (Next)Professor Yoshua Bengio (Personal homepage) is one of the great Gods of machine learning, especially in the field of deep learning. Together with Geoff Hinton and Professor Yann LeCun (Yan), he created the deep

implied variables obtained by the E step.Repeat 2 steps above until convergence.The formula is as follows:The derivation process of the Nether function in M-Step formula:A common example of the EM algorithm is the GMM model, where each sample is likely to be produced by K-Gaussian, except that each Gaussian produces a different probability, so each sample has a corresponding Gaussian distribution (one of the k's), at which point the implied variable is a Gaussian distribution corresponding to e

nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob

Machine Learning notes of the Dragon Star program 　Preface In recent weeks, I spent some time learning the machine learning course of the Dragon Star program for the next summer vacation. For more information, see the appendix. Th

azurealgorithm Flowchart )Source: Https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheetSAS Algorithmic Flowchart (SAS algorithm Flowchart)Source: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-

Awesome series Awesome Machine Learning Awesome Deep Learning Awesome TensorFlow Awesome TensorFlow implementations Awesome Torch Awesome Computer Vision Awesome Deep Vision Awesome RNN Awesome NLP Awesome AI Awesome Deep Learning Papers Awesome 2vec Deep

python Programming Huangge python Remote Video Training Course Article/index. md at master · pythonpeixun/article · GitHub Yellow brother python Training Workshop video playback address Article/python_shiping.md at master · pythonpeixun/article · GitHub I recommend you a book "Collective smart programming". All the examples in this section are written in python. You may learn a lot from them by reading all the code. Compared with python, this

Write in front of the crap:Well, I have to say Fish C markdown Text editor is very good, full-featured. Again thanks to the little turtle Brother's python video Let me last year in the next semester of the introduction of programming, fell in love with the programming of the language, because it is biased statistics, after the internship decided to put the direction of data mining, more and more found the importance of specialized courses. In the days when everyone was busy attending various tra

This paper mainly records the cost function of neural network, the usage of gradient descent in neural network, the reverse propagation, the gradient test, the stochastic initialization and other theories, and attaches the MATLAB code and comments of the relevant parts of the course work. Concepts of neural networks, models, and calculation of predictive classification using forward propagation refer to Andrew Ng

Label: style blog HTTP Io ar use strong SP data Machine Learning Courses Requirements: Basic linear algebra (matrix, vector, matrix vector multiplication), basic probability (probability of random variables and basic attributes), and Calculus Machine Learning: Course