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Understand what multi-task learning and transfer learning is
Recognize bias, variance and data-mismatch by looking in the performances of your algorithm on train/dev/test sets
"Chinese Translation"Learning GoalsLearn what multi-tasking
Transferred from Infoq, author Zhang Tianrei
Machine learning is a hot topic in the field of data analysis, which often uses a variety of machine learning algorithms in peacetime learning and life. In fact, many of the machine
8,800 machine learning Open source projects for you to select TOP30.
Licensed from AI Technology Base (id:rgznai100)This article is a combination of text, suggested reading 5 minutes.This article brings you 30 highly acclaimed machine learning open source
: Modeling on training data is too precise, and there are some noise points in the model. At this point, overfitting reduces the scale of the model, much more in higher dimensions and more complex class boundaries.
Data adoption and segmentation: With respect to data disclosure, you need to be very careful to know whether the training, testing, and cross-checking datasets are truly independent datasets. For timing problems, many ideas and work need to ensure that the system can reply to the
decision on one input is Muti-task learning, and the other is learning a classifier for each category. When the sample data of each class is small, it is wiser to adopt muti-task learning, so that the characteristics of the image can be learned better, and when the sample data of each class is large, the multi-classification model can be used, so the accuracy of
What is machine learning?What is machine learning? The answer to this question can refer to the authoritative machine learning definition, but in reality machine
Original address: Ten machine learning Examples in JavaScriptIn the past year, Libraries for machine learning (machines learning) have become increasingly fast and easy to use. Python has always been the language of choice for
performance of machine learning algorithms by using unmarked large amounts of data.There are five main algorithms for semi-supervised learning: probability-based algorithms, modified methods based on existing supervisory algorithms, methods for directly relying on clustering assumptions, methods based on multiple attempts, and graph-based methods.Examples of sem
[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning)
PDF
Video
Keras
Example application-handwriting Digit recognition
Step 1
This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust
Learning about open-source android projects and open-source android ProjectsThis article collects a relatively good open-source Android project. Researching open-source projects is the best way to master the Android platform.
The core development team of Android provides 15 excellent sample projects, including games,
) And generate (Generate!), In the target directory, you can find the generated OpenCV sample project file:"Samples. sln"
Click to open"Samples. sln", The solution resource manager shows a total of 172 projects (there are many examples, so nice looking !)
Right-click solution and select"Generate a solution"
About one minute or so (different machine configurati
Python projects worth learning and python Projects
This website organized 2014 worth a learning pytho project, http://pycoders.com/2014/
Project resources are all on github, which is essential for python programmers.
As a plan for 2015. First list, then check the number
Projects
-based linear regression method for predicting house prices:
However, when the prediction is applied to a new training data there is a large error (error), there should be some solution:
Get more training examples Try smaller sets of features try getting additional features Try adding polynomial features (E. G. x1^2, X2^2, x1x2 ...) Try Decreasingλtry increasingλ
Diagnosis of Machine
the reason. What you want to study can be directly read the author's deduction.Dual form of perceptual machine learning algorithmHere are the examples given in the author's book, but there is no specific derivation process.We derive as follows. We can tell from the original form. The update process for W.The first update is that the x1y1= ((3,3) t,1) point canno
Great path to the gods-Great Integration of high-quality learning resources and projects for AndroidAndroid has many excellent learning resources, projects, and websites, but most of them are not concentrated, which is not conducive to beginners to learn and grasp Android as a whole. Today, we are just free to sort out
, algorithms, and then systematically. Professor Geoffrey Hinton is the world's most famous cognitive psychologist and computer science historian. Although he was a very early success in academia, he has a reputation for excellence, but he has been active on the line, writing code. Many of his ideas are simple, workable and very effective, so they are called great thinkers. It is because of his wisdom and the progress of deep learning technology usher
Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k-
After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-
WEEK1:Machine learning:
A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves with experience E.
Supervised learning:we already know what we correct output should look like.
Regression:try to map input variables to some continuous function.
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