tensorflow for deep learning from linear regression to reinforcement learning

and the radius is 1, so a curve separates the areas of Y=1 and y=0, so what we need is a two-bit feature:Assuming that the parameter is [-1 0 0 1 1], then we get the decision boundary that is exactly the circle at the origin and the radius is 1.We can use very complex models to accommodate the decision boundaries of very complex shapes.cost function for logistic regression modelFor linear

Entry route1, first of all on their own computer to install an open source framework, like TensorFlow, Caffe such, play this framework, the framework to use2, and then run some basic network, from the3, if there are conditions, the entire GPU computer, GPU run a lot faster, compared to the CPU To be more specific, I think you can follow these steps to learn it:First phase:1, realize and train only one layer of Softmax

the neural network can be used as a linear classifier, and then we can replace it with a classifier with better performance. During the study, we can find that adding the features obtained by automatic learning to the original features can greatly improve the accuracy, and even make the classification problem better than the current best classification algorithm! There are some variants of AutoEncoder. Her

Python vector: Import NumPy as np a = Np.array ([[[1,2],[3,4],[5,6]]) SUM0 = Np.sum (A, axis=0) sum1 = Np.sum (A, Axis=1) PR int SUM0 Print sum1 > Results: [9 12][3 7] Dropout In the training process of the deep Learning Network, for the Neural network unit, it is temporarily discarded from the network according to certain probability.Dropout is a big kill for CNN to prevent the effect of fitting. Output

Logical regression of machine learningIn the previous chapter, we learned about general linear regression, and now let's take a look at what the hell is logistic regression?In fact, from this point of view, I think that the logistic regression is not a return, but directly b

computing??? just Human-computer (HCI), it is interaction mac Hine learning driven by powerful algorithms (models) and nearly unlimited data processing. To understand a cognitive system that uses IoT sensors and deep-analysis, your A-learning to need the leap F ROM Advanced machine learning to neural networks. In the

This lesson mainly describes the processing of linear models. Including: 1. Input Representation) 2. Linear Classification) 3. Linear Regression) 4. nonlinear transformation) The author believes that to test the availability of a model, it is to use real data to do a good job. To explain how to apply

, that is, the retrieval and ranking,retrieval in the above figure are responsible for retrieving some of the user-related apps,ranking from the database to rate the apps of these retrieved, and finally, Returns the corresponding list to the user according to the score level. 3.2, the characteristics of apps recommendation Before training the model, the most important work is the preparation of the training data and the selection of features, in the apps recommendation, the data that can be used

= clusters.centers[clusters.predict (point)] return sqrt (sum ([X**2 to X in (Point-center)]) WSS SE = Parseddata.map (Lambda point:error (point)). Reduce (lambda x, y:x + y) print ("Within Set Sum of squared, error =" + STR (Wssse)) #聚类结果 def sort (point): Return Clusters.predict (point) Clusters_result = Parseddata.map (sort) # Save and load model # $example off$ print ' cluster result: ' Print clusters_result.collect () sc.stop () As you can see Using spark for machine

difference between music audio signals (semantic gap) is large, on the other hand, the factors that affect audience preferences are varied. Some of the information can be easily extracted from the audio signal, such as the type of music and playing instruments, while others are more challenging, such as the mood of music, and the release of the year (or period), and some are actually impossible to get from the audio: Just like the artist's location and lyrical theme.Despite these challenges, it

From this section, I started to go to "regular" machine learning. The reason is "regular" because it starts to establish a value function (cost function) and then optimizes the value function to obtain the weight, then test and verify. This entire process is an essential part of machine learning. The topic to learn today is logical regression, which is also a sup

The last time we shared a multivariate linear regression model (Linear Regression with multiple Variables), let's talk about polynomial regression (polynomial Regression) and the normal equation (normal equation). (we still disc

three, in recent years has ushered in the winter after the spring. Neural networks are mainly composed of neurons (neuron), a neuron is usually a linear combination of multiple inputs + an activation function, where the activation function is often a nonlinear function. Like the human brain, many neurons are combined with a variety of links to have a powerful ability. Andrew gave two simple examples at the outset. single neuron networks (neural netw

rise, below gradient descent) in real-world problems can be problematic,Here is the gradient descent algorithm, which is also used in the linear regression, the final optimization equation is the same as the above logical regression. The iteration formula is as follows:Every time you adjust to the direction of the W, you get the bias of the W, then you initializ

Chapter One introduction to Deep learning 1. Artificial sometimes not very good to extract the characteristics of the entity, then there is an automatic way. Yes, one of the key problems in the deep learning solution is to automatically combine simple features into more complex features and use these combination featur

/* First write the title, so you can often remind yourself * *From elsewhere there are many articles similar to this and do not know who is original because of the original text by less than the error, so the following changes to this and made the appropriate emphasis mark (the line see the content is not large clear and somewhat complex, the following operating flow according to the preceding operator to classify)Preliminary contactCalled the LR classifier (Logistic