Начните с Tensorflow по закону и статистике

sudo easy_install pip

pip3 install --upgrade virtualenv

virtualenv --system-site-packages 

cd 

source ./bin/activate

easy_install -U pip3

pip3 install tensorflow

pip3 install matplotlib

import tensorflow as tfimport numpy as npimport mathimport matplotlibmatplotlib.use('TkAgg')import matplotlib.pyplot as pltimport matplotlib.animation as animation

# Generate evidence numbers between 10 and 20# Generate a number of convictions from the evidence with a random noise addednp.random.seed(42)sampleSize = 200numEvid = np.random.randint(low=10, high=50, size=sampleSize)numConvict = numEvid * 10 + np.random.randint(low=200, high=400, size=sampleSize)

# Plot the data to get a feelingplt.title("Number of convictions based on evidence")plt.plot(numEvid, numConvict, "bx")plt.xlabel("Number of Evidence")plt.ylabel("Number of Convictions")plt.show(block=False)  # Use the keyword 'block' to override the blocking behavior

# create a function for normalizing values# use 70% of the data for training (the remaining 30% shall be used for testing)def normalize(array):    return (array - array.mean()) / array.std()

numTrain = math.floor(sampleSize * 0.7)

# convert list to an array and normalize arraystrainEvid = np.asanyarray(numEvid[:numTrain])trainConvict = np.asanyarray(numConvict[:numTrain])trainEvidNorm = normalize(trainEvid)trainConvictdNorm = normalize(trainConvict)

testEvid = np.asanyarray(numEvid[numTrain:])testConvict = np.asanyarray(numConvict[numTrain:])testEvidNorm = normalize(testEvid)testConvictdNorm = normalize(testConvict)

# define placeholders  and variablestfEvid = tf.placeholder(tf.float32, name="Evid")tfConvict = tf.placeholder(tf.float32, name="Convict")tfEvidFactor = tf.Variable(np.random.randn(), name="EvidFactor")tfConvictOffset = tf.Variable(np.random.randn(), name="ConvictOffset")

# define the operation for predicting the conviction based on evidence by adding both values# define a loss function (mean squared error)tfPredict = tf.add(tf.multiply(tfEvidFactor, tfEvid), tfConvictOffset)tfCost = tf.reduce_sum(tf.pow(tfPredict - tfConvict, 2)) / (2 * numTrain)

# set a learning rate and a gradient descent optimizerlearningRate = 0.1gradDesc = tf.train.GradientDescentOptimizer(learningRate).minimize(tfCost)

# initialize variablesinit = tf.global_variables_initializer()

with tf.Session() as sess:    sess.run(init)

    # set up iteration parameters    displayEvery = 2    numTrainingSteps = 50

    # Calculate the number of lines to animation    # define variables for updating during animation    numPlotsAnim = math.floor(numTrainingSteps / displayEvery)    evidFactorAnim = np.zeros(numPlotsAnim)    convictOffsetAnim = np.zeros(numPlotsAnim)    plotIndex = 0

    # iterate through the training data    for i in range(numTrainingSteps):

        # ======== Start training by running the session and feeding the gradDesc        for (x, y) in zip(trainEvidNorm, trainConvictdNorm):            sess.run(gradDesc, feed_dict={tfEvid: x, tfConvict: y})

        # Print status of learning        if (i + 1) % displayEvery == 0:            cost = sess.run(                tfCost, feed_dict={tfEvid: trainEvidNorm, tfConvict: trainConvictdNorm}            )            print(                "iteration #:",                "%04d" % (i + 1),                "cost=",                "{:.9f}".format(cost),                "evidFactor=",                sess.run(tfEvidFactor),                "convictOffset=",                sess.run(tfConvictOffset),            )

            # store the result of each step in the animation variables            evidFactorAnim[plotIndex] = sess.run(tfEvidFactor)            convictOffsetAnim[plotIndex] = sess.run(tfConvictOffset)            plotIndex += 1

    # log the optimized result    print("Optimized!")    trainingCost = sess.run(        tfCost, feed_dict={tfEvid: trainEvidNorm, tfConvict: trainConvictdNorm}    )    print(        "Trained cost=",        trainingCost,        "evidFactor=",        sess.run(tfEvidFactor),        "convictOffset=",        sess.run(tfConvictOffset),        "\n",    )

# de-normalize variables to be plotable again    trainEvidMean = trainEvid.mean()    trainEvidStd = trainEvid.std()    trainConvictMean = trainConvict.mean()    trainConvictStd = trainConvict.std()    xNorm = trainEvidNorm * trainEvidStd + trainEvidMean    yNorm = (        sess.run(tfEvidFactor) * trainEvidNorm + sess.run(tfConvictOffset)    ) * trainConvictStd + trainConvictMean

    # Plot the result graph    plt.figure()

    plt.xlabel("Number of Evidence")    plt.ylabel("Number of Convictions")

    plt.plot(trainEvid, trainConvict, "go", label="Training data")    plt.plot(testEvid, testConvict, "mo", label="Testing data")    plt.plot(xNorm, yNorm, label="Learned Regression")    plt.legend(loc="upper left")

    plt.show()

    # Plot an animated graph that shows the process of optimization    fig, ax = plt.subplots()    line, = ax.plot(numEvid, numConvict)

    plt.rcParams["figure.figsize"] = (10, 8) # adding fixed size parameters to keep animation in scale    plt.title("Gradient Descent Fitting Regression Line")    plt.xlabel("Number of Evidence")    plt.ylabel("Number of Convictions")    plt.plot(trainEvid, trainConvict, "go", label="Training data")    plt.plot(testEvid, testConvict, "mo", label="Testing data")

    # define an animation function that changes the ydata    def animate(i):        line.set_xdata(xNorm)        line.set_ydata(            (evidFactorAnim[i] * trainEvidNorm + convictOffsetAnim[i]) * trainConvictStd            + trainConvictMean        )        return (line,)

    # Initialize the animation with zeros for y    def initAnim():        line.set_ydata(np.zeros(shape=numConvict.shape[0]))        return (line,)

    # call the animation    ani = animation.FuncAnimation(        fig,        animate,        frames=np.arange(0, plotIndex),        init_func=initAnim,        interval=200,        blit=True,    )

    plt.show()