Welcome to the Machine Learning, Deep Learning, and AI Forum

Discussion in 'Machine Learning, Deep Learning, and AI' started by Patrick, May 11, 2017.

  1. Patrick

    Patrick Administrator
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    This is at least a year too late but it is time. This is going to be our official forum for ML/ DL/ AI and likely all things GPU, FPGA and MIC compute accelerated.
     
    #1
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  2. TangoWhiskey9

    TangoWhiskey9 Active Member

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  3. marcoi

    marcoi Active Member

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    might be nice to have if you could put together a newbie post on how to get started for those who want to tinker. Especially if there is a cheap way for the home lab user to play, I think that would get a few more people interested in it.
     
    #3
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  4. Patrick

    Patrick Administrator
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  5. marcoi

    marcoi Active Member

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    @Patrick - great articles on hardware. I got enough to play with using my laptop with my external 1080 card. Next questions is how do i start after i get the hardware? Are there software packages? Do I need to code something? What would be a good way to get my feet wet to learn about the tech and what it can do and how i can use it? Maybe after doing that i might decide it's not something i would be interested in but ATM i don't have enough exposure/experience to decide that. And if this isn't the right place to learn that, no worries, I figured it wouldn't hurt to ask.
     
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    Patrick likes this.
  6. Patrick

    Patrick Administrator
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    #6
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  7. JackieAI

    JackieAI New Member

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    Hi All,
    I'm attending this Deep Learning Webinar next week. I wanted to post if anyone is interested. You can register for free.

    https://register.gotowebinar.com/register/386638150584367875
     

    Attached Files:

    #7
  8. yuzmemak

    yuzmemak New Member

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    I like new things. I want to be in, as well.


    Tapatalk kullanarak iPad aracılığıyla gönderildi
     
    #8
  9. Terry Hasan

    Terry Hasan New Member

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    Hi Patrick, Is this still an active forum?

    Best,
    Terry
     
    #9
  10. Patrick

    Patrick Administrator
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    I am now devoting about 10% of my weeks to working on getting more content for this segment going forward. 2018 is going to be a big year.
     
    #10
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  11. Regiane Folter

    Regiane Folter New Member

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    Hey there! That's a good idea. Can I give you a suggestion? It's a post+tutorial for creating a ML application based on soccer results. For ML and soccer fans haha! And it's focused on beginners, so I think you will find it useful :)
     
    #11
  12. vishabhgoel

    vishabhgoel New Member

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    Hi All,

    I have a Dataset which explains the quality of wines based on the factors like acid contents, density, pH, etc. I am attaching the dataset.csv file which will show you the various data. According to the dataset we need to use the Multi Class Classification Algorithm to Analyze this dataset using Training and test data. Please correct me if I am wrong?

    Also I have used Principal Component Analysis Algorithm to work with this dataset. Below is the code I have used :-

    # -*- coding: utf-8 -*-
    """
    Created on Sun Aug 26 14:14:44 2018

    @author: 1022316
    """

    # Wine Quality testing
    #Multiclass classification - PCA

    #importing the libraries
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd

    #importing the Dataset
    dataset = pd.read_csv('C:\Machine learning\winequality-red_1.csv')
    X = dataset.iloc[:, 0:11].values
    y = dataset.iloc[:, 11].values

    # Splitting the dataset into the Training set and Test set
    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

    # Feature Scaling
    from sklearn.preprocessing import StandardScaler
    sc = StandardScaler()
    X_train = sc.fit_transform(X_train)
    X_test = sc.transform(X_test)

    #Applying the PCA
    from sklearn.decomposition import PCA
    pca = PCA(n_components = 2 )
    X_train = pca.fit_transform(X_train)
    X_test = pca.fit_transform(X_test)
    explained_variance = pca.explained_variance_ratio_

    # Fitting Logistic Regression to the Training set
    #from sklearn.tree import DecisionTreeClassifier
    #classifier = DecisionTreeClassifier(max_depth = 2).fit(X_train, y_train)
    #y_pred = classifier.predict(X_test)

    #classifier = LogisticRegression(random_state = 0)
    #classifier.fit(X_train, y_train)

    #Fiiting the Logistic Regression model to the training set
    from sklearn.linear_model import LogisticRegression
    classifier = LogisticRegression(random_state = 0)
    classifier.fit(X_train, y_train)


    #Predicting thr Test set results
    y_pred = classifier.predict(X_test)


    # Making the Confusion Matrix
    from sklearn.metrics import confusion_matrix
    cm = confusion_matrix(y_test, y_pred)


    Please let me know if I am using the correct algorithm of this dataset. Also, as I can see we have 9 classes in which this dataset will be divided. Please also let me know how will I visualize and plot the data accordingly in different classes.

    I will be attentive to the reply.

    Regards,
    Vishabh Goel
     

    Attached Files:

    #12
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