Don’t use deep learning your data isn’t that big · Simply Statistics

Don't use deep learning your data isn't that big

  • Don’t use deep learning your data isn’t that big

    The other day Brian was at a National Academies meeting and he gave one of his usual classic quotes:

    When I saw that quote I was reminded of the blog post Don’t use hadoop – your data isn’t that big.

  • Just as with Hadoop at the time that post was written – deep learning has achieved a level of mania and hype that means people are trying it for every problem.
  • The issue is that only a very few places actually have the data to do deep learning.
  • But I’ve always thought that the major advantage of using deep learning over simpler models is that if you have a massive amount of data you can fit a massive number of parameters.
  • If you are Google, Amazon, or Facebook and have near infinite data it makes sense to deep learn.

Best quote from NAS DS Round Table: “I mean, do we need deep learning to analyze 30 subjects?” – B Caffo @simplystats #datascienceinreallife
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TensorFlow in a Nutshell — Part Three: All the Models

TensorFlow in a Nutshell — Part Three

  • X = tf.reshape(X, [-1, 28, 28, 1]) # first conv layer will compute 32 features for each 5×5 patch with tf.variable_scope(‘conv_layer1’): h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5], bias=True, activation=tf.nn.relu) h_pool1 = max_pool_2x2(h_conv1) # second conv layer will compute 64 features for each 5×5 patch.
  • Getting the best of both worlds.
  • This type of model can be used for classification and regression problems.
  • The last layer in the network produces the output.
  • Convolution Neural Networks are unique because they’re created in mind that the input will be an image.

The fast and easy guide to the most popular Deep Learning framework in the world.
Continue reading “TensorFlow in a Nutshell — Part Three: All the Models”

The Gentlest Introduction to Tensorflow – Part 2

The Gentlest Introduction to #Tensorflow Part 2  #DeepLearning #MachineLearning @reculture_us

  • Calculate prediction (y) & cost using a single datapoint
  • Using a variety of datapoints generalizes our model, i.e., it learns W, b values that can be used to predict any feature value.
  • For simplicity, we use least minimum squared error (MSE) as our cost function.
  • Create a TF Graph with model & cost, and initialize W, b with some values
  • We select a datapoint (x, y [C], and feed [D] it into the TF Graph to get the prediction (y) as well as the cost.

Read the full article, click here.


@kdnuggets: “The Gentlest Introduction to #Tensorflow Part 2 #DeepLearning #MachineLearning @reculture_us”


 
In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/$).


The Gentlest Introduction to Tensorflow – Part 2

The Gentlest Introduction to Tensorflow – Part 1

The Gentlest Introduction to Tensorflow Part 1  #MachineLearning #DeepLearning @reculture_us

  • In the spirit of keeping things simple, we will model our data points using a linear model.
  • With the concepts of linear model, cost function, and gradient descent in hand, we are ready to use TF.
  • To compare which model is a better-fit more rigorously, we define best-fit mathematically as a cost function that we need to minimize.
  • Minimizing the cost function is similar because, the cost function is undulating like the mountains (chart below), and we are trying to find the minimum point, which we can similarly achieve through gradient descent.
  • We cannot predict values for features that we don’t have data points for (chart below)

Read the full article, click here.


@kdnuggets: “The Gentlest Introduction to Tensorflow Part 1 #MachineLearning #DeepLearning @reculture_us”


In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.


The Gentlest Introduction to Tensorflow – Part 1