Відео

garbage

Could you please tell us what you did at the end of line 91? It's not visible in the video...

github repo doesn't appear to exist?

nowadays no one use this shit language for data science

Thank you for a very clear overview. Also, this guy has most calm, reassuring voice of anyone I've ever heard in a UA-cam tech video. I think my blood pressure is measurably lower now than it was when I started watching.

how can we import a .h5 model file and test in java without the json file

Ah, 2016, no greetings with a guy on cocaine "WHAAATS UP GUYS, WELCOME TO MY CHANNEL, THUMBS UP, SUBSCRIBE AND COMMENT, YES, EVEN BEFORE YOU HAVE SEEN THE FIRST 10 SECONDS OF THE VIDEO, YES, I ON DRUGS, ENERGY DRINKS, I WANT TO BECOME A UA-camR... ". No, instead a calm voice, sovereign, presenting only the facts. What a joy. I miss 2016.

I have to say that it is weird how I see this 6 years old video, is like best Java AI related video in youtube. props for creator, Deeplearning4j Apis have changed little so this is outdated. Java AI promotion is so bad state, first I get terrible Tensor flow videos(it Java api 20% features implemented is worst possible choise for Java dev), Python it is good for it but not Java. So much of AI community has moved using Python that is even hard to find good videos for Java about subject. some small changes to made it work, if this helps someone .. RecordReader rr = new CSVRecordReader('\t'); .. RecordReader rrTest = new CSVRecordReader('\t'); Nesterovs updater = new Nesterovs(); updater.setMomentum(0.9); updater.setLearningRate(learningRate); conf.. .seed(seed).maxNumLineSearchIterations(1).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(updater) .. .weightInit(WeightInit.XAVIER).activation(Activation.RELU) .. conf.setBackpropType(BackpropType.Standard); also i ovverride ScoreIterationListener class to print my screen .. INDArray features = t.getFeatures(); worked fine

Great video.

EXCELLENT!

The deeplearning4j api has changed so much that this tutorial is now somehow hard to follow using the latest API for DL4J.... To follow the tutorial, make sure you use the 0.7.0 version of DeepLearning4J

Hi. could you post another series with the latest DL4j 1.0.0 Beta 7? I am having major problems with my code. thanks.

There is no pom.xml file in the outer most file directory so when I run mvn clean install it doesn't work, and when you try to run it on the other pom.xml files it runs but get's stuck with no result. Outdated video maybe?

Could I have a link for download?

Idk why but this sound scary

Wtf was that.....I didn't learn anything!!!

getting below exception when i try to run in IntelliJ. followed all instructions in the video. do we have a separate set up for the IRISData ? Exception in thread "main" java.io.IOException: Server returned HTTP response code: 403 for URL: @t at java.base/sun.net.www.protocol.http.HttpURLConnection.getInputStream0(HttpURLConnection.java:1919) at java.base/sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1515) at java.base/sun.net.www.protocol.https.HttpsURLConnectionImpl.getInputStream(HttpsURLConnectionImpl.java:250)

Most soothing nn tutorial ever

Bots, where?

omg java :(

We are writing this and then writing this .. . . what is THIS ?? So just give us the codes if you dont explain .

last part of the code is cut off.... incompetency is astounding..

Which software tool is used?

it keeps saying" the java : package com.sun.media.sound does not exist" and gives a bunch of errors when i try to run any of the examples

I want to use Scala, and not Java, but these videos are nice and clear and will help a lot! Thanks!

playlist would be useful edit ua-cam.com/play/PL9iheGibFMtrJZJFrTIdd-QNQyjDX5v2X.html

Looking so long at 'File' without 'new' was a very disturbing experience. Also code formatting in Idea is very fast and accessible, but idk if it exists in CE.

sir can u just copying the code on command please

Thanks you for your guiding video. I followed all your steps, and got to the final result. Except, IntelliJ and JDK 11 doest support JavaFx which is used in class ImageDrawer in the folder dataexamples in the folder of the directory src,main,java

the website doesn't work I clicked on it and it said 404

Subscribed

Ok, this all is cool, but... How I can extract an image path in evaluation part of code? So maybe I can send to a network the image, resized to 28x28 and get prediction category? I don't understand yet how to extract concrete category(int value)... Does can anybody respond me how ...?

What are hidden nodes, seed, updater, momentum, dense layer builder, iteration, weight init?

do you need to always run mvn clean install?

Here is my code that is updated to the latest version of this software: package net.neural; import org.datavec.api.records.reader.RecordReader; import org.datavec.api.records.reader.impl.csv.CSVRecordReader; import org.datavec.api.split.FileSplit; import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator; import org.deeplearning4j.eval.Evaluation; import org.deeplearning4j.nn.api.OptimizationAlgorithm; import org.deeplearning4j.nn.conf.MultiLayerConfiguration; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.layers.DenseLayer; import org.deeplearning4j.nn.conf.layers.OutputLayer; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.optimize.listeners.ScoreIterationListener; import org.nd4j.linalg.activations.Activation; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; import org.nd4j.linalg.learning.config.Nesterovs; import org.nd4j.linalg.lossfunctions.LossFunctions; import java.io.File; public class MLPClassifierLinear { public static void main(String[] args) { int seed = 123; double learningRate = 0.01; int batchSize = 50; int numberOfEpochs = 3; //30 is way more than you'll need, you get 100% accuracy, 3 is enough for 96% accuracy int numberOfInputs = 2; int numberOfOutputs = 2; int numberOfHiddenNodes = 20; //load training data RecordReader recordReader = new CSVRecordReader(); File testFile = new File("[pathToWhereIRanGitCloneFromOnWindows]\\dl4j-examples\\dl4j-examples\\src\\main\ esources\\classification\\linear_data_train.csv"); try { recordReader.initialize(new FileSplit(testFile)); } catch (Exception excecption) { excecption.printStackTrace(); } DataSetIterator trainingDataSetIterator = new RecordReaderDataSetIterator(recordReader, batchSize, 0, 2); //load evaluation data RecordReader recordReaderEvaluation = new CSVRecordReader(); File evaluationFile = new File("[pathToWhereIRanGitCloneFromOnWindows]]\\dl4j-examples\\dl4j-examples\\src\\main\ esources\\classification\\linear_data_eval.csv"); try { recordReaderEvaluation.initialize(new FileSplit(evaluationFile)); } catch (Exception excecption) { excecption.printStackTrace(); } DataSetIterator evaluationDataSetIterator = new RecordReaderDataSetIterator(recordReaderEvaluation, batchSize, 0, 2); MultiLayerConfiguration multiLayerConfiguration = new NeuralNetConfiguration.Builder() .seed(seed) .maxNumLineSearchIterations(1) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new Nesterovs(learningRate,0.5)) .list() .layer(0, new DenseLayer.Builder() .nIn(numberOfInputs) .nOut(numberOfHiddenNodes) .weightInit(WeightInit.XAVIER) .activation(Activation.RELU) .build() ) .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .nIn(numberOfHiddenNodes) .nOut(numberOfOutputs) .weightInit(WeightInit.XAVIER) .activation(Activation.SOFTMAX) .build() ) .pretrain(false).backprop(true).build(); MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(multiLayerConfiguration); multiLayerNetwork.init(); multiLayerNetwork.setListeners(new ScoreIterationListener(10)); for(int n = 0; n < numberOfEpochs; n++){ multiLayerNetwork.fit(trainingDataSetIterator); } Evaluation evaluation = new Evaluation(numberOfOutputs); while(evaluationDataSetIterator.hasNext()){ DataSet dataSet = evaluationDataSetIterator.next(); INDArray features = dataSet.getFeatureMatrix(); INDArray labels = dataSet.getLabels(); INDArray predicted = multiLayerNetwork.output(features, false); evaluation.eval(labels, predicted); } System.out.println(evaluation.stats()); } }

None of these terminal commands work on windows. What should Windows users do?

Where can I get the image data folder in training and testing?

How do we go about importing the Keras Tokenizer that has been fit on files for certain vocabulary size? Appreciate if you can provide any pointers

Just coding without any semantic information about what he is actually doing. Very bad!!! Especially for beginners in ML.

Thank You! excellent tutorial, I changed the speed to x1.5 much better

Hi, I wanna know how to convert hand written text to binary text using java. Thanks

Is that working with Android Studio??

Okay, if this is outdated is there a (link to a) current one available that could be posted here?

Can you please leave out unnecessary dependencies as well? There are a lot of dependencies in this project, and I don't see the necessity of it all. I want to know as bare bones as possible how to use DeepLearning4j.

is this outdated? I get an error for org.deeplearning4j.nn.conf.Updator.NESTEROVS

I did everything step by step, but it gives me "UnsatisfiedLinkError: no gfortran in java.library.path" while running. I am using LinuxMint

Sadly, I can't figure out how to set up the project. It simply doesn't work for me. It seems to start from the middle of no where.

how do we save a word2vec model? It looks like Modelserializer is not able to handle a model when it is created as: Word2Vec model = new Word2Vec.Builder();

Hello again, I just wanted to ask if the DenseLayer is more like an input layer oder a hidden layer? And if it's not an input layer, if we do not have to configure an input layer?

Error:"method call expected java" Just found my mistake after quite some time desperate search. XD Hope it helps for those typing while watching. In 06:38 "File(...)" was used, in 14:02 you'll see the correct version. "new File(...)".