Google releases new TensorFlow Object Detection API

  • Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images.
  • Google is trying to offer the best of simplicity and performance — the models being released today have performed well in benchmarking and have become regularly used in research.
  • The handful of models included in the detection API include heavy duty inception-based convolutional neural networks and streamlined models designed to operate on less sophisticated machines — a MobileNets single shot detector comes optimized to run in real-time on a smartphone.
  • Earlier this week Google announced its MobileNets family of lightweight computer vision models.
  • Google, Facebook and Apple have been pouring resources into these mobile models.

Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images. Google is trying..
Continue reading “Google releases new TensorFlow Object Detection API”

Teaching an AI to write Python code with Python code • Will cars dream?

Teaching an #AI to write Python code with Python code  #MachineLearning #DeepLearning

  • So we want to train a neural net to write some Python code.
  • We can now write our code to train a LSTM network on Python code.
  • Teaching an AI to write Python code with Python code
  • The network takes a few hours to train.
  • You will be able to write code directly in your browser and have it run on your instance.

OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.
Continue reading “Teaching an AI to write Python code with Python code • Will cars dream?”

Photon ML Tutorial · linkedin/photon-ml Wiki · GitHub

.@LinkedIn #MachineLearning team shares tutorial on building #Recommender systems

  • You will need to install Docker or Docker Toolbox on your system to use it.
  • After you have installed Docker, launch the Docker daemon (this happens automatically on some systems).
  • Remove the tutorial container by running docker rm photon-ml-tutorial .
  • To access the Photon ML Tutorial, navigate to http://localhost:5334/tutorial .

Read the full article, click here.


@kdnuggets: “.@LinkedIn #MachineLearning team shares tutorial on building #Recommender systems”


photon-ml – A scalable machine learning library on Apache Spark


Photon ML Tutorial · linkedin/photon-ml Wiki · GitHub

Teaching an AI to write Python code with Python code • Will cars dream?

Teaching an #AI to write Python code with Python code  #MachineLearning #DeepLearning

  • So we want to train a neural net to write some Python code.
  • We can now write our code to train a LSTM network on Python code.
  • Teaching an AI to write Python code with Python code
  • The network takes a few hours to train.
  • You will be able to write code directly in your browser and have it run on your instance.

Read the full article, click here.


@MikeTamir: “Teaching an #AI to write Python code with Python code #MachineLearning #DeepLearning”


OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.


Teaching an AI to write Python code with Python code • Will cars dream?

Teaching an AI to write Python code with Python code • Will cars dream?

Teaching #AI to write #Python code with #Python code #DeepLearning #LSTM #MachineLearning

  • So we want to train a neural net to write some Python code.
  • We can now write our code to train a LSTM network on Python code.
  • Teaching an AI to write Python code with Python code
  • The network takes a few hours to train.
  • You will be able to write code directly in your browser and have it run on your instance.

Read the full article, click here.


@kdnuggets: “Teaching #AI to write #Python code with #Python code #DeepLearning #LSTM #MachineLearning”


OK, let’s drop autonomous vehicles for a second. Things are getting serious. This post is about creating a machine that writes its own code. More or less.


Teaching an AI to write Python code with Python code • Will cars dream?

GitHub

A handful of #TensorFlow tutorials:  #DeepLearning #MachineLearning

  • Tensorflow Tutorials using Jupyter Notebook
  • Machine Learing Basics with TensorFlow: Linear Regression / Logistic Regression with MNIST / Logistic Regression with Custom Dataset
  • Hope the tutorials to be a useful recipe book for your deep learning projects.
  • Tried to explain as kindly as possible, as these tutorials are intended for TensorFlow beginners.
  • Most of the codes are simple refactorings of Aymeric Damien’s Tutorial or Nathan Lintz’s Tutorial .

Read the full article, click here.


@randal_olson: “A handful of #TensorFlow tutorials: #DeepLearning #MachineLearning”


Tensorflow-101 – TensorFlow Tutorials


GitHub

GitHub

Very interesting code, lda2vec tools for interpreting natural language  #machinelearning #NLP

  • We build a model that builds both word and document topics, makes them interpreable, makes topics over clients, times, and documents, and makes them supervised topics.
  • lda2vec also yields topics over clients.
  • lda2vec the topics can be ‘supervised’ and forced to predict another target.
  • It’s research software, and we’ve tried to make it simple to modify lda2vec and to play around with your own custom topic models.
  • LDA on the other hand is quite interpretable by humans, but doesn’t model local word relationships like word2vec.

Read the full article, click here.


@andradeandrey: “Very interesting code, lda2vec tools for interpreting natural language #machinelearning #NLP”


Contribute to lda2vec development by creating an account on GitHub.


GitHub