Humans are still quicker than robots at learning to drive

Humans are still quicker than robots at learning to drive

  • Computer giant Nvidia developed a convolutional neural network (CNN) to learn how to steer a car in any weather condition, using only the data taken from cameras and a car’s steering wheel.
  • “A small amount of training data from less than 100 hours of driving was sufficient to train the car to operate in diverse conditions, on highways, local and residential roads in sunny, cloudy and rainy conditions.”
  • By collecting data from roads in New Jersey, Nvidia was able to train the CNN to steer a car the same way a human does in the same conditions.
  • Artificial intelligence takes less than 100 hours to learn to drive-slower than a human.
  • Nvidia’s deep-learning algorithm can teach a car to drive itself in all weather conditions.

Read the full article, click here.


@Newsweek: “Humans are still quicker than robots at learning to drive”


Artificial intelligence takes less than 100 hours to learn to drive—slower than a human.


Humans are still quicker than robots at learning to drive

Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium

Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium

  • More data makes the problem harder for our neural network to solve, but we can compensate for that by making our network bigger and able to learn more complicated patterns.
  • We need to be smarter about how we process images into our neural network.
  • But now we want to process images with our neural network.
  • Our program can now recognize birds in images!
  • Step 1: Break the image into overlapping image tiles

Read the full article, click here.


@MikeTamir: “Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium”


Update: Machine Learning is Fun! Part 4 is now available!


Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium

Artificial Intelligence: Don’t Fear It, Embrace It

Artificial Intelligence: Don't Fear It, Embrace It | #BigData #Artificialintelligence #RT

  • Many intermediary steps had to be taken to teach machine learning systems.
  • “We’re seeing the point at which data-driven deep learning systems are starting to overtake systems that we’ve engineered ourselves,” said Coates. “
  • Big data could drive the next big security strategy shift.
  • Thanks to mobile devices, data that’s useful to deep learning systems is being generated at an ever growing rate.
  • But traditional machine learning hit a wall, Coates said.

Read the full article, click here.


@Ronald_vanLoon: “Artificial Intelligence: Don’t Fear It, Embrace It | #BigData #Artificialintelligence #RT”


Adam Coates, the director of the Baidu Research’s Silicon Valley AI Lab, says don’t fear artificial intelligence. Instead, look to it to save lives. He spoke at the InformationWeek Elite 100 Conference this week.


Artificial Intelligence: Don’t Fear It, Embrace It

Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.

#Deeplearning tutorial on #Caffe technology : basic commands, #Python and C++ code

  • ‘Data’ : for data saved in a LMDB database, such as before
  • net.blobs[‘data’] contains input data, an array of shape (1, 1, 100, 100) net.blobs[‘conv’] contains computed data in layer ‘conv’ (1, 3, 96, 96)
  • Learn : solve the params on training data
  • /data/ilsvrc12/get_ilsvrc_aux.sh #have a look at the model python python/draw_net.py models/bvlc_reference_caffenet/deploy.prototxt caffenet.png open caffenet.png
  • Let’s create a layer to add a value.

Read the full article, click here.


@deeplearningldn: “#Deeplearning tutorial on #Caffe technology : basic commands, #Python and C++ code”


Disrupting SASU. Christopher Bourez


Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.

Keras: Deep Learning library for Theano and TensorFlow

Keras:Deep Learning library for Theano & TensorFlow Tutorial   #DataScience #MachineLearning

  • The core data structure of Keras is a model , a way to organize layers.
  • By default, Keras will use Theano as its tensor manipulation library.
  • The main type of model is the Sequential model, a linear stack of layers.
  • To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
  • Getting started: 30 seconds to Keras

Read the full article, click here.


@gcosma1: “Keras:Deep Learning library for Theano & TensorFlow Tutorial #DataScience #MachineLearning”


Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.


Keras: Deep Learning library for Theano and TensorFlow

Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.

#DeepLearning tutorial on Caffe technology: basic commands, #Python and C++ code

  • ‘Data’ : for data saved in a LMDB database, such as before
  • net.blobs[‘data’] contains input data, an array of shape (1, 1, 100, 100) net.blobs[‘conv’] contains computed data in layer ‘conv’ (1, 3, 96, 96)
  • Learn : solve the params on training data
  • /data/ilsvrc12/get_ilsvrc_aux.sh #have a look at the model python python/draw_net.py models/bvlc_reference_caffenet/deploy.prototxt caffenet.png open caffenet.png
  • Let’s create a layer to add a value.

Read the full article, click here.


@kdnuggets: “#DeepLearning tutorial on Caffe technology: basic commands, #Python and C++ code”


Disrupting SASU. Christopher Bourez


Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.

Artificial Intelligence: Don’t Fear It, Embrace It

Artificial Intelligence: Don't Fear It, Embrace It | #BigData #Artificialintelligence #RT

  • Many intermediary steps had to be taken to teach machine learning systems.
  • “We’re seeing the point at which data-driven deep learning systems are starting to overtake systems that we’ve engineered ourselves,” said Coates. “
  • Big data could drive the next big security strategy shift.
  • Thanks to mobile devices, data that’s useful to deep learning systems is being generated at an ever growing rate.
  • But traditional machine learning hit a wall, Coates said.

Read the full article, click here.


@Ronald_vanLoon: “Artificial Intelligence: Don’t Fear It, Embrace It | #BigData #Artificialintelligence #RT”


Adam Coates, the director of the Baidu Research’s Silicon Valley AI Lab, says don’t fear artificial intelligence. Instead, look to it to save lives. He spoke at the InformationWeek Elite 100 Conference this week.


Artificial Intelligence: Don’t Fear It, Embrace It

Artificial Intelligence: Don’t Fear It, Embrace It

Artificial Intelligence: Don't Fear It, Embrace It | #BigData #Artificialintelligence #RT

  • Many intermediary steps had to be taken to teach machine learning systems.
  • “We’re seeing the point at which data-driven deep learning systems are starting to overtake systems that we’ve engineered ourselves,” said Coates. “
  • Big data could drive the next big security strategy shift.
  • Thanks to mobile devices, data that’s useful to deep learning systems is being generated at an ever growing rate.
  • But traditional machine learning hit a wall, Coates said.

Read the full article, click here.


@Ronald_vanLoon: “Artificial Intelligence: Don’t Fear It, Embrace It | #BigData #Artificialintelligence #RT”


Adam Coates, the director of the Baidu Research’s Silicon Valley AI Lab, says don’t fear artificial intelligence. Instead, look to it to save lives. He spoke at the InformationWeek Elite 100 Conference this week.


Artificial Intelligence: Don’t Fear It, Embrace It

Artificial Intelligence: Don’t Fear It, Embrace It

Artificial Intelligence: Don't Fear It, Embrace It | #BigData #Artificialintelligence #RT

  • Many intermediary steps had to be taken to teach machine learning systems.
  • “We’re seeing the point at which data-driven deep learning systems are starting to overtake systems that we’ve engineered ourselves,” said Coates. “
  • Big data could drive the next big security strategy shift.
  • But traditional machine learning hit a wall, Coates said.
  • Adam Coates, director of Baidu Research’s Silicon Valley AI Lab , isn’t worried about artificial intelligence taking over the world.

Read the full article, click here.


@Ronald_vanLoon: “Artificial Intelligence: Don’t Fear It, Embrace It | #BigData #Artificialintelligence #RT”


Adam Coates, the director of the Baidu Research’s Silicon Valley AI Lab, says don’t fear artificial intelligence. Instead, look to it to save lives. He spoke at the InformationWeek Elite 100 Conference this week.


Artificial Intelligence: Don’t Fear It, Embrace It

The (fizz) buzz around TensorFlow and machine learning

Sure, you’ve probably written a Fizz Buzz test. But can you do it in @TensorFlo? Learn how:

  • In his recent blog post ” Fizz Buzz in TensorFlow ,” Grus imagines he’s asked to solve Fizz Buzz as part of a job interview.
  • With Fizz Buzz, you print the numbers from 1 to 100, except if it is divisible by 3, you print “fizz”; if it’s divisible by 5, you print “buzz”; and if it’s divisible by 15 you print “fizzbuzz.”
  • If you’ve ever learned to program, you’ve probably written a Fizz Buzz test.
  • Because Grus is building a multi-layer perceptron – a neural network – to let the computer learn from the training data set (the actual Fizz Buzz results) and let it predict whether you’ll get Fizz or Buzz for each number.
  • Check out the full post for details on how Grus builds a simple neural network with TensorFlow to predict FizzBuzz numbers.

Read the full article, click here.


@googlecloud: “Sure, you’ve probably written a Fizz Buzz test. But can you do it in @TensorFlo? Learn how:”


The blog post Fizz Buzz in TensorFlow by Joel Grus raised buzz by imagining a machine learning solution to Fizz Buzz.


The (fizz) buzz around TensorFlow and machine learning