Machine Learning A-Z™: Hands-On Python & R In Data Science Coupon Save 95 %

Machine Learning A-Z™: Hands-On Python & R In Data Science
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  • The course is fun and exciting, but at the same time we dive deep into Machine Learning.
  • Part 4 – Clustering: K-Means, Hierarchical Clustering
  • Part 10 – Model Selection & Boosting: k-fold Cross Validation, XGBoost
  • We will walk you step-by-step into the World of Machine Learning.
  • The course is packed with practical exercises which are based on live examples.

Coupon 100 10 15 75 Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Continue reading “Machine Learning A-Z™: Hands-On Python & R In Data Science Coupon Save 95 %”

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science
☞

  • The course is structured in a fun and exciting way, but at the same time we dive deep into Machine Learning.
  • We will walk you step-by-step into the World of Machine Learning.
  • The course is packed with practical exercises which are based on live examples.
  • The course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
  • And as a bonus, this course includes both R and Python code templates which you can download and use on your own projects.

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
Continue reading “Machine Learning A-Z™: Hands-On Python & R In Data Science”

Artificial Intelligence Takes a Trip on the London Underground

Artificial Intelligence takes a trip on the #London underground

  • Feedforward networks and other neural network varieties have been proved useful for a variety of tasks in computer science and psychology .
  • Neural networks have their limits, in part because they don’t possess good memories-whatever memory they do have is encoded in the computations they perform, and those computations depend in turn on their (ongoing) training.
  • Over the decades, researchers developed more capable neural networks, often by layering perceptrons on top of one another in what’s called a feedforward network-so named because each layer takes a set of inputs, performs a set of simple computations, and feeds its results forward to the next layer.
  • Researchers have devised a system that can learn sophisticated tasks, like figuring out where in the heck you are.
  • A simple way around the, the DeepMind team figured, was to add a memory module, where the network could store and retrieve information related to the situation it found itself in.

Researchers have devised a system that can learn sophisticated tasks, like figuring out where in the heck you are.
Continue reading “Artificial Intelligence Takes a Trip on the London Underground”

How does human behavior influence the work place ? – Thoughts by StatusToday

Discover 7 dangerous ways #human behaviour influences your company  #infographic #AI

  • StatusToday is an Employee Insights Platform to ensure security, engagement and productivity, through patent-pending AI that understands human behavior.
  • Our technology offers a simple way to understand your employees and enable you to reach your key business objectives.
  • The AI-driven solution has been proven to streamline risk management and supplement leadership in business settings.
  • Don’t miss StatusToday’s next story

The AI-driven solution has been proven to streamline risk management and supplement leadership in business settings. Our technology offers…
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Joel Grus – Fizz Buzz in Tensorflow

Really, really deep #DeepLearning #Humor: Fizz Buzz in #Tensorflow (mostly works)

  • interviewer: Before you get too far astray, the problem you’re supposed to be solving is to generate fizz buzz for the numbers from 1 to 100.
  • interviewer: OK, so I need you to print the numbers from 1 to 100, except that if the number is divisible by 3 print “fizz”, if it’s divisible by 5 print “buzz”, and if it’s divisible by 15 print “fizzbuzz”.
  • We want the input to be a number, and the output to be the correct “fizzbuzz” representation of the number.
  • me: So, once the model has been trained, it’s fizz buzz time.
  • array ([ binary_encode ( i , NUM_DIGITS ) for i in range ( 101 , 2 ** NUM_DIGITS )]) trY = np .

Read the full article, click here.


@kdnuggets: “Really, really deep #DeepLearning #Humor: Fizz Buzz in #Tensorflow (mostly works)”


Posts and writings by Joel Grus


Joel Grus – Fizz Buzz in Tensorflow

Joel Grus – Fizz Buzz in Tensorflow

  • interviewer: Before you get too far astray, the problem you’re supposed to be solving is to generate fizz buzz for the numbers from 1 to 100.
  • interviewer: OK, so I need you to print the numbers from 1 to 100, except that if the number is divisible by 3 print “fizz”, if it’s divisible by 5 print “buzz”, and if it’s divisible by 15 print “fizzbuzz”.
  • We want the input to be a number, and the output to be the correct “fizzbuzz” representation of the number.
  • me: So, once the model has been trained, it’s fizz buzz time.
  • array ([ binary_encode ( i , NUM_DIGITS ) for i in range ( 101 , 2 ** NUM_DIGITS )]) trY = np .

Read the full article, click here.


@randal_olson: “”Fizz Buzz in #TensorFlow”

Proper way to respond to silly #programming interviews.”


Posts and writings by Joel Grus


Joel Grus – Fizz Buzz in Tensorflow