• Take Two Algorithms and Call Me in the Morning
  Data Science Bowl winners use deep learning to speed crucial step in drug discovery...

• Food Discovery with Uber Eats: Building a Query Understanding Engine
  At Uber Eats, we strive to help eaters find the exact food they want as effortlessly as possible. We approach this task through search and recommendation technologies...

• Metacar:
  A reinforcement learning environment for self-driving cars in the browser.
  Check out examples of algorithms created with metacar...

• Taskonomy: Disentangling Task Transfer Learning
  We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space...

• Exploring Tensorflow for Poets, or, Building a Pottery Classifier
  In our recent paper, ‘Fleshing Out the Bones’ we used the trained ‘Inception 3’ model as a way of determining clusters of images that we then studied for clues and hints: why did the machine cluster them this way? What are the common features?...

• Augmented space planning:
  Using procedural generation to automate desk layouts
  We explain @wework's desk layout algorithm in a new paper ... We developed a suite of procedural algorithms for space planning in commercial offices. These algorithms were benchmarked against 13,000 actual offices designed by human architects. The algorithm performed as well as an architect on 77% of offices, and achieved a higher capacity in an additional 6%, all while following a set of space standards...

• Speech Synthesis as a Service
  MLaaS Part 2: Speaker on the wall, who’s got the best voice of them all?...

• Real-World Reasons To Use GraphQL
  There are a lot of great blog posts that give an overview of GraphQL, or detail why GraphQL is better than REST, but we’re going to talk about why GraphQL worked well for us building a real-world, production app...

eCommerce Data Science & Machine Learning Analyst - PepsiCo - NYC
Have a strong opinion about Tensorflow lacking an autoregressive dynamic network? So do we!
PepsiCo’s eCommerce Data Science and Analytics group is a team of data scientists, technology specialists, and business innovators who operate within eCommerce to build industry-leading systems and solutions. By focusing on machine learning and automation, the Data Science & Analytics group is pushing the bounds of possibility for PepsiCo and its strategic partners...

• Create TensorFlow Name Scopes For TensorBoard
  Learn how to use TensorFlow Name Scopes (tf.name_scope) to group graph nodes in the TensorBoard web service so that your graph visualization is legible, via a screencast video and full tutorial transcript...

• How to handle multiple intents per input using Rasa NLU TensorFlow pipeline
  In this post, we are going to take a comprehensive look at how the TensorFlow-based pipeline can help us solve the problem of multiple intents. The result of this tutorial will be a very simple chatbot, that can recommend meetups to attend in Berlin...

• A Visual Introduction to Machine Learning: Part 2
  Model Tuning and the Bias-Variance Tradeoff...

  Test-Driven Machine Learning
The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression...

For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
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