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Train Imagenet in 18 minutes

Jeremy Howard et al, at fast.ai has done what one might consider a huge breakthrough in regards to training deep learning models quickly.

They managed to train Imagenet in 18 minutes using publicly available resources that only cost them $40 to run!

this was their method:

  • fast.ai’s progressive resizing for classification, and rectangular image validation
  • NVIDIA’s NCCL with PyTorch’s all-reduce
  • Tencent’s weight decay tuning; a variant of Google Brain’s dynamic batch sizes, gradual learning rate warm-up (Goyal et al 2018, and Leslie Smith 2018).
  • ResNet-50 architecture
  • SGD with momentum.

http://www.fast.ai/2018/08/10/fastai-diu-imagenet/

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Deep Drive Dataset available

The large dataset for teaching your algorithms to drive can be downloaded from http://bdd-data.berkeley.edu/.

It contains over 100,000 HD video sequences, that make up over a thousand hours of footage. The data contains over 100 000 annotated  images for object detection for bus, traffic light, traffic sign, person, bike, truck, motor, car, train, and rider. Alos segmentation, drivable area, lane markings etc.

I love how data is released to the public for the greater good.

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Harward Data Science Course 2015

If you are interested in learning more about Data Science, you can check out the course page for the CS109 Data Science Course at Harvard University.
Topics covered are among others:

  • Pandas
  • Python
  •  Web Scraping
  • Regular Expressions
  • Data Reshaping
  • Data Cleanup
  • Probability
  • Distributions
  • Frequentist Statistics
  • Bias and Regression
  • SVM, Decision Trees, Random Forests
  • Ensemble Methods
  • MapReduce
  • Spark
  • Bayes Theorem and Bayesian Methods
  • Interactive Visualization
  • Deep Networks
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The final Deep Learning Specialization course is now out

After a long wait, the final and much-anticipated course in the Coursera Deep Learning Specialization series taught by Andrew Ng, called Sequence Models, has now been released.

The first week will be about Recurrent Neural Networks, the second week will address Natural Language Processing & Word Embeddings and the final week will be about Sequence models & Attention mechanism.

 

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Fast.ai 2018 has been released, and it’s truly awesome

Last year the 2017 course of fast.ai was amazing, which taught state of the art deep learning to coders. There are so many goodies in the blog post about the Fast.ai 2018 launch which is available now. This year they held the course using Pytorch instead of Keras and wrote their own library for speeding up development and were the first to add several implementations from papers to the library such as Learning Rate Finder (Smith 2015) and Stochastic Gradient Descent with Restarts (SGDR). With one line of code, you can also get the images that the classifier gets wrong.

17 of the 20 top participants in a kaggle competitors were students in the preview course.

I recommend reading the blog post and taking the course.