Practical Deep Learning For Coders—18 hours of lessons for free

Welcome to a 7 week course, Practical Deep Learning For Coders, Part 1,

  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.
  • If you can code, you can do deep learning
  • It is very hands-on and adopts a top-down approach, which means everyone irrespective of varying knowledge can get started with implementing Deep learning models immediately.
  • If you are looking to venture into the Deep learning field, look no further and take this course.
  • I now have the tools to apply deep learning models to real world problems.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

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Deep Learning in Clojure with Cortex

Deep Learning in Clojure With Cortex

  • The training data consists of 25,000 images of cats and dogs.
  • It reads in the external nippy file that contains the trained network description, takes a random image from the testing directory, and classifies it.
  • We want all the dog images to be under a “dog” directory and the cat images under the “cat” directory so that the all the indexed images under them have the correct “label”.
  • How many times it thought a cat was really a cat and how many times it got it wrong.
  • We need all the images to be the same size as well as in a directory structure that is split up into the training and test images.

There is an awesome new Clojure-first machine learning library called Cortex that was open sourced recently. I’ve been exploring it lately and …
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Practical Deep Learning For Coders—18 hours of lessons for free

Practical Deep Learning For Coders

  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.
  • If you can code, you can do deep learning
  • It is very hands-on and adopts a top-down approach, which means everyone irrespective of varying knowledge can get started with implementing Deep learning models immediately.
  • I now have the tools to apply deep learning models to real world problems.
  • If you are looking to venture into the Deep learning field, look no further and take this course.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

Continue reading “Practical Deep Learning For Coders—18 hours of lessons for free”

The hard thing about deep learning

The hard thing about deep learning  #ai

  • In a nutshell: the deeper the network becomes, the harder the optimization problem becomes.
  • The simplest neural network is the single-node perceptron , whose optimization problem is convex .
  • To provably solve optimization problems for general neural networks with two or more layers, the algorithms that would be necessary hit some of the biggest open problems in computer science.
  • There is a rich variety of optimization algorithms to handle convex optimization problems, and every few years a better polynomial-time algorithm for convex optimization is discovered.
  • Judd also shows that the problem remains NP-hard even if it only requires a network to produce the correct output for just two-thirds of the training examples, which implies that even approximately training a neural network is intrinsically difficult in the worst case.

Deeper neural nets often yield harder optimization problems.
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Using Machine Learning to Detect Malicious URLs

#ICYMI Using #MachineLearning to Detect Malicious URLs  #security

  • Now that we have the data in our list, we have to vectorize our URLs.
  • KDnuggets Home > News > 2016 > Oct > Opinions, Interviews > Using Machine Learning to Detect Malicious URLs ( 16:n39 )
  • I wrote my own tokenizer function for this since URLs are not like some other document text.
  • Unfortunately or fortunately, there has been little work done on security with machine learning algorithms.
  • The data and code is available at Github .


This is a write-up of an experiment employing a machine learning model to identify malicious URLs. The author provides a link to the code for you to try yourself.

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Mapping global fishing activity with machine learning

  • Global Fishing Watch combines cloud computing technology with satellite data to provide the world’s first global view of commercial fishing activities.
  • Today, Global Fishing Watch is an early preview of what is possible.
  • Indonesia’s Minister of Fisheries and Marine Affairs, Susi Pudjiastuti, has committed to making the government’s Vessel Monitoring System (VMS) public in Global Fishing Watch in 2017.
  • Global Fishing Watch was not possible five years ago.
  • Bali Seafood, the largest exporter of snapper from Indonesia, has teamed up with Pelagic Data Systems, manufacturers of cellular and solar powered tracking devices to bring the same transparency for small scale and artisanal fishing vessels, into Global Fishing Watch as part of a pilot program.

Global Fishing Watch is a free, simple, online platform that gives anyone, anywhere, a way to to visualize, track, and share information about fishing activity worldwide.
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A Development Methodology for Deep Learning – Medium

A Development Methodology for #DeepLearning.  #BigData #MachineLearning #DataScience #AI

  • The machine learning convention has been to create a training set, a validation set and a test set.
  • Although Deep Learning is built from software it is a different kind of software and a different kind of methodology is needed.
  • The observations that differs from conventional machine learning is that Deep Learning has more flexibility in that a developer has the additional options of employing either a bigger model or using more data.
  • The methodology addresses the necessary interplay of the need for more training data and the exploration of alternative Deep Learning patterns that drive the discovery of an effective architecture.
  • Deep Learning differs most from traditional software development in that a substantial portion of the process involves the machine learning how to achieve objectives.

The practice of software development has created development methodologies such agile development and lean methodology to tackle the…
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