Deep Learning Cheat Sheet (using Python Libraries)

#DeepLearning Cheat Sheet (using Python Libraries) #abdsc

  • This cheat sheet was produced by DataCamp, and it is based on the Keras library.
  • Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.
  • For other cheat sheets covering all data science topics, click here.

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that pr…
Continue reading “Deep Learning Cheat Sheet (using Python Libraries)”

Deep Learning Cheat Sheet (using Python Libraries)

#DeepLearning Cheat Sheet (using #Python Libraries) | @DataScienceCtrl  #Keras #TensorFlow

  • This cheat sheet was produced by DataCamp, and it is based on the Keras library.
  • Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models.
  • For other cheat sheets covering all data science topics, click here.

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that pr…
Continue reading “Deep Learning Cheat Sheet (using Python Libraries)”

6 areas of AI and Machine Learning to watch closely

#ICYMI 6 areas of #AI and #MachineLearning to watch closely

  • Those who are able to train faster and deploy AI models that are computationally and energy efficient are at a significant advantage.
  • Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”.
  • If we want AI systems to solve tasks where training data is particularly challenging, costly, sensitive, or time-consuming to procure, it’s important to develop models that can learn optimal solutions from less examples (i.e. one or zero-shot learning).
  • Deep learning models are notable for requiring enormous amounts of training data to reach state-of-the-art performance.
  • Without large scale training data, deep learning models won’t converge on their optimal settings and won’t perform well on complex tasks such as speech recognition or machine translation.


Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

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Can a deep net see a cat? – Piekniewski’s blog

Can #deeplearning recognise a cat?  The answer is not obvious. #AI #vision

  • In summary, the thesis of this post is that visual perception is NOT a solved problem and requires more work, likely a fundamental shift.
  • An adversarial example constructed from the category “Egyptian cat” classified with 98% confidence.
  • BTW: since I uploaded these cat images to ClarifAI, they may become part of their training set future results from them might be different than those presented .
  • The “best cat” is quite confidently classified as a dalmatian (0.74) and only with 0.04 confidence as the “Egyptian cat”.
  • It does classify my best cat as a cat (success!

In this post I will explore the capabilities of contemporary deep learning models on the vitally important task of detecting a cat. Not an ordinary cat though, but a sketch of an abstract cat. This task matters because success tells us something about whether a visual system has learned generalization and abstraction  — at least on par with a 2-year old. This post is inspired by my ex co-worker Peter O’Connor who tried similar experiments on LeNet several years ago. In addition, this post is a continuation of this blog’s highly popular “Just how close are we to solving vision?” which to-date has amassed nearly 15,000 hits. Let’s begin by introducing my menagerie:
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TensorFlow in a Nutshell  —  Part One: Basics – Medium

TensorFlow in a Nutshell  — Part One: Basics – Medium   #MachineLearning #DeepLearning

  • TensorFlow is a framework created by Google for creating Deep Learning models.
  • tf.zeros() – creates a matrix full of zeros
  • An Operation also referred to as op can return zero or more tensors which can be used later on in the graph.
  • The session run actually causes the execution of three operations in the graph, creating the two constants then the matrix multiplication.
  • any variables or operations used outside of the with new_graph.as_default() will be added to the default graph that is created when the library is loaded.

The fast and easy guide to the most popular Deep Learning framework in the world.

TensorFlow is a framework created by Google for creating Deep Learning m…
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Multi-Class Classification Tutorial with the Keras Deep Learning Library

Multi-Class #Classification Tutorial with the #Keras #DeepLearning Library #MachineLearning

  • The network uses the efficient ADAM gradient descent optimization algorithm with a logarithmic loss function, which is called categorical_crossentropy in Keras.
  • It ensures that the stochastic process of training a neural network model can be reproduced.
  • In the post you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems.
  • The Keras library provides wrapper classes to allow you to use neural network models developed with Keras in scikit-learn.
  • How to evaluate a Keras neural network model using scikit-learn with k-fold cross validation

In this post you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems.
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