Google gives everyone machine learning superpowers with TensorFlow 1.0

Google gives everyone machine learning superpowers with TensorFlow 1․0

  • That began to change with the release of a number of open-source machine learning frameworks like Theano, Spark ML, Microsoft’s CNTK, and Google’s TensorFlow.
  • Among them, TensorFlow stands out for its powerful, yet accessible, functionality, coupled with the stunning growth of its user base.
  • With this week’s release of TensorFlow 1.0, Google has pushed the frontiers of machine learning further in a number of directions.
  • In an effort to make TensorFlow a more-general machine learning framework, Google has added both built-in Estimator functionality, and support for a number of more traditional machine learning algorithms including K-means, SVM (Support Vector Machines), and Random Forest.
  • While there are certainly other frameworks like SparkML that support those tools, having a solution that can combine them with neural networks makes TensorFlow a great option for hybrid problems.

Google gives everyone machine learning superpowers with TensorFlow 1.0
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The hard thing about deep learning

The hard thing about #DeepLearning is the #optimization problem

  • In a nutshell: the deeper the network becomes, the harder the optimization problem becomes.
  • 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.
  • In the post, I explore the “hardness” in optimizing neural networks and see what the theory has to say.
  • The simplest neural network is the single-node perceptron , whose optimization problem is convex .
  • The reasons for the success of deep learning go far beyond overcoming the optimization problem.


It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.

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Design Patterns for Deep Learning Architectures

Design Patterns for #DeepLearning Architectures:  #BigData #DataScience #MachineLearning

  • We purposely use “pattern language” to reflect that the field of Deep Learning is a nascent, but rapidly evolving, field that is not as mature as other topics in computer science.
  • Each pattern describes a problem and offers alternative solutions.
  • You can find more details on this book at: A Pattern Language for Deep Learning .
  • Pattern Languages are languages derived from entities called patterns that when combined form solutions to complex problems.
  • Or you can check for updates at Design Patterns for Deep Learning

Deep Learning can be described as a new machine learning toolkit that has a high likelihood to lead to more advanced forms of artificial intelligence. The evidence for this is in the sheer number of breakthroughs that had occurred since the beginning of this decade. There is a new found optimism in the air and we are now again in a new AI spring. Unfortunately, the current state of deep learning appears to many ways to be akin to alchemy. Everybody seems to have their own black-magic methods of designing architectures. The field thus needs to move forward and strive towards chemistry, or perhaps even a periodic table for deep learning. Although deep learning is still in its early infancy of development, this book strives towards some kind of unification of the ideas in deep learning. It leverages a method of description called pattern languages.
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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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Embrace Randomness in Machine Learning

Embrace Randomness in #MachineLearning  #AI #DeepLearning #DataScience

  • Pull Back the Curtain on Machine Learning Algorithms
  • Trained with different data, machine learning algorithms will construct different models.
  • Machine learning algorithms are stochastic in practice.
  • Machine learning algorithms make use of randomness.
  • Never report the performance of your machine learning algorithm with a single number.

Once you get it, you will see things differently. In a whole new light. Things like choosing between one algorithm and another, hyperparameter tuning and reporting results.
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Python Programming Tutorials

Practical #MachineLearning Intro Tutorial with #Python by @sentdex

  • We’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.
  • Handling Non-Numerical Data for Machine Learning
  • Deep Learning with TensorFlow – Creating the Neural Network Model
  • Beyond this, there are ample resources out there to help you on your journey with machine learning, like this tutorial.
  • It is really only very recently that we’ve been able to put much of machine learning to any decent test.

Read the full article, click here.


@kdnuggets: “Practical #MachineLearning Intro Tutorial with #Python by @sentdex”


Python Programming tutorials from beginner to advanced on a massive variety of topics. All video and text tutorials are free.


Python Programming Tutorials