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[P] Keras-surgeon: pruning Keras models in python made easy (pip-installable) • r/MachineLearning

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I've just released keras-surgeon, a network pruning library for Keras implemented in python. A few months ago I wanted to experiment with pruning neural network channels and discovered that there wasn't really a straightforward way to do that. Source code documentation (see readme): https://github.com/BenWhetton/keras-surgeon Features of this implementation: * It's very easy to use * It should work for any network architecture * The majority of Keras layers are fully supported * It supports other network surgery: deleting, inserting and replacing layers. I hope this will help more people experiment with network pruning!


Machine Learning using Advanced Algorithms and Visualization

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Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. Then, we'll walk you through the next example on letter recognition, where you will train a program to recognize letters using a support Vector machine, examine the results, and plot a confusion matrix. Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there.


Machine Learning vs. Statistics: The Texas Death Match of Data Science

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Both Statistics and Machine Learning create models from data, but for different purposes. In conclusion, the Statistician is concerned primarily with model validity, accurate estimation of model parameters, and inference from the model. In Machine Learning, the predominant task is predictive modeling: the creation of models for the purpose of predicting labels of new examples. In predictive analytics, the ML algorithm is given a set of historical labeled examples.


How To Write Better SQL Queries: The Definitive Guide – Part 1

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Structured Query Language (SQL) is an indispensable skill in the data science industry and generally speaking, learning this skill is fairly easy. There are several reasons: one of the first reasons would be that companies mostly store data in Relational Database Management Systems (RDBMS) or in Relational Data Stream Management Systems (RDSMS) and you need SQL to access that data. Next, the chosen query plan is executed, evaluated by the system's execution engine and the results of your query are returned. You can add the LIMIT or TOP clauses to your queries to set a maximum number of rows for the result set.


Introducing Inbox Samples: saving your data for future training samples MonkeyLearn Blog

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Today we're launching Inbox Samples, an exciting new feature that will make it much easier to improve the machine learning models built on our platform. Later on, you can use the texts in your Inbox as new training samples and improve your models over time. Training samples saved in the inbox of a classifier. By default, these new samples don't have a category assigned and are not used as training samples by your model.


At its core, AI is versatile and creative – AI4ALL – Medium

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On the front, the SAILORS motto was written: "AI will change the world. I think that even though each girl in my SAILORS year arrived at the program with a different level of CS and AI knowledge, we all left with an appreciation of the versatility and creativity at the core of AI. I wanted to bring this understanding of AI as fundamentally creative and versatile to girls of all CS and AI levels in my community. The exciting confluence of AI and art, neural art is the generation or modification of artwork using machine learning algorithms.


Backprop is not just the chain rule

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Let's view the intermediate variables in our optimization problem as simple equality constraints in an equivalent constrained optimization problem. It turns out that the de facto method for handling constraints, the method Lagrange multipliers, recovers exactly the adjoints (intermediate derivatives) in the backprop algorithm! The standard way to solve a constrained optimization is to use the method Lagrange multipliers, which converts a constrained optimization problem into an unconstrained problem with a few more variables \(\boldsymbol{\lambda}\) (one per \(x_i\) constraint), called Lagrange multipliers. I described how we could use something we did learn from calculus 101, the method of Lagrange multipliers, to support optimization with intermediate variables.


[R] [1708.06733] BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain • r/MachineLearning

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The upshot is that it's pretty easy to get a network to learn to treat the presence of a "backdoor trigger" in the input specially without affecting the performance of the network on inputs where the trigger is not present. We also looked at transfer learning: if you download a backdoored model from someplace like the Caffe Model Zoo and fine-tune it for a new task by retraining the fully connected layers, it turns out that the backdoor can survive the retraining and lower the accuracy of the network when the trigger is present! It appears that retraining the entire network does make the backdoor disappear, but we have some thoughts on how to get around that that didn't make it into the paper. We argue that this means you need to treat models you get off the internet more like software and be careful about making sure you know where they came from and how they were trained.


Learn Data Science in 8 (Easy) Steps

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The most significant start of this trend or tradition was in 2010, when Drew Conway presented a Venn diagram to define the concept "data science". In the center of the picture is data science and it is the result of the combination of hacking skills, mathematics and statistics knowledge and substantive expertise. Data science is now defined through its relation to other disciplines, such as Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Big Data (BD) and Data Mining (DM). These two visuals might seem completely different, but they do share a lot of similarities: the disciplines that are visualized in Piatetsky-Shapiro's picture all require hacking skills, mathematics and statistics knowledge and substantive expertise or domain knowledge.


Machine Learning with Python - Udemy

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If you have a desire to learn machine learning concepts and have some previous programming or Python experience, this course is perfect for you. Writing processing from scratch allows students to gain a more in-depth insight into data processing, and as each machine learning app is created, explanations and comments are provided to help students understand why things are being done in certain ways. Each code walk through also shows the building process in real time. The course begins with an introduction to machine learning concepts, after which you'll build your first machine learning application.