python


Keras: Deep Learning in Python - Udemy

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Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. After taking this course, you should feel comfortable building neural nets for time sequences, images classification, pure classification and/or regression.


Deep Learning Prerequisites: Logistic Regression in Python

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This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We show you how one might code their own logistic regression module in Python. If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you want more than just a superficial look at machine learning models, this course is for you.


21 Best Online Courses On Data Science

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Also, these data science tutorials give you idea about data science, python, data scientist, big data, analytics, machine learning, deep learning and Artificial Intelligence (AI) are the most booming topics now. Description: Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Learn data visualization through Tableau 10 and create opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks. Learn data visualization through Microsoft Power BI and create opportunities for you or key decision makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks.


In Raw Numpy: t-SNE

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To ensure the perplexity of each row of \(P\), \(Perp(P_i)\), is equal to our desired perplexity, we simply perform a binary search over each \(\sigma_i\) until \(Perp(P_i) \) our desired perplexity. It takes a matrix of negative euclidean distances and a target perplexity. Let's also define a p_joint function that takes our data matrix \(\textbf{X}\) and returns the matrix of joint probabilities \(P\), estimating the required \(\sigma_i\)'s and conditional probabilities matrix along the way: So we have our joint distributions \(p\) and \(q\). The only real difference is how we define the joint probability distribution matrix \(Q\), which has entries \(q_{ij}\).


Learning Path:TensorFlow: The Road to TensorFlow-2nd Edition

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It can be hard to get started with machine learning, particularly as new frameworks like TensorFlow start to gain traction across enterprise companies. The goal of this Learning Path is to help you understand deep learning and machine learning by getting to know Python first and then TensorFlow. After working for 3 years with kernel machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras, the deep learning library for Python.


Deep Learning: A Practitioner's Approach: 9781491914250: Computer Science Books @ Amazon.com

@machinelearnbot

I ordered this book back in May and was very pleased to get it; so far it is excellent and exactly at the level I need: I am a sometime practitioner of machine learning and AI using a range of open source and off-the-shelf tools. I initially thought my Kindle software was broken when searching for the first occurrence of "SGD" didn't show up; I remembered it was referred to as the "canonical" solution to solving a system of linear in an iterative fashion, but forgot what it stood for. Sure enough, right there on page 15 you find "The canonical example of iterative methods most commonly seen in machine learning today is Stochastic Gradient Descent (SDG), which we discuss later in this chapter.". Having source code on GitHub at least means I am less worried about such issues with code samples...


Geospatial Analysis with Python

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Data comes in all shapes and sizes and often government data is geospatial in nature. Often times data science programs & tutorials ignore how to work with this rich data to make room for more advanced topics. Our MinneMUDAC competition heavily utilized geospatial data but was processed to provide students a more familiar format. As always data & code will be provided.


Data Scientist versus Data Architect

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It's not only difficult to maintain, but by using hash tables, you're ineffectively re-writing code that achieves what a database platform already does, but much better, as the database platform's binary code is optimised for exactly this sort of operation, whereas Python isn't. By working in this way, you're also guilty of pulling all of the data from every data set you're using from a server (lots of IO) across a network (lots of bandwidth usage) to do something on a low spec laptop rather than a high spec server (lots of time penalties). You're effectively moving your own learning pain as a cost onto your employer because you're insisting on using a tool you're familiar with (Python), rather than choosing the most suitable tool for the job (SQL), which I would argue every data scientist should have a half decent understanding of in order to achieve precisely these goals. I'd certainly not employ someone who claimed to be an operational data scientist if they could not write basic SQL (3-4 way joins, filtering, aggregates).


Real-time object detection with deep learning and OpenCV - PyImageSearch

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Luckily, extending our previous tutorial on object detection with deep learning and OpenCV to real-time video streams is fairly straightforward -- we simply need to combine some efficient, boilerplate code for real-time video access and then add in our object detection. In the first part we'll learn how to extend last week's tutorial to apply real-time object detection using deep learning and OpenCV to work with video streams and video files. Then we capture a key press (Line 82) while checking if the'q' key (for "quit") is pressed, at which point we break out of the frame capture loop (Lines 85 and 86). In today's blog post we learned how to perform real-time object detection using deep learning OpenCV video streams.


Deep Learning: Convolutional Neural Networks in Python

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You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge! We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.