Are you looking to learn python for data science but have a time crunch? Are you making your career shift into data science and want to learn python? In this blog, we will talk about learning python for data science in just 30 days. Also, we will look at weekly schedules and topics to cover in python.

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Efficient Convolutional Neural Network Training with Direct Feedback Alignment

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques. Deep learning in agriculture: A survey

(TL;DR: author continues to use his undiagnosed OCD for good. Breath-first search introduced on simple graph.)

1. Visualizing Data

Alright, seems like this is developing into a blog where I am increasingly investigating my own music listening habits.Recently, I’ve come across the analyzelastfm package by Sebastian Wolf. I used it to download my complete listening history from Last.FM for the last ten years. That’s a complete dataset from 2009 to 2018 with exactly 65,356 “scrobbles� (which is the word Last.FM uses to describe one instance of a playback of a song).

A “Robot Scientist” – Machine learning automates a kind of scientific research

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Deep neural networks have become state-of-the-art technology for a wide range of practical machine learning tasks such as image classification, handwritten digit recognition, speech recognition, or game intelligence. This paper develops the fundamental limits of learning in deep neural networks by characterizing what is possible if no constraints on the learning algorithm and the amount of training data are imposed. Concretely, we consider information-theoretically optimal approximation through deep neural networks with the guiding theme being a relation between the complexity of the function (class) to be approximated and the complexity of the approximating network in terms of connectivity and memory requirements for storing the network topology and the associated quantized weights. The theory we develop educes remarkable universality properties of deep networks. Specifically, deep networks are optimal approximants for vastly different function classes such as affine systems and Gabor systems. This universality is afforded by a concurrent invariance property of deep networks to time-shifts, scalings, and frequency-shifts. In addition, deep networks provide exponential approximation accuracy i.e., the approximation error decays exponentially in the number of non-zero weights in the network of vastly different functions such as the squaring operation, multiplication, polynomials, sinusoidal functions, general smooth functions, and even one-dimensional oscillatory textures and fractal functions such as the Weierstrass function, both of which do not have any known methods achieving exponential approximation accuracy. In summary, deep neural networks provide information-theoretically optimal approximation of a very wide range of functions and function classes used in mathematical signal processing. Deep Neural Network Approximation Theory