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Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

@machinelearnbot

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.


Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

#artificialintelligence

This post is made up of a collection of 10 Github repositories consisting in part, or in whole, of IPython (Jupyter) Notebooks, focused on transferring data science and machine learning concepts. This warmup notebook is from postdoctoral researcher Randal Olson, who uses the common Python ecosystem data analysis/machine learning/data science stack to work with the Iris dataset. Aaron Masino has shared a series of very detailed, very technical machine learning IPython Notebook learning resources. From UC Boulder's Research Computing group, this older collection of notebooks (it's from way back in Fall 2013) covers a wide range of material, with an apparent focus on Linux command line-powered data management.


Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

#artificialintelligence

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.


Accelerate your Machine Learning applications with zero code changes

#artificialintelligence

InAccel, a pioneer on FPGA-based acceleration, has released an accelerated machine learning platform that allows instant acceleration of ML applications and neural network models. Data scientists and ML engineers can now speedup by more than 10x computationally intensive workloads and reduce the total cost of ownership with zero code changes. It fully supports widely used frameworks like Keras, Scikit-learn, Jupyter Notebooks and Spark. FPGAs are adaptable hardware platforms that can offer great performance, low-latency and reduced OpEx for applications like machine learning, video processing, quantitative finance, genomics, etc. However, the easy and efficient deployment from users with no prior knowledge on FPGA was challenging.


Accelerate Your Machine Learning Applications With Zero Code Changes

#artificialintelligence

InAccel, a pioneer on FPGA-based acceleration, has released an accelerated machine learning platform that allows instant acceleration of ML applications and neural network models. Data scientists and ML engineers can now speedup by more than 10x computationally intensive workloads and reduce the total cost of ownership with zero code changes. It fully supports widely used frameworks like Keras, Scikit-learn, Jupyter Notebooks and Spark. FPGAs are adaptable hardware platforms that can offer great performance, low-latency and reduced OpEx for applications like machine learning, video processing, quantitative finance, genomics, etc. However, the easy and efficient deployment from users with no prior knowledge on FPGA was challenging. InAccel provides an FPGA resource manager that allows the instant deployment, scaling and resource management of FPGAs making easier than ever the utilization of FPGAs for applications like machine learning and data processing applications.