# Tag: PyTorch (28)

**Beating the Bookies with Machine Learning**- Mar 8, 2019.

We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.**5 things that happened in Data Science in 2018**- Jan 8, 2019.

We review 5 things that happened in Data Science in 2018 and offer 20% discount on Reinforce AI Conference, Mar 20-22 in Budapest.**Top Python Libraries in 2018 in Data Science, Deep Learning, Machine Learning**- Dec 19, 2018.

Here are the top 15 Python libraries across Data Science, Data Visualization. Deep Learning, and Machine Learning.**State of Deep Learning and Major Advances: H2 2018 Review**- Dec 13, 2018.

In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.**Introduction to PyTorch for Deep Learning**- Nov 7, 2018.

In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.**Top 13 Python Deep Learning Libraries**- Nov 2, 2018.

Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.**Deep Learning Framework Power Scores 2018**- Sep 24, 2018.

Who’s on top in usage, interest, and popularity?**[ebook] Apache Spark™ Under the Hood**- Jun 27, 2018.

Learn how to install and run Spark yourself; A summary of Spark core architecture and concepts; Spark powerful language APIs and how you can use them.**Top 20 Python Libraries for Data Science in 2018**- Jun 27, 2018.

Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.**Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health**- Jun 14, 2018.

After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.**KDnuggets™ News 18:n21, May 23: Python eats away at R; Top 2018 Analytics, Data Science, Machine Learning tools; 9 Must-have skills for a Data Scientist**- May 23, 2018.

Also How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch; Frameworks for Approaching the Machine Learning Process.**Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch**- May 21, 2018.

Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application**How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1**- May 17, 2018.

The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we'll do in this tutorial.**KDnuggets™ News 18:n20, May 16: PyTorch Tensor Basics; Data Science in Finance; Executive Guide to Data Science**- May 16, 2018.

PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; The Executive Guide to Data Science and Machine Learning; Data Augmentation: How to use Deep Learning when you have Limited Data**Simple Derivatives with PyTorch**- May 14, 2018.

PyTorch includes an automatic differentiation package, autograd, which does the heavy lifting for finding derivatives. This post explores simple derivatives using autograd, outside of neural networks.**PyTorch Tensor Basics**- May 11, 2018.

This is an introduction to PyTorch's Tensor class, which is reasonably analogous to Numpy's ndarray, and which forms the basis for building neural networks in PyTorch.**Ultra-compact workstation for top deep learning frameworks**- Apr 27, 2018.

For workstation development platforms purpose-built for Tensorflow, PyTorch, Caffe2, MXNet, and other DL frameworks, the solution is BOXX. We're bringing deep learning to your deskside with the all-new APEXX W3!**KDnuggets™ News 18:n16, Apr 18: Key Algorithms and Statistical Models; Don’t learn Machine Learning in 24 hours; Data Scientist among the best US Jobs in 2018**- Apr 18, 2018.

Also: Top 10 Technology Trends of 2018; 12 Useful Things to Know About Machine Learning; Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks; Understanding What is Behind Sentiment Analysis - Part 1; Getting Started with PyTorch**Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works**- Apr 11, 2018.

PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.**Comparing Deep Learning Frameworks: A Rosetta Stone Approach**- Mar 26, 2018.

A Rosetta Stone of deep-learning frameworks has been created to allow data-scientists to easily leverage their expertise from one framework to another.**Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch**- Feb 20, 2018.

Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.**A Simple Starter Guide to Build a Neural Network**- Feb 5, 2018.

This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.**Top 10 Videos on Deep Learning in Python**- Nov 17, 2017.

Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!**Ranking Popular Deep Learning Libraries for Data Science**- Oct 23, 2017.

We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.**Top KDnuggets tweets, Oct 04-10: Using #MachineLearning to Predict, Explain Attrition; Tidyverse, an opinionated #DataScience Toolbox in R**- Oct 11, 2017.

Also #MachineLearning: Understanding Decision Tree Learning; #PyTorch tutorial distilled - Moving from #TensorFlow to PyTorch.**PyTorch or TensorFlow?**- Aug 29, 2017.

PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.**Top KDnuggets tweets, Aug 2-8: PyTorch: concise overview of the framework and its tensor implementation**- Aug 9, 2017.

Also: What is the most important step in a #MachineLearning project? #MachineLearning Algorithms: a concise technical overview; McKinsey state of #MachineLearning and #AI.**Design by Evolution: How to evolve your neural network with AutoML**- Jul 20, 2017.

The gist ( tl;dr): Time to evolve! I’m gonna give a basic example (in PyTorch) of using evolutionary algorithms to tune the hyper-parameters of a DNN.