# Tag: Theano (14)

**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?**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!**Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe**- Nov 8, 2017.

Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.**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.**The Search for the Fastest Keras Deep Learning Backend**- Sep 26, 2017.

This is an overview of the performance comparison for the popular Deep Learning frameworks supported by Keras – TensorFlow, CNTK, MXNet and Theano.**Getting Started with Deep Learning**- Mar 24, 2017.

This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.**An Overview of Python Deep Learning Frameworks**- Feb 27, 2017.

Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.**5 Machine Learning Projects You Can No Longer Overlook**- May 19, 2016.

We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.**7 Steps to Understanding Deep Learning**- Jan 11, 2016.

There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!**50 Deep Learning Software Tools and Platforms, Updated**- Dec 15, 2015.

We present the popular software & toolkit resources for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Explore the new list!**7 Steps to Mastering Machine Learning With Python**- Nov 19, 2015.

There are many Python machine learning resources freely available online. Where to begin? How to proceed? Go from zero to Python machine learning hero in 7 steps!**Introducing: Blocks and Fuel – Frameworks for Deep Learning in Python**- Oct 26, 2015.

Blocks and Fuel are machine learning frameworks for Python developed by the Montreal Institute of Learning Algorithms (MILA) at the University of Montreal. Blocks is built upon Theano (also by MILA) and allows for rapid prototyping of neural network models. Fuel serves as a data processing pipeline and data interface for Blocks.**Popular Deep Learning Tools – a review**- Jun 18, 2015.

Deep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch.