Displaying 1 to 7 from 7 results

tflearn - Deep learning library featuring a higher-level API for TensorFlow.

  •    Python

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques.

tensorlayer-tricks - How to use TensorLayer

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While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

Deep-Learning-101 - The tools and syntax you need to code neural networks from day one.

  •    Jupyter

When I started learning deep learning I spent two weeks researching. I selected tools, compared cloud services, and researched online courses. In retrospect, I wish I could have built neural networks from day one. That’s what this article is set out to do. You don’t need any prerequisites, yet a basic understanding of Python, the command line, and Jupyter notebook will help. This is the code experiments from the article.




How-to-learn-Deep-Learning - A top-down, practical guide to learn AI, Deep learning and Machine Learning

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A practical, top-down approach, starting with high-level frameworks with a focus on Deep Learning. Note: You don't need a math background (I only know high school math), but a lot of determination. I found learning deep learning as hard as learning to program in C (my first programming language).

tensorflow-wrapper-compare - Comparison of TensorFlow Wrappers

  •    Python

Run Keras, TensorLayer and Tflearn with same model and data on a same GPU machine. The parameter initialization may have slightly different, but would not effect the speed.