Deep Learning / fast

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fastai_notes 🌿
30 (+0) ⭐

My classnotes, experiments, reproducible notebooks from fast.ai Deep Learning Class (v2)

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easyDL 🌿
20 (+0) ⭐

Easy and fast deep learning

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149 (+0) ⭐

Modification of fast.ai deep learning course notebooks for usage with Keras 2 and Python 3.

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24 (+0) ⭐

Deep Learning Quick Reference, published by Packt

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caffe 🌿
30077 (+6) ⭐

Caffe: a fast open framework for deep learning.

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fastai 🌿
17568 (+8) ⭐

The fastai deep learning library, plus lessons and tutorials

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FastPhotoStyle 🌿
10176 (+1) ⭐

Style transfer, deep learning, feature transform

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572 (+0) ⭐

An evolving guide to learning Deep Learning effectively.

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77 (+0) ⭐

Deliberate Practice for Learning Deep Learning

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15 (+0) ⭐

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

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