- With plenty of libraries out there for deep learning, one thing that confuses a beginner in this field the most is which library to choose.Deep Learning libraries/frameworks as per popularity(Source : Google)In this blog post, I am only going to focus on Tensorflow and Keras.
- And if Keras is more user-friendly, why should I ever use TF for building deep learning models?
- You can tweak TF much more as compared to Keras.FunctionalityAlthough Keras provides all the general purpose functionalities for building Deep learning models, it doesn’t provide as much as TF.
- Absolutely, check the example below:Playing with gradients in TensorFlow (Credits : CS 20SI: TensorFlow for Deep Learning Research)Conclusion (TL;DR)if you are not doing some research purpose work or developing some special kind of neural network, then go for Keras (trust me, I am a Keras fan!!)
- But as we all know that Keras is going to be integrated in TF, it is wiser to build your network using tf.contrib.Keras and insert anything you want in the network using pure TensorFlow.
Deep learning is everywhere. 2016 was the year where we saw some huge advancements in the field of Deep Learning and 2017 is all set to see many more advanced use cases. With plenty of libraries out…
Continue reading “TensorFlow or Keras? Which one should I learn? – Imploding Gradients – Medium”
- Keras and TensorFlow are the state of the art in deep learning tools and with the keras package you can now access both with a fluent R interface.
- To prepare this data for training we one-hot encode the vectors into binary class matrices using the Keras function:
The core data structure of Keras is a model, a way to organize layers.
- We begin by creating a sequential model and then adding layers using the pipe ( ) operator:
The argument to the first layer specifies the shape of the input data (a length 784 numeric vector representing a grayscale image).
- Use the function to print the details of the model:
Next, compile the model with appropriate loss function, optimizer, and metrics:
Use the function to train the model for 30 epochs using batches of 128 images:
The object returned by includes loss and accuracy metrics which we can plot:
Evaluate the model’s performance on the test data:
Keras provides a vocabulary for building deep learning models that is simple, elegant, and intuitive.
- After you’ve become familiar with the basics, these articles are a good next step:
Keras provides a productive, highly flexible framework for developing deep learning models.
We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a focus on enabling fast experimentation. Keras has the following key features:
Continue reading “Keras for R”
- For that reason, I suggest starting with image recognition tasks in Keras, a popular neural network library in Python.
- Deep learning is a name for machine learning techniques using many-layered artificial neural networks.
- See a plot of AUC score for logistic regression, random forest and deep learning on Higgs dataset (data points are in millions):
In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees.
- Deep learning (that is – neural networks with many layers) uses mostly very simple mathematical operations – just many of them.
- Its mathematics is simple to the point that a convolutional neural network for digit recognition can be implemented in a spreadsheet (with no macros), see: Deep Spreadsheets with ExcelNet.
I teach deep learning both for a living (as the main deepsense.io instructor, in a Kaggle-winning team1) and as a part of my volunteering with the Polish Chi…
Continue reading “Learning Deep Learning with Keras”
- In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
- The core data structure of Keras is a model, a way to organize layers.
- A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code).
- You can now iterate on your training data in batches:
Alternatively, you can feed batches to your model manually:
Evaluate your performance in one line:
Or generate predictions on new data:
Building a question answering system, an image classification model, a Neural Turing Machine, or any other model is just as fast.
- For a more in-depth tutorial about Keras, you can check out:
In the examples folder of the repository, you will find more advanced models: question-answering with memory networks, text generation with stacked LSTMs, etc.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Continue reading “Keras: Deep Learning library for Theano and TensorFlow”
- Deep learning course: Getting Started with the Caffe Framework
- Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started.
- Chainer is a deep learning framework that’s designed on the principle of define-by-run.
- Caffe is a deep learning framework made with expression, speed, and modularity in mind.
Read the full article, click here.
@GPUComputing: “New #cuDNN 5.1, 2.7x faster training of #deeplearning networks with 3×3 convolutions.”
The NVIDIA Deep Learning SDK accelerates widely-used deep learning frameworks such as Caffe, CNTK, TensorFlow, Theano and Torch as well as many other deep learning applications. Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started.
Deep Learning Frameworks
- The core data structure of Keras is a model , a way to organize layers.
- By default, Keras will use Theano as its tensor manipulation library.
- The main type of model is the Sequential model, a linear stack of layers.
- To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
- Getting started: 30 seconds to Keras
Read the full article, click here.
@gcosma1: “Keras:Deep Learning library for Theano & TensorFlow Tutorial #DataScience #MachineLearning”
Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Keras: Deep Learning library for Theano and TensorFlow
- And you, as an early adopter of deep learning, you have a responsibility to make sure that the opportunities that AI will create are open to everyone.
- The purpose of Keras is to make deep learning accessible to anyone with an idea and with some basic computer science literacy.
- There is a lot of hype around deep learning right now, and people sometimes have unrealistic short-term expectations.
- AI will take time to get deployed to its true potential, but when it does, it will have a long-term social and economic impact that most people seem to underestimate.
- Making deep learning more accessible should be one of our priorities.
Read the full article, click here.
@kdnuggets: “On the importance of democratizing #ArtificialIntelligence, from Keras #DeepLearning blog”
Keras is a Deep Learning library for Python, that is simple, modular, and extensible.
On the importance of democratizing Artificial Intelligence