Popular Frameworks for Neural Networks and Deep Learning in Python

In this Python article, let's learn about Deep Learning: Popular Frameworks for Neural Networks and Deep Learning in Python

Table of contents:

• caffe - A fast open framework for deep learning..
• keras - A high-level neural networks library and capable of running on top of either TensorFlow or Theano.
• mxnet - A deep learning framework designed for both efficiency and flexibility.
• pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
• SerpentAI - Game agent framework. Use any video game as a deep learning sandbox.
• tensorflow - The most popular Deep Learning framework created by Google.
• Theano - A library for fast numerical computation.

What is Python in deep learning?

Python is a general-purpose high level programming language that is widely used in data science and for producing deep learning algorithms. This brief tutorial introduces Python and its libraries like Numpy, Scipy, Pandas, Matplotlib; frameworks like Theano, TensorFlow, Keras.

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Popular Frameworks for Neural Networks and Deep Learning in Python

1. Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Community

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}

View on GitHub

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2.  Keras

This repository hosts the development of the Keras library. Read the documentation at keras.io.

About Keras

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.

Keras is:

• Simple -- but not simplistic. Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter.
• Flexible -- Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you've already learned.
• Powerful -- Keras provides industry-strength performance and scalability: it is used by organizations and companies including NASA, YouTube, and Waymo.

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Keras & TensorFlow 2

TensorFlow 2 is an end-to-end, open-source machine learning platform. You can think of it as an infrastructure layer for differentiable programming. It combines four key abilities:

• Efficiently executing low-level tensor operations on CPU, GPU, or TPU.
• Computing the gradient of arbitrary differentiable expressions.
• Scaling computation to many devices, such as clusters of hundreds of GPUs.
• Exporting programs ("graphs") to external runtimes such as servers, browsers, mobile and embedded devices.

Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning. It provides essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.

Keras empowers engineers and researchers to take full advantage of the scalability and cross-platform capabilities of TensorFlow 2: you can run Keras on TPU or on large clusters of GPUs, and you can export your Keras models to run in the browser or on a mobile device.

View on GitHub

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3.  MXNet

Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scalable to many GPUs and machines.

Apache MXNet is more than a deep learning project. It is a community on a mission of democratizing AI. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

Features

• NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.
• Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
• Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO.
• Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
• Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
• Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia.
• Cloud-friendly and directly compatible with AWS and Azure.

View on GitHub

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4.  pytorch

PyTorch is a Python package that provides two high-level features:

• Tensor computation (like NumPy) with strong GPU acceleration
• Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Usually, PyTorch is used either as:

• A replacement for NumPy to use the power of GPUs.
• A deep learning research platform that provides maximum flexibility and speed.

Elaborating Further:

A GPU-Ready Tensor Library

If you use NumPy, then you have used Tensors (a.k.a. ndarray).

PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, reductions. And they are fast!

Dynamic Neural Networks: Tape-Based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world. One has to build a neural network and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as torch-autograd, autograd, Chainer, etc.

While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.

View on GitHub

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5.  Serpent.AI

Serpent.AI is a simple yet powerful, novel framework to assist developers in the creation of game agents. Turn ANY video game you own into a sandbox environment ripe for experimentation, all with familiar Python code. The framework's raison d'être is first and foremost to provide a valuable tool for Machine Learning & AI research. It also turns out to be ridiculously fun to use as a hobbyist (and dangerously addictive; a fair warning)!

The framework features a large assortment of supporting modules that provide solutions to commonly encountered scenarios when using video games as environments as well as CLI tools to accelerate development. It provides some useful conventions but is absolutely NOT opiniated about what you put in your agents: Want to use the latest, cutting-edge deep reinforcement learning algorithm? ALLOWED. Want to use computer vision techniques, image processing and trigonometry? ALLOWED. Want to randomly press the Left or Right buttons? sigh ALLOWED. To top it all off, Serpent.AI was designed to be entirely plugin-based (for both game support and game agents) so your experiments are actually portable and distributable to your peers and random strangers on the Internet.

Serpent.AI supports Linux, Windows & macOS.

The next version of the framework will officially stop supporting macOS. Apple's aversion to Nvidia in their products means no recent macOS machine can run CUDA, an essential piece of technology for Serpent.AI's real-time training. Other decisions like preventing 32-bit applications from running in Catalina and deprecating OpenGL do not help make a case to support the OS.

Experiment: Game agent learning to defeat Monstro (The Binding of Isaac: Afterbirth+)

Background

The project was born out of admiration for / frustration with OpenAI Universe. The idea is perfect, let's be honest, but some implementation details leave a lot to be desired. From these, the core tennets of the framework were established:

1. Thou shall run natively. Thou shalt not use Docker containers or VNC servers.
2. Thou shall allow a user to bring their own games. Thou shalt not wait for licensing deals and special game APIs.
3. Thou shall encourage diverse and creative approaches. Thou shalt not only enable AI flavors of the month.

Want to know more about how Serpent.AI came to be? Read The Story Behind Serpent.AI on the blog!

View on GitHub

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6.  tensorflow

An Open Source Machine Learning Framework for Everyone

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

Other devices (DirectX and MacOS-metal) are supported using Device plugins.

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

View on GitHub

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7.  Theano

Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

============================================================================================================
MILA will stop developing Theano: https://groups.google.com/d/msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ

The PyMC developers have forked Theano to a new project called Aesara that is being actively developed: https://github.com/aesara-devs/aesara
============================================================================================================

To install the package, see this page:
  http://deeplearning.net/software/theano/install.html

For the documentation, see the project website:
  http://deeplearning.net/software/theano/

Related Projects:
  https://github.com/Theano/Theano/wiki/Related-projects

It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package.

View on GitHub

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Deep Learning in Python FAQ

• What is meant by deep learning?

Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. Deep learning is an important element of data science, which includes statistics and predictive modeling.

• What is Python in deep learning?

Python is a general-purpose high level programming language that is widely used in data science and for producing deep learning algorithms. This brief tutorial introduces Python and its libraries like Numpy, Scipy, Pandas, Matplotlib; frameworks like Theano, TensorFlow, Keras.

• Is deep learning part of Python?

Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow 2. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library.

• What is deep learning in programming?

Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

• What is an example of deep learning?

Deep learning utilizes both structured and unstructured data for training. Practical examples of deep learning are Virtual assistants, vision for driverless cars, money laundering, face recognition and many more.

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Related videos:

Deep Learning With Python | Deep Learning Tutorial For Beginners | Learn Deep Learning

Below topics are explained in this Deep Learning tutorial:
0:00 Introduction
0:13 Introduction to Deep Learning tutorial
0:53 What is Deep Learning?
01:41 what are artificial neurons
03:01 What is a neural network?
07:46 Activation function
25:02 Introduction to TensorFlow
41:42 Gradient descent

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Related posts:

• Machine Learning with Neural Networks
• Deep Learning with Python
• Deep Learning Tutorial For Beginners
• How To Prepare Your Dataset For Machine Learning In Python
• Machine Learning, Data Science and Deep Learning with Python

#python #deep-learning 

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Deep learning on graphs: successes, challenges, and next steps

TL;DR This is the first in a [series of posts] where I will discuss the evolution and future trends in the field of deep learning on graphs.

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases [2], has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [3] if not two [4], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the ML community and even to the popular science press (with Quanta Magazine running a series of excellent articles on geometric deep learning for the study of manifolds, drug discovery, and protein science).

Graphs are powerful mathematical abstractions that can describe complex systems of relations and interactions in fields ranging from biology and high-energy physics to social science and economics. Since the amount of graph-structured data produced in some of these fields nowadays is enormous (prominent examples being social networks like Twitter and Facebook), it is very tempting to try to apply deep learning techniques that have been remarkably successful in other data-rich settings.

There are multiple flavours to graph learning problems that are largely application-dependent. One dichotomy is between node-wise and graph-wise problems, where in the former one tries to predict properties of individual nodes in the graph (e.g. identify malicious users in a social network), while in the latter one tries to make a prediction about the entire graph (e.g. predict solubility of a molecule). Furthermore, like in traditional ML problems, we can distinguish between supervised and unsupervised (or self-supervised) settings, as well as transductive and inductive problems.

Similarly to convolutional neural networks used in image analysis and computer vision, the key to efficient learning on graphs is designing local operations with shared weights that do message passing [5] between every node and its neighbours. A major difference compared to classical deep neural networks dealing with grid-structured data is that on graphs such operations are permutation-invariant, i.e. independent of the order of neighbour nodes, as there is usually no canonical way of ordering them.

Despite their promise and a series of success stories of graph representation learning (among which I can selfishly list the [acquisition by Twitter] of the graph-based fake news detection startup Fabula AI I have founded together with my students), we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In the following, I will try to outline my views on the possible reasons and how the field could progress in the next few years.

**Standardised benchmarks **like ImageNet were surely one of the key success factors of deep learning in computer vision, with some [6] even arguing that data was more important than algorithms for the deep learning revolution. We have nothing similar to ImageNet in scale and complexity in the graph learning community yet. The [Open Graph Benchmark] launched in 2019 is perhaps the first attempt toward this goal trying to introduce challenging graph learning tasks on interesting real-world graph-structured datasets. One of the hurdles is that tech companies producing diverse and rich graphs from their users’ activity are reluctant to share these data due to concerns over privacy laws such as GDPR. A notable exception is Twitter that made a dataset of 160 million tweets with corresponding user engagement graphs available to the research community under certain privacy-preserving restrictions as part of the [RecSys Challenge]. I hope that many companies will follow suit in the future.

**Software libraries **available in the public domain played a paramount role in “democratising” deep learning and making it a popular tool. If until recently, graph learning implementations were primarily a collection of poorly written and scarcely tested code, nowadays there are libraries such as [PyTorch Geometric] or [Deep Graph Library (DGL)] that are professionally written and maintained with the help of industry sponsorship. It is not uncommon to see an implementation of a new graph deep learning architecture weeks after it appears on arxiv.

Scalability is one of the key factors limiting industrial applications that often need to deal with very large graphs (think of Twitter social network with hundreds of millions of nodes and billions of edges) and low latency constraints. The academic research community has until recently almost ignored this aspect, with many models described in the literature completely inadequate for large-scale settings. Furthermore, graphics hardware (GPU), whose happy marriage with classical deep learning architectures was one of the primary forces driving their mutual success, is not necessarily the best fit for graph-structured data. In the long run, we might need specialised hardware for graphs [7].

**Dynamic graphs **are another aspect that is scarcely addressed in the literature. While graphs are a common way of modelling complex systems, such an abstraction is often too simplistic as real-world systems are dynamic and evolve in time. Sometimes it is the temporal behaviour that provides crucial insights about the system. Despite some recent progress, designing graph neural network models capable of efficiently dealing with continuous-time graphs represented as a stream of node- or edge-wise events is still an open research question.

#deep-learning #representation-learning #network-science #graph-neural-networks #geometric-deep-learning #deep learning

Deep Learning Explained in Layman's Terms

In this post, you will get to learn deep learning through a simple explanation (layman terms) and examples.

Deep learning is part or subset of machine learning and not something that is different than machine learning. Many of us, when starting to learn machine learning, try and look for the answers to the question, “What is the difference between machine learning and deep learning?” Well, both machine learning and deep learning are about learning from past experience (data) and make predictions on future data.

Deep learning can be termed as an approach to machine learning where learning from past data happens based on artificial neural networks (a mathematical model mimicking the human brain). Here is the diagram representing the similarity and dissimilarity between machine learning and deep learning at a very high level.

#machine learning #artificial intelligence #deep learning #neural networks #deep neural networks #deep learning basics

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

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