In today’s world, more and more organizations are turning to machine learning and artificial intelligence (AI) to improve their business processes and stay ahead of the competition.
The growth of machine learning and AI has enabled organizations to provide smart solutions and predictive personalizations to their customers. However, not all organizations have the liberty to implement machine learning and AI for their processes due to various reasons.
This is where the services of various deep learning frameworks come in. These are interfaces, libraries, or tools, which are generally open-source that people with little to no knowledge of machine learning and AI can easily integrate. Deep learning frameworks can help you upload data and train a deep learning model that would lead to accurate and intuitive predictive analysis.
In this article, we’ll cover some of the frameworks set around deep learning and neural networks, including:
Google’s Brain team developed a Deep Learning Framework called TensorFlow, which supports languages like Python and R, and uses dataflow graphs to process data. This is very important because as you build these neural networks, you can look at how the data flows through the neural network.
TensorFlow’s machine learning models are easy to build, can be used for robust machine learning production, and allow powerful experimentation for research.
With TensorFlow, you also get TensorBoard for data visualization, which is a large package that generally goes unnoticed. TensorBoard simplifies the process for visually displaying data when working with your shareholders. You can use the R and Python visualization packages as well.
Francois Chollet originally developed Keras, with 350,000+ users and 700+ open-source contributors, making it one of the fastest-growing deep learning framework packages.
Keras supports high-level neural network API, written in Python. What makes Keras interesting is that it runs on top of TensorFlow, Theano, and CNTK.
Keras is used in several startups, research labs, and companies including Microsoft Research, NASA, Netflix, and Cern.
Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan authored PyTorch. It’s built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. PyTorch employed CUDA, along with C/C++ libraries, for processing and was designed to scale the production of building models and overall flexibility. If you’re well-versed with C/C++, then PyTorch might not be too big of a jump for you.
PyTorch is widely used in large companies like Facebook, Twitter, and Google.
The University de Montreal developed Theano, which was written in Python and centers around NVIDIA CUDA, allowing users to integrate it with GPS. The Python library allows users to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays.
Master deep learning concepts and the TensorFlow open-source framework with the Deep Learning Training Course. Get skilled today!
A machine learning group led by Adam Gibson developed this Deep Learning Framework Deeplearning4j. Written in java and scala, DL4J supports different neural networks, like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and LSTM (Long Short-Term Memory).
After Skymind joined the Eclipse Foundation in 2017, DL4J was integrated with Hadoop and Apache Spark. It brings AI to business environments for use on distributed CPUs and GPUs.
Developed at BAIR or Berklee Artificial Intelligence Research, Caffe stands for Convolutional Architecture for Fast Feature Embedding. Caffe is written in C++ with a Python Interface and is generally used for image detection and classification.
Developed by PreferredNetworks in collaborations with IBM, Intel, Microsoft, and Nvidia, Chainer is written purely in Python. Chainer runs on top of Numpy and CuPy Python libraries and provides a number of extended libraries, like Chainer MN, Chainer RL, Chainer CV, and many other extended libraries.
Microsoft Research developed CNTK, a deep learning framework that builds a neural network as a series of computational steps via a direct graph. CNTK supports interfaces such as Python and C++ and is used for handwriting, speech recognition, and facial recognition.
All of these deep learning packages have their own advantages, benefits, and uses. It’s not mandatory that you stick to a single framework—you can jump back and forth between most.
To learn more about deep learning frameworks, you can opt for Simplilearn’s Deep Learning Course, which is developed by industry leaders and aligned with the latest best practices. Enroll with us and you’ll master deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms, preparing you for a career in deep learning.
Name | Date | Place | |
---|---|---|---|
Artificial Intelligence Engineer | Class starts on 6th Feb 2021, Weekend batch | Your City | View Details |
Artificial Intelligence Engineer | Class starts on 8th Feb 2021, Weekdays batch | Los Angeles | View Details |
Artificial Intelligence Engineer | Class starts on 13th Feb 2021, Weekend batch | Chicago | View Details |
Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.
Artificial Intelligence Engineer
Machine Learning
Deep Learning with Keras and TensorFlow
*Lifetime access to high-quality, self-paced e-learning content.
Explore CategoryHow to Become a Machine Learning Engineer?
Artificial Intelligence Career Guide: A Comprehensive Playbook to Becoming an AI Expert
AI Engineer Salaries From Around the World and What to Expect in 2020-21
How To Become an Artificial Intelligence Engineer?
How to Become a Big Data Engineer?
Getting Started With Azure AI Tools