Top 5 Deep Learning Frameworks for 2019

Artificial Intelligence and Deep learning is being used extensively by companies to improve their services and products. There are several deep learning open source frameworks that can be easily integrated with basic knowledge to machine learning and deep learning, Top 5 Deep Learning Frameworks for 2019

Now more and more businesses are looking to scale up their products and operations using Deep Learning. It has become integral for the companies to imbibe both with Deep Learning and Machine Learning frameworks. In this Article we will be discussing about top 5 deep learning frameworks which can be used for boosting services and products with AI.

1. Tensorflow


Currently the most famous framework for deep learning is TensorFlow. TensorFlow is a free and open-source deep learning framework developed by Google Brain Team. It is a symbolic math library, and is also used for machine learning applications and deep learning applications.

TensorFlow is also used for numerical computation using DataFlow graphs. TensorFlow is used by lot of companies like Airbnb, Twitter, Dropbox, IBM etc.

TensorFlow eases the process of acquiring data, training models, serving predictions, and refining future results. It bundles together a variety of machine learning and deep learning models and algorithms.

TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition with convolutional neural network, recurrent neural networks, word embeddings, sequence-to-sequence models for machine translation, natural language processing, etc.

The Biggest benefit of using TensorFlow is abstraction. TensorFlow offers additional conveniences for developers who need to debug and gain insights on TensorFlow apps.

TensorFlow offers eager execution mode which lets you evaluate and modify each graph operation separately and transparently, instead of constructing the entire graph as a single object.

TensorFlow also has TensorBoard Visualisation suite which lets you inspect and profile the way graphs run by way of an interactive, web-based dashboard.

Coding Language Support: Python, C

2. PyTorch


PyTorch is built on top of Torch, a scientific computing librabry built for deep learning training and to perform tensor computations.

Pytorch is extensively used for deep learning and Natural Language Processing.  PyTorch uses CUDA architecure and C/C++ libraries to build robust and flexible models which can be scaled for different business and products.

PyTorch is much easier to understand and code as compared to TensorFlow. PyTorch models are simple and easy to use without having prior understanding of deep learning and machine learning models.

PyTorch lacks visualization tools like TensorBoard but it supports other python visualization like MatplotLib, Seaborn, etc.

Coding Language Support: Python, C, C++

3. Keras


Keras is an Open Source Neural network and Deep Learning package written in Python. It can run on top of tensorflow, theano, etc.

Keras has implemented multiple deep learning models which are very easy to implement. Keras doesn’t handle low-level computation, Instead, it uses another library to do it, called the “Backend.

Keras is a solid and robust framework for deep learning models like ANN, RNN, CNN etc.  Keras can run on top of TensorFlow, Theano, Microsoft cognitive toolkit, etc. Keras is designed to be fast, modular and easy to use.

Coding Language Support: Python

4. Caffe


Caffe is a deep learning framework developed by University of Berkeley, California by Berkeley AI Research (BAIR) team. It is written in C++ and has support for interfaces like python, MATLAB and C. Caffe stands out in speed of processing as compared to other deep learning models.

Caffe is fast which makes Caffe perfect for research experiments and deployment in industries. Caffe can process over 60M plus images per day with a single NVIDIA K40 GPU.

That is 1 ms/image for inference and 4 ms/image for learning. So, we can believe that Caffe is the fastest convnet implementation available.

Coding Language Support: Python

5. Deeplearning4j


Deep Learning 4J is written in JAVA and works with JVM languages. Deep Learning 4J is Open Source. This framework supports CPU as well as GPU.

Using Eclipse DeepLearning4j, Big Data analytics can be performed along with Apache Spark and Hadoop. You can use Java as well as Scala programming for performing Big Data Analytics. Deep Learning 4J integrates the techniques and algorithms of Artificial Intelligence (AI),

Which can be used for business intelligence, robotic process automation (RPA), network intrusion detection and prevention, recommended systems, predictive analytics, regression, face recognition, object detection, natural language processing, anomaly detection and many others.

Coding Language Support: JAVA, Scala