The complex problems in the world of computers and artificial Intelligence need the aid of deep learning tools. The challenges change with time, and so does the analysis pattern. Constant updates on tools and newer perspectives to deal with the problems require hands-on expertise and experience handling deep learning tools. Review the updated list of top tools and the key features of each.
Deep learning is a subset of Machine Learning and part of Artificial Intelligence which is important for computers’ operation learning. The associated deep learning tools are responsible for curating the programs that process the computer’s data and patterns for decision-making. It is capable of predictive analytics through algorithms.
Top 10 Deep Learning Tools
Here are top 10 deep learning tools:
TensorFlow
Keras
PyTorch
OpenNN
CNTK
MXNet
DeeplearningKit
Deeplearning4J
Darknet
PlaidML
TensorFlow
Key Features
TensorFlow provides interface in different languages like Go, Java, and Python.
Seamless processing from Python development to mobile devices deployment
Adaptable to C++ interface, allowing low latency and high-performance applications.
Allows different GPU supports for implementing deep learning models
Comprises of direct interface with ONNX in the standard ONNX format (Open Neural Network Exchange)
Provides robust ecosystem libraries for efficient developments
OpenNN
Key Features
Suited for non-technical experts as it does not require programming to create neural networks
Efficient in speed execution and memory allocation
Allows normalization, feature scaling, and automatic differentiation
The user interface is easily learnable for functionalities like data management
Easy interpretation from neural designer tool
Quick training, thus saving time
CNTK
Key Features
CNTK, or Microsoft Cognitive Toolkit, is also open source deep learning framework available at a commercial scale
Supports different programming languages like C++, C, and Python and is integrated with Microsoft Azure
Allows easy combination of diverse deep learning models such as deep-feed forward neural network, recurrent neural network, and convolutional neural network
Offers model programming language BrainScript
Self-capable of parallelization and differentiation on different serves and GPUs
Evaluation supported by Java Apps
Multi-support is offered to different learning methods such as supervise, reinforcement, unsupervised learning, and generative adversarial networks.
MXNet
Key Features
Offered by Apache, version 7.0 of open source deep learning framework was released in 2016
Assists in symbolic and imperative programming features along with automatic differentiation and gradient optimization features
Efficiency evident by compatibility with dual parameters for Horovod and server for training and performance optimization
Supports multiple programming languages, such as Perl, Scala, Java, C++, R, Clojure and
Contains pre-trained models
Offers detailed and flexible Python APIs
Efficient with scalability options
DeeplearningKit
Key Features
It is the open-source deep learning framework
Compatible with operating systems like OS X, Apple iOS and tvOS
Performs image recognition on Apple devices using convolutional neural networks
Uses Metal for GPU acceleration and Swift for app integration
Deeplearning4J
Key Features
Supports different Java Virtual Machine-based languages like Scala, Kotlin, Clojure and Java
Capable of huge-sized text sets management and performing NLP tasks with vector space and topic model
Cluster-based training supported by Apache Hadoop and Spark
Performs numerous implementations such as deep belief networks, recursive neural tensor network, Boltzmann machine, word2vec, deep autoencoder, denoising autoencoder, doc2vecc, and GloVe
Provides good performance due to framework in CUDA and C
Compatible with GPU and CPU computations
Eases time series prediction, image classification, and NLP
Supports a variety of neural network architectures
Provides Command Line Interface
PlaidML
Key Features
Integrates with operating systems like Windows, MacOS, and Linux
Contains graph compatibility supports for novel platforms and GPUs
Provides modular hardware supports from embedded to new processors
Integrability with multiple deep learning frameworks such as ONNX, TensorFlow, and
Good for experimentation purposes
Allows automatic differentiation and integration with Python
Deep Learning Roles and Salary
Different roles deal with deep learning toolboxes (such as Matlab deep learning toolbox) and require hands-on machine learning with scikit-learn, Keras, and TensorFlow. Their salaries are tabulated as follows:
The emerging requirement for talented and skilled professionals with the right set of knowledge has created room for skilled candidates. Having an exact set of experience with accurately chosen tools is necessary to secure a job. The above-stated deep learning tools are among the currently trending ones in 2024. Do you have them in your skillset? If not, go on to learn and shine in the domain of deep learning. Remember to use your innovative bent of mind to prove your caliber.
Frequently Asked Questions
Q1. Which tools are used for deep learning?
A. Multiple well-known and updated tools, such as TensorFlow, PyTorch, MXnet, and others, are available for deep learning.
Q2. Which tool is best suited for deep learning problems?
A. The choice of deep learning tools depends on the problem, technical expertise, and available resources. Yet, the more generally suited tools are TensorFlow and PyTorch.
Q3. What are ML data tools?
A. The ML tools are those specialized in ML or Machine Learning workflows. They can efficiently handle and process the associated data.
Q4. Is deep learning a tool of AI?
Ans. Deep learning is a subfield of Artificial Intelligence and hence can be considered a tool of AI. It deals with the development and application of Artificial Neural Networks.
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