TensorFlow is a library that allows users to showcase unpredictable computation as a graph of data flows. It has grown to be one of the most loved and widely adopted ML platforms in the industry and in research.
TensorFlow provides a collection of workflows to develop and train models, which can be deployed via cloud, on-prem, in the browser, or on-device.
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TensorFlow has grown to be one of the most loved and widely adopted ML platforms in the industry and research. It is an open source machine learning platform that helps you develop and train machine learning models. At a high level, TensorFlow is a library that allows users to showcase unpredictable computation as a graph of data flows. It leverages various optimization techniques to make the calculation of mathematical expressions easier and more performant.
It was developed by the Google Brain Team within Google's Machine Intelligence research organization with an intention of doing research in the fields of Machine Learning and Deep Learning. At the time of writing this article, TensorFlow 2.0 was released with features that make this library more powerful and robust for creating Machine Learning models.
Features of TensorFlow
Machine/Deep Learning Services: TensorFlow was developed by the Google Brain Team within Google's Machine Intelligence research organization with an intention of doing research in the fields of Machine Learning and Deep Learning. This library exposes a lot of built-in algorithms and APIs for speech recognition, image recognition, image search, art creation, sentimental analysis, natural language processing, building neural networks and search engines.
Multiple Platform Support: TensorFlow is cross-platform and can be used to build and train machine learning models on Linux, MacOS, Windows, Android, iOS and Raspberry Pi. It can run on multiple CPUs, GPUs, Mobile Operating Systems and TPUs. TensorFlow models can be deployed on different environments including cloud, on-prem, in the browser and on-device.
Libraries & Extensions: To access domain specific application packages, building advanced models and methods and accelerating workflows, TensorFlow offers a wide variety of libraries, tools and extensions. These tools and libraries are domain specific and helps in solving a specific set of challenges.
Vibrant Community: TensorFlow has grown to be the de facto ML platform and the favorite amongst Data Scientists, Researchers and machine learning experts. Being an open source library, TensorFlow encourages enthusiasts to contribute towards the community. This has made learning TensorFlow much easier due to the variety of information available through YouTube channels, Blogs, Forums and many other sources.
Machine Learning Ecosystem
Machine learning is vast and has a variety of technologies and libraries that help you develop and train Machine Learning models. In this section, we are going to take a quick look at those technologies.
Keras: Keras is a popular Python library for high-level neural networks. It stands out for its speed, modularity, and extensibility. Instead of dealing with low-level computations like tensor products and convolutions, Keras focuses on providing a user-friendly interface. It utilizes back-end libraries such as TensorFlow, Microsoft Cognitive Toolkit, R, Theano, and PlaidML to handle the low-level tasks efficiently.
Scikit-learn: Scikit-learn is a free Python library for machine learning. It supports NumPy and SciPy and provides a wide range of supervised and unsupervised learning algorithms. It covers essential functionalities like classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. Scikit-learn is a robust library that enables the creation of production-ready machine learning models using Python.
Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit (CNTK) is an open-source deep learning framework that supports the ONNX format for interoperability. It runs on 64-bit Linux and Windows, Universal Windows Platform (UMP), and Azure. CNTK can be integrated as a library in programs written in C#, Java, Python, and C++.
Theano: Theano is a Python library primarily used for evaluating complex mathematical expressions. It offers tight integration with NumPy and excels in data-intensive computations on the GPU. While Theano itself is not a machine learning library with pre-built models, it provides tools to construct custom machine learning models.
Caffe: Caffe is a deep learning framework designed for efficiency and versatility. It is widely used in both academic and industrial AI projects. Caffe provides pre-trained models, optimization settings, and support for CPUs and GPUs, making it suitable for various architectures like CNN, LRCN, LSTM, and fully connected neural networks. Its focus on speed and modularity makes it a valuable tool for quick deployment and development in AI applications.
Torch: Torch is an open-source machine learning library known for its wide range of algorithms and deep learning capabilities. It offers flexible N-dimensional arrays, supports linear algebra and numerical optimization routines, and provides functionalities for neural networks, energy-based models, and basic tensor operations. PyTorch, a popular machine learning library, is built on top of Torch.
Spark: Spark is an open-source cluster computing framework designed for fast big data processing and machine learning. It offers a scalable machine learning library with algorithms for clustering, collaborative filtering, and dimension reduction. The library includes regression, clustering, classification, decision trees, random forests, topic modeling, and optimization primitives. Spark also provides workflow utilities for feature transformation, pipeline construction, model evaluation, hyperparameter tuning, and persistence. Learn more.
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Why is data science hard for beginners? It’s because the entire process is quite complex and requires expertise in many different facets including information retrieval, data engineering and data science. At the bare minimum, the process consists of the following steps:
Our team of experts is extremely fortunate to work with top companies all over the world. We have the added advantage of creating best practices after witnessing what works and what doesn't work in the industry. We can help you with the full development life cycle of your products, from initial consulting to development, testing, automation, deployment, and scale in an on-premises, multi-cloud, or hybrid environment.
Technology stack: We can help create top AI solutions with incredible user experience. We work with the right AI stack using top technologies, frameworks, and libraries that suit the talent pool of your organization. This includes OpenAI, Azure OpenAI, Semantic Kernel, Pinecone, Azure Search, FAISS, ChromaDB, Redis, Weaviate, Stable Diffusion, PyTorch, TensorFlow, Keras, Apache Spark, Scikit-learn, Microsoft Cognitive Toolkit, Theano, Caffe, Torch, Kafka, Hadoop, Spark, Ignite, and/or others.
Develop models, optimize them for production, deploy and scale them.
Best practices: Introduce best practices into the DNA of your team by delivering top quality machine learning (ML) and deep learning (DL) models and then training your team.
Incorporating ML/DL models in your existing enterprise solutions.
Customized AI Solutions - The Future of Business Efficiency: Develop enterprise apps or augment existing apps with real time ML/DL models. This includes Web apps, iOS, Android, Windows, Electron.js app.
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