Did you know our CEO, Chander Dhall, who has been awarded by both Microsoft (Microsoft Most Valuable Professional for close to a decade) and Google (Google Developer Expert) has interacted and shared knowledge with creators of machine learning technologies? Did you know Cazton has been a big supporter of open source and has experts who have contributed to open source machine learning libraries as well as data engineering libraries? Did you know the Cazton team has been working on machine learning projects long before its competitors? Did you know Cazton's team of data scientists and machine learning experts have created its own ML libraries?
Machine learning projects require serious understanding of data. Cazton’s data scientists with years of successful experience in the industry have worked in multiple business domains. Our machine learning experts not only evaluate the data, but are adept at all facets of data engineering. If you are serious about doing machine learning properly, please check out our expert Machine Learning team comprised of PhDs, as well as Microsoft-awarded Most Valuable Professionals and Google Developer Experts.
At Cazton, we not only offer TensorFlow development and consulting services, but also world class training services. We are also one of the very few companies that offer flexible training. For example, if you want to learn more about machine learning, but also want to learn data engineering and data science we can help. If you want to learn TensorFlow, but also want to learn Scala, Hadoop, Ignite, Kafka and related technologies we can help. Interested in our TensorFlow training services? Click here to learn more.
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What is TensorFlow?
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 one of the leading high-level neural networks library written in Python. The reason why Keras has gained a lot of attention is because it is fast, modular and highly extensible. Keras doesn't handle low-level computation such as tensor products and convolutions. It relies on back-ends libraries to perform those low-level tasks. It is a high-level API wrapper that is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano and PlaidML.
- Scikit-learn: Scikit-learn is a free machine learning library for Python programming language. It supports Python numerical and scientific libraries like NumPy and SciPy and offers a variety of supervised and unsupervised learning algorithms via a consistent interface in Python. The functionality that scikit-learn provides include Classification, Regression, Clustering, Dimensionality reduction, Model selection and Preprocessing. This library is robust and can be used to build Machine Learning models using Python for production.
- Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit, which is often abbreviated as CNTK, is an open source deep learning framework. It is one of the first deep-learning frameworks to support the Open Neural Network Exchange (ONNX) format which enables interoperability and allow us to run trained neural network on programs written in Java and C#. This toolkit can run on 64-bit Linux and 64-bit Windows operating systems, Universal Windows Platform (UMP) and Azure. It can be included as a library in programs written in C#, Java, Python and C++.
- Theano: Theano is yet another Python library that is used in building deep learning projects. Theano at its heart is a library that is built for evaluating complex mathematical expressions due to its tight integration with NumPy. Theano performs data-intensive computations much faster on the GPU than on a CPU. Theano isn’t actually a machine learning library as it doesn’t provide pre-built models to train our datasets. It is a mathematical library that provides tools to build our own machine learning models.
- Caffe: Caffe is yet another deep learning framework made with expression, speed, and modularity in mind. This framework was originally created by Yangqing Jia at UC Berkeley and was later developed by Berkeley AI Research (BAIR) and community contributors. This framework is well-tested and can be used for both academic research projects and industrial applications in AI. It offers model definitions, optimization settings, pre-trained models so that one can start right away. It is primarily for speed offers support for CPUs and GPUs. The framework is suitable for various architectures such as CNN (Convolutional Neural Network), Long-Term Recurrent Convolutional Network (LRCN), Long Short-Term Memory (LSTM ) or fully connected neural networks.
- Torch: Torch is an open source machine learning library that offers a wide range of algorithms for deep learning. It provides a flexible N-dimensional array or Tensor and offers support for linear algebra routines, numerical optimization routines, neural network, energy-based models and basic routines for indexing, slicing, transposing, type-casting, resizing, sharing storage and cloning. One famous machine learning library that has been built on top of Torch is PyTorch.
- Spark: Spark is an open-source, lightning fast, cluster computing framework that provides a fast and powerful engine for big data processing and creating machine learning models. Many data scientists prefer to use Spark’s scalable machine learning library that enables clustering, collaborative filtering and dimension reduction. This library consists of machine learning algorithms and utilities that includes Regression, Clustering, Classification, Decision trees, Random forests, Collaborative filtering, Dimensionality reduction, Topic Modeling and underlying optimization primitives. Its workflow utilities include feature transformation, machine learning pipeline construction, model evaluation and hyper-parameter tuning and persistence mechanism.
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:
- Data Collection
- Storage and data flow
- ETL (Extract, Transform and Load)
- Clean up and anomaly detection
- Aggregation and training
Learn more about these critical steps.
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