Python Frameworks for Your Upcoming Deep Learning and Data Science Projects


26/06/19

Owing to its simplicity, elegant syntax, interactive mode, and large standard library, Python has become the world’s most popular programming language. In fact, according to the Stack Overflow Trends, Python is the fastest growing language in high-income countries like the United States, United Kingdom, Germany, and Canada among others.

Top tech organizations, namely Google, Amazon Web Services, Hewlett-Packard, IBM, Quora, and Facebook are not only using Python in their products but also investing in the language. No wonder, there’s a growing demand for Python experts globally. 

Python’s success as a modern coding language makes it the language of choice among software developers, data scientists, and AI and deep learning professionals. 

Below is a list of the most popular Python frameworks for two major applications, deep learning and data science. 

 

Top Python Frameworks for Deep Learning Projects

An ideal deep learning framework is optimized for performance, easy to understand and code, enjoys good community support, and automatically computes gradients and parallelizes processes to reduce computations. 

Let’s take a closer look at the top Python frameworks that allow deep learning experts to build models easily without getting into the details of the underlying algorithms and codes.

 

  • TensorFlow

 

  With top brands like Gmail, Uber, and Airbnb using it, Google’s TensorFlow is the undisputed heavyweight champion among all the deep learning frameworks. This open-source framework enjoys excellent community support and has pre-written codes for the most complicated deep learning models, including Recurrent Neural Networks and Convolutional Neural Networks.

  Python is the most convenient programming language for working with TensorFlow.  The framework can perform computation and data visualization quite efficiently and tap into GPU (Graphics Processor Unit) to speed up the process. 

 

  • Keras

 

  Keras is a great Python framework for those who are starting out on their deep learning journey. The framework is written in Python and is a high-level neural network API (unlike TensorFlow, which is a low-level library), developed to learn and prototype simple concepts and understand models. 

  Use Keras if you require a deep learning framework that – 

  • Is user-friendly and allows fast experimentation and prototyping
  • Supports convolutional and recurrent neural networks or a combination of both
  • Runs seamlessly on GPUs and CPUs

 

  • PyTorch

 

  Facebook’s PyTorch is a flexible deep learning framework that runs on Python. Unlike TensorFlow, PyTorch operates with a dynamically updated graph that allows it to build deep neural networks and change tensor computations on the go. This feature helps when working on deep learning situations where one is not sure how much memory is required for building a neural network. 

  The framework supports data parallelism and distributed learning model and is ideal for small projects and prototyping. It can be effectively used to manage deep learning for images (detection and classification), text (natural language processing), and reinforcement learning. 

 

  • Caffe

 

  Caffe is yet another popular open-source deep learning framework that supports Python and other interfaces like C, C++, and MATLAB. Though the framework doesn’t offer exceptional support for recurrent networks and language modeling (like TensorFlow, PyTorch, or Keras), it is proving to be an asset in the image processing field owing to its speed of processing and learning from images. In fact, Caffe can process more than 60 million images daily with a single NVIDIA K40 GPU. 

 

Top Python Frameworks for Data Science Projects

An increasing number of data scientists are switching to Python because of its excellent math library. Further, in comparison to R, Python enjoys massive community support. Several organizations use Python to evaluate large datasets and for data visualization and prototyping. If you are planning to apply Python programming to your next data crunching and scientific computing project, here’s a list of the top frameworks that can help you.

 

  • NumPy

 

  NumPy (Numerical Python) is a fundamental framework for mathematical and scientific computing with Python. The framework allows data scientists to convert Python into a powerful scientific analysis tool. The Array Interface is one of its most significant features that support large multi-dimensional arrays. The framework has a vast collection of high-level math and statistical functions that can operate on these arrays.

  Additionally, NumPy can be used to express images or raw binary streams as arrays of real numbers with N dimensions. 

 

  • SciPy

 

  SciPy is an open-source Python-based ecosystem designed for math, science, and engineering applications. Since the framework includes modules for special functions, optimization, linear algebra, integration, interpolation, and ODE solvers, it is primarily used for technical and scientific computing. 

 

  • Theano

 

  Theano is a useful Python library for data scientists that perform large multi-dimensional arrays related computing operations. Data scientists can use Theano to optimize, express, and evaluate array-enabled mathematical operations. Since it has a GPU-based infrastructure, it can process operations faster than the CPU. 

  Just like TensorFlow, Theano can be used on parallel and distributed environments. Its dynamic C code generator makes it a popular choice among data scientists. 

 

  • PANDAS

 

  If you are into data munging, PANDAS (Python Data Analysis Library) is the library for you. PANDAS offers powerful tools for shaping, aggregating, analyzing, and visualizing datasets and performing extensive data analysis

  PANDAS supports automatic data alignment, makes it easy to handle missing data, and allows you to work with differently-indexed data gathered from multiple data sources.

 

  • Matplotlib

 

  Matplotlib is a 2D Python scientific framework that’s popular among data scientists owing to its ability to design figures in multiple formats and produce simple and powerful data visualizations. You can use this framework for generating creative graphs, bar charts, scatter plots, and histograms without worrying about writing the codes. 

Wrapping Up

Python is the go-to language for deep learning professionals and data scientists. This is because the language helps them create sophisticated models and carry out complex mathematical computations, allowing them to escalate the functionality of their applications. The Python frameworks shared in this post have made it easier for these professionals to complete such complicated projects without having to face any coding hassles. 

We are sure you will use the information shared above to choose the most appropriate Python framework for your next deep learning and data science project.

 

Gaurav Belani is a senior SEO and content marketing analyst at The 20 Media, a content marketing agency that specializes in data-driven SEO. He has more than seven years of experience in digital marketing and loves to read and write about AI, machine learning, data science, and other emerging technologies. In his spare time, he enjoys watching movies and listening to music. Connect with him on Twitter @belanigaurav.


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