How to become a TensorFlow dev

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How to become a TensorFlow developer

(Partner Content) AI is no longer an emerging technology, it is already here with us and is being adopted widely across industries. AI technology has disrupted operations, giving businesses better means of understanding and interacting with their customers.

How to become a TensorFlow dev

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Deep learning is one of the AI branches that has seen the widest adoption with a predicted $10.2 billion worth by 2025 and a growth rate of 52.1% CAGR. At the same time, the demand for deep learning skills is high and professionals with credentials like TensorFlow Certification will be highly sought after. 

Increased GPU computing capabilities, availability of data in large volumes, and improvements in ML algorithms are considered key growth factors for deep learning. Soon it will be crucial for businesses to integrate deep learning solutions in their structures as they seek to apply this technology in analyzing visual, audio, and text content in order to gain insight.  Already adoption is highest in the healthcare and automotive spaces. 

How is deep learning market growing

Deep learning is a subcategory of machine learning in the field of AI, that uses artificial neural networks to process data and create patterns useful for decision making by imitating the functioning of the human brain’s neural network. This technology has the capability of learning unstructured data of varying types including images, text, and audio with very minimal pre-processing, a feature that makes it unique from other categories of machine learning. 

Deep learning is widely used for voice search in virtual assistants like Cortana, Alexa, and Siri to improve customer experience. Voice search queries, as established by Gartner, are the fastest-growing mobile search types. As such, brands whose websites support visual and voice search stand to increase their revenue by 30% in 2021. 

Other applications of deep learning include: 

  • Speech and image recognition
  • Fraud detection 
  • Social network filtering 
  • Natural language processing 
  • Medical image analysis 
  • Drug design 
  • Detection, diagnosis, and treatment of disease 
  • Surveillance 
  • Self-driving cars
  • Natural disaster prediction 
  • Board games programs 

There are far too many applications of deep learning. The hardware segment is fastest growing thanks to the demand for chipsets with high computing power which will be used in running deep learning algorithms. The effect will be reduced cost of hardware further increasing adoption of deep learning solutions. 

The healthcare industry stands as the largest beneficiary of deep learning solutions with an anticipated growth of 55% with deep learning being incorporated in bioinformatics, medical imaging, and sensor-driven analysis. 

As the deep learning market grows, a number of frameworks are being developed to make it easy and simple to build complex deep learning models. Such frameworks include Keras, PyTorch, Caffe, and TensorFlow. The latter is the most widely used. 

What is TensorFlow 

TensorFlow is the most popularly used deep learning framework. A deep learning framework is an interface or a library that data scientists use to build deep learning models without getting into the basics of its algorithms. 

TensorFlow, Google’s open-source platform for developing deep learning models, is written in JavaScript language. Its flexible architecture allows you to deploy DL models on CPUs, GPUs, in the cloud, on devices, and in browsers. and other platforms like Google cloud. TensorFlow runs on Linux, Android, Mac, and Windows operating systems.

How TensorFlow works 

TensorFlow is derived from the terms, Tensors, and DataFlow graphs. Tensors flow through dataflow graphs. DataFlow graphs, on the other hand, are structures that show how data moves through a graph or through nodes. 

Developers first build a dataflow graph in which they define constants, variables, and operations. The graph contains network nodes with each node representing a mathematical operation. A tensor is a connection between nodes. Data moves through the nodes and tensors which performs operations on it. 

Advantages of TensorFlow 

  • TensorFlow features a flexible architecture which allows models to be deployed on CPUs, GPUs, the cloud, macOS, Linux, and Android devices
  • TensorFlow supports multiple programming languages in creating the DL models including Python, C++, R, Java, and more 
  • It comes with excellent library management capabilities including quick updates and frequent releases with additional features 
  • It is open-source and customizable
  • TensorFlow enjoys great community support 
  • It is easy to use 
  • TensorFlow version 2.0 features additional features including Keras integration, eager execution, and TensorBoard which make it even more functional

TensorFlow Applications 

  • Text-based applications including language detection, text classification, and summarization, 
  • Image recognition including face recognition, image captioning, and object detection 
  • Speech recognition 
  • Video analysis 
  • Time series analysis

What is a TensorFlow Developer

TensorFlow developers build and train neural network models using the TensorFlow framework. They are responsible for developing and maintaining TensorFlow systems and applications with interactive user interfaces, TensorFlow chatbots to enhance customer experience, DataFlow graphs, OCR, and ICR, and complex computations. 

A TensorFlow developer should have the following vital skills

  • Fundamentals of machine learning and deep learning 
  • Mathematics background in linear algebra, statistics, probability, and calculus
  • Knowledge of programming languages particularly Python, R, C++, and Java
  •  Basic knowledge of neural networks 
  • Advanced business analytics
  • An understanding of SDLC including the Agile methodology, CI/CD concept

How to become a TensorFlow Developer  

TensorFlow is the most widely adopted deep learning framework. It has attracted big names in the industry like Uber, Twitter, and Snapchat as it continues to grow in popularity. This makes it a must-have skill for the deep learning developer.

However, you must first have basic knowledge of ML concepts and principles and how to apply them to grow your skills. Together with these, be familiar with linear algebra, statistics, calculus, and probability. 

What does it take to become a TensorFlow developer? 

Formal qualifications 

A bachelor’s degree in mathematics, computer science, physics, or any other relevant qualification is a great place to start. 

Software development experience 

Previous software development experience is vital since technically you will just be upskilling to include TensorFlow in your skillset. Get to understand the phases of the SDLC and particularly the Agile methodology and the continuous integration of continuous delivery (CI/CD) concepts. 

Learn how to train neural networks

Learn how to train deep neural networks with large datasets. Most DL frameworks, TensorFlow included, are accelerated on GPUs as this offers the flexibility to build new frameworks easily and model architectures from libraries without the need for programming. GPU-accelerated deep learning frameworks come with built-in interfaces for programming languages like Python, C++, and C that are popularly used. 

Learn programming languages 

A solid understanding of Python programming language as the language mostly used with the TensorFlow framework. Other languages that you should consider learning include C and C++ because it is not enough to learn one language. 

TensorFlow Developer certification 

The TensorFlow developer certification is a demonstration that a data scientist or ML developer has the practical skills to build neural network models using the TensorFlow framework. It is an indication that a professional possesses fundamental knowledge on ML and DL based on the TensorFlow framework and can integrate it into various applications including natural language processing, image and speech processing, and others. 

Conclusion

Are you ready to launch your career or upskill in TensorFlow? Consider enrolling for this Deep Learning course with Keras and TensorFlow.