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Create your first deep-learning network with Tenserflow
TensorFlow is an open-source software library for machine and deep learning. It was created by the Google Brain Team and has a wide range of uses, such as neural networks for computer vision, natural language processing, and self-driving vehicles.
With high performance and efficiency, TensorFlow enables users to construct, optimize, and assess mathematical expressions involving multi-dimensional arrays. Researchers and developers choose it because it offers a versatile and high-level API for building, honing, and deploying machine learning models. Additionally, TensorFlow has a sizable and expanding contributor community that offers a plethora of tools, tutorials, and resources for users to employ.
In order to create your first deep-learning model using Tensorflow, you really don't need to know much about machine learning, but if you really want to know what happens in the background and how machine-learning models work, you need to learn a little about different machine learning methods and have some mathematical background. Those topics are not really the scope of this article, and I will just try to explain some basic concepts as we are creating our model to familiarize you with the terminology.
Requirements
Tensorflow library is available for different programming languages like Python, JavaScript, C++, and Java. In this article, we will use Python since it is easy to use and set up. The following requirements are not a must, but they are good to have.
- Basic Python knowledge
- Basic Machine-learning knowledge
To run the app, you will need to have a Python environment set up to run your codes. You can use different IDEs like PyCharm which is free and simple to use, or any other editor that you are comfortable with.
Another option is to use Colab or JupyterLab. They will provide the Python environment to run your code and also write notes and comments. If you are familiar with the…
