Today I'm going to talk about TensorFlow Playground, an interactive tool that allows you to visualize and understand how neural networks work. It allows us to:
Build and train neural networks:
You can choose between different types of layers, such as perceptrons, convolutions, and recursives, as well as adjust network parameters such as learning rate, number of epochs, and batch size.
You can train the network with different data sets, such as MNIST (digit recognition) or CIFAR-10 (image classification).
Visualize the operation of the network, since you can see how the network transforms the data as it passes through the different layers.
See how the network classifies different data examples and how the net weights change during training.
Experiment with different network architectures.
You can try different types of layers and different parameter settings and thus compare the performance of different network architectures.
Learn about neural networks that are basic today in GenAI. TensorFlow Playground gives us information about the different components of a neural network, it also helps us understand how neural networks work and how they can be used to solve machine learning problems and it is useful for interacting with neural networks as it allows us to build, train and visualize neural networks.
Below are some examples of how you can use TensorFlow Playground
If you are just getting started with neural networks, you can use TensorFlow Playground to build a simple neural network and learn how it works.

If you are working on a machine learning project, you can use TensorFlow Playground to experiment with different network architectures and find the best one for your problem.
If you are teaching about neural networks, you can use TensorFlow Playground to help your students understand how neural networks work.
If you develop projects in the cloud, you can use TensorFlow on EC2 instances (AWS) and its application in LLMs (Large Language Models).
TensorFlow on EC2 Instances
TensorFlow is an open source machine learning library developed by Google. Amazon Web Services (AWS) offers Elastic Compute Cloud (EC2) instances that can be used to run TensorFlow and train machine learning models.
EC2 instances offer several advantages for running TensorFlow, such as scalability as EC2 instances can be easily scaled to meet the computing needs of machine learning models.
EC2 instances can be used in conjunction with other AWS services such as Amazon S3 (object storage) and Amazon SageMaker (machine learning platform) to interact.
Applying TensorFlow in LLMs
LLMs are language models that are trained on large amounts of text and can generate coherent and natural text. TensorFlow is one of the most popular libraries for training LLMs.
Using larger and more complex language models as language models are increasing in size and complexity, requiring more computing power and better optimization techniques.
Using more specialized language models as language models are being trained to perform specific tasks, such as language translation or question answering.
TensorFlow is a powerful tool for training language models, and its application in LLMs is an area of ​​active research and development and you can use it on AWS.