build a simple neural network with tensorflow 2 0 in google colab

sure! in this tutorial, we will learn how to build a simple neural network using tensorflow 2.0 in google colab. we will use a popular dataset, the mnist dataset, which consists of handwritten digits. our goal will be to create a model that can classify these digits.

step 1: setting up google colab

2. create a new notebook by clicking on `file new notebook`.

step 2: importing libraries

first, we need to import the necessary libraries. we will use `tensorflow` for building the neural network and `numpy` for numerical operations.

step 3: load the mnist dataset

tensorflow provides a convenient way to load the mnist dataset. we will load the dataset and preprocess it.

step 4: building the neural network model

we will create a simple feedforward neural network using the sequential api in keras.

step 5: compile the model

next, we need to compile the model by specifying the optimizer, loss function, and metrics to track.

step 6: train the model

now, we can train the model using the training data. we will use 5 epochs for training.

step 7: evaluate the model

after training, we can evaluate the model's performance on the test dataset.

step 8: make predictions

finally, we can use the model to make predictions on new data.

conclusion

in this tutorial, you learned how to build a simple neural network using tensorflow 2.0 in google colab. you loaded the mnist dataset, built a feedforward neural network, trained it, and evaluated its performance. you also made predictions on test images.

feel free to experiment with the model architecture, number of epochs, or try different datasets. happy coding!

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