# tensorflow

Tensorflow insights - part 1: Image classification from zero to a trained model

When we start a machine learning project, the first mandatory question is where we get data from and how the data is prepared. Only when this stage has been completed, does we can go to the training stage. In this tutorial, we first introduce you how to use and prepare the [Stanford Dogs dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/); then we show the 5 core steps to implement a neural network for training in Tensorflow. _You can reach the code version of this post in [here](https://github.com/willogy-team/insights--tensorflow/tree/main/part1)._

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Tensorflow - part 4: Graph in Tensorflow

They are the two types of execution in Tensorflow. Eager execution is easier to use, but Graph execution is faster.

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Tensorflow - part 3: Automatic differentiation

Automatic differentiation is very handy for running backpropagation when training neural networks.

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Tensorflow - part 2: Ragged tensors and tf.variable

In this post, we will tell about ragged tensors and ```tf.Variable```.

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Tensorflow - part 1: Creating and manipulating tensors (continue)

In the previous part, we have shown how to create a tensor, convert a tensor to numpy array, some attributes of a tensor, math operations, concatenating and stacking, reshaping. Now, we will tell a litte more things about what we can do with tensors.

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Tensorflow - part 1: Creating and manipulating tensors

When learning and working with machine learning, we have to get on well with tensors. In this tutorial, we we will show some of the ways to create and manipulate tensor in Tensorflow.

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