#### Cognisant

##### Prolific Member

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- Joined
- Dec 12, 2009

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- If you know the diameter: The radius is half of the diameter, so you can simply divide the diameter by 2. That is, if the diameter is "d", the radius is "r", and the formula is:

For example, if the diameter of a circle is 10 units, the radius would be 5 units.

- If you know the circumference: The circumference is the distance around the circle. The formula to calculate the circumference is:

where "π" is pi, which is approximately 3.14, and "r" is the radius. To find the radius, you can rearrange the formula to solve for "r":

r = C/(2π)

For example, if the circumference of a circle is 20 units, the radius would be:

r = 20/(2π) = 3.18 units (rounded to two decimal places)

So, depending on the information you have about the circle, you can use one of the above formulas to calculate the radius.

- Open the Marlin firmware configuration files in your preferred code editor.
- Search for the "configuration.h" file and open it.
- Find the section for X-axis endstop settings. This section should have the following lines of code:

#define X_MAX_ENDSTOP_INVERTING true // set to true to invert the logic of the endstop. #define X_MAX_ENDSTOP_PIN 22 // specify the pin for the X-axis max endstop. - If the X-axis endstop is not currently defined, you may need to uncomment the relevant lines by removing the "//" at the start of each line.
- Change the value of the X_MAX_ENDSTOP_PIN to the pin number that you have connected the endstop to.
- If your endstop logic is reversed (triggered when not pressed), change the value of X_MAX_ENDSTOP_INVERTING to "false".
- Save the configuration.h file and recompile the firmware.
- Upload the firmware to your 3D printer.

Note: It's important to make sure that your endstop is properly wired and connected to the correct pin before updating the firmware.

There is no one universally accepted solution to Zeno's paradox, as it remains a subject of ongoing debate among philosophers and mathematicians. However, there are a few common ways in which the paradox has been addressed:

- One way to address the paradox is by considering the idea of limits. According to this approach, the infinite series of steps that would be required to complete a motion is simply a mathematical concept that approaches a limit but never reaches it. In other words, the motion is never actually completed, but it gets arbitrarily close to the destination.
- Another way to address the paradox is to argue that the paradox is based on a misunderstanding of the nature of time and motion. For example, some philosophers have argued that motion is not a series of steps, but a continuous process that cannot be broken down into smaller parts.
- Some philosophers have suggested that the paradox can be resolved by making a distinction between potential infinity and actual infinity. According to this approach, the infinite series of steps required to complete a motion is only a potential infinity, which means that it has the potential to continue indefinitely, but it never actually does so.

When a neural network is presented with an observation, it processes the input data through a series of interconnected layers of artificial neurons, each of which performs a specific computation on the input data. These computations involve multiplying the input data by a set of weights and biases that are initially random, and then passing the result through an activation function that introduces non-linearity into the computation.

As the input data flows through the neural network, the weights and biases are adjusted through a process called backpropagation, which involves computing the error between the network's output and the desired output, and then adjusting the weights and biases to minimize this error. This process is repeated many times until the network is able to accurately classify or predict the input data.

During the training process, the neural network learns to recognize and abstract features from the input data that are relevant to the task at hand. These features can be thought of as higher-level representations of the input data that capture important patterns and relationships in the data. These abstractions are learned by the network through a process of feature extraction and dimensionality reduction, where the network learns to identify the most salient features in the input data and represent them in a lower-dimensional space.

Once the neural network has learned these abstractions, it can use them to make predictions or classifications on new, unseen data. This is known as the inference phase, where the network takes in new observations and maps them onto the abstract features it has learned during the training phase, in order to make predictions or classifications about the new data.

In summary, a neural network turns an observation into an abstraction by learning to recognize and abstract relevant features from the input data through a process of feature extraction and dimensionality reduction, and then using these abstractions to make predictions or classifications on new, unseen data.

This is a great way to learn things.