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Navigation Introduction Overview The Walkthrough Walkthrough Quiz The Background Quick Fire Quiz 2 The Code Along Progress Summary Hover Here Gradient Descent The Practical Way In this chapter, we will explore the basics of the Gradient Descent algorithm and how to create it using Python. Overview 1.1 - What is Gradient Descent? Machine Learning is the autonomous process by which a machine can "learn" to accomplish a task rather than relying on a programmer to provide a step-by-step solution. This process is useful when solving a problem with no clear answer. Gradient Descent is one of many machine learning algorithms that can be used to optimise an AI model in this way. In this tutorial, we will explore the basics of gradient descent, and learn how to create it from scratch using the Python langauge. The Aim 2.1 - The Aim of Gradient Descent A critical component of optimising an AI model is establishing the error - by how much do the predictions from the AI model differ from the target values in our real-world training data? ​ The aim of gradient descent is to tune the model's parameters to reduce this error. Treating this as a minimisation problem, the goal is to find the combination of parameter values that will result in the lowest error our model can achieve. Each change in the model parameters will lead to a change in the model's accuracy. For a simple problem, such as the example above, we can visualise these results as an "error surface," otherwise referred to as a "cost surface." This netted shape represents the calculated error values for all of the individual combinations of the variables at play, in this case, the inputs named "Weight Value" and "Bias Value". At some point on this surface, there will be a region where the error value is at its lowest point, known as the "global minimum." Unlike other low points, referred to as "local minimums", the global minimum reveals the optimum parameter values for the model. Ultimately, finding this global minimum is the main goal of the gradient descent algorithm. 2.2 - The Approach In reality, searching every combination to find the global minimum is not an efficient use of time or computer resources. Additionally, most real-world problems require many more variables than the 2D and 3D examples on this page. This is important to remember, considering that most graphs can only represent up to axis three dimensions plus colour. However, using gradient descent, we can begin with the parameters holding a randomised combination of values and then iteratively "descend" towards the minimum point, therefore expecting to vastly reduce overhead. A common analogy for this process is to imagine the cost surface as a hill, where we can place a ball at a random position and let it roll down to the lowest point. By finding where the ball rests, it will indicate the location of the lowest point the ball reached. To accomplish this task, we will be taking the following approach: Initialise the model parameters to starting values Derive the gradient of the error for the current position in the search space Adjust the model parameters based on the gradient to reduce the error Repeat steps 2 and 3 until the training criteria have been satisfied ​ Examples of training criteria to indicate that training can stop include: The error score reaching a suitably low level The training period overrunning a maximum time limit (May need to run again) A high error score not changing over a significant period of time (May need to run again) The Walkthrough 3.1 - Establishing the Model Ultimately, an AI model is a complex set of functions produced to compute a solution to a given problem, such as detecting and replicating a pattern found in data. When improving our model, an error function needs to be added to scrutinise the model's performance against supplied training data and output the calculated error score. This combined AI model and error function can be represented as one large overall equation. The more optimised this equation, the more accurate the model's result. To demonstrate gradient descent more easily, we can substitute the complexity of this overall function with a simplistic quadratic equation. Therefore, if we find the minimum of our placeholder function, we have "learned" a solution for our imaginary model. This will enable us to experiment with gradient descent without the overhead of choosing a problem to solve and gathering example data for this problem. A quadratic equation gets its name from one or more of the input terms being raised to the power of 2, giving the line a curved "bowl" like shape. The equation y = x^2 + 2 was selected for three reasons. Firstly, the equation only has one variable to tune, simplifying the problem. Secondly, the x^2 produces a simple shape with a single global minimum. And lastly, the + 2 raises the line above the x-axis, allowing the line to be more easily seen at its lowest points. 3.2 - Initialise the Model Parameters ​ The overall function is represented by the following equation: ​ y = x^2 + 2 ​ The dependent variable "y" acts as the output we monitor, with the independent variable "x" representing the input we control. The other parameters of the function, such as the ^2 and +2, are locked and cannot be changed. The last parameters control the characteristics of how the function behaves. ​ The goal is to find the minimum of the function, or in other words, uncover what value of "x" provides the lowest result in "y". In later examples, this will translate to what values of the model parameters offer the most accurate results. ​ To improve the function, we first need a starting point. For this scenario, we don't need a complicated method of choosing a starting point, so we can select any point at random, such as x = -4. After this, we can slowly nudge the value of x so that the y output reaches the lowest point, as signified by the arrow. 3.3 - Derive the Gradient After selecting a starting point, we can begin to move towards the optimum point. In order to do this, we need to establish two important values for each variable we will adjust. How much does the value need to be changed? In what direction (+/-) does the value need to be changed? ​ These values can be calculated using the gradient at this point. A gradient represents a rate of change. For example, the gradient can be applied to a slope to measure steepness. Moving along a steep slope will cause a large change in height, whereas a slight slope will lead to a smaller change in height. ​ The value of the gradient reveals how drastic of a change is needed. For example, if the rate of change is high, then the model is far from the optimum point. However, if the gradient is low, then less change is needed. Therefore, a gradient close to or equal to zero would suggest that the model has reached optimum and no further changes are needed. ​ The direction of the gradient indicates the direction in which the variable needs to be altered. For example, a negative gradient (pointing down) will show that the value is too low and needs to be increased. However, a positive gradient (pointing up) reveals that the parameter value is too high and needs to decrease.​ In short, two important rules can be followed: The closer the gradient approaches zero, the smaller of a change is needed. The direction to adjust a parameter is opposite to the sign of the gradient. ​ It is also important to note that although the gradient indicates a "slope," it represents specifically a rate of change. The point is not to draw a line on a graph but to discover how quickly the value of one variable is changing with respect to another. 3.4 - Making a Change The next stage of gradient descent is to update the model parameters. This is thanks to one equation in particular, at the heart of this machine learning algorithm: ​ ​ New_Value = Old_Value +/- (Amount to Change) or New_Value = Old_Value - (Step_Size * Gradient) In essence, we get our parameter's new value by either increasing or decreasing the previous value by an appropriate amount to change. When updating model parameters, it is important to make changes in controlled amounts. Too large of a change can lead to "overshooting", making it impossible to get to the true optimum point. Alternatively, making too small of a change may be more accurate but will take too long. Therefore, we can introduce a "step size", an appropriately small number chosen to nudge the parameter by the right amount. This step size is multiplied by the gradient to further control the intensity of the change. For example, when the gradient is high, we can make a larger change, and when the gradient is closer to zero, we can make a much finer change. ​ The direction of the gradient is the opposite of the direction in which the parameter is to be adjusted. To overcome this, we can update the value by subtracting the amount to adjust it. The general characteristic of the algorithm is displayed in the image above. At the start, there are large distances between updated points as the algorithm quickly approaches the general region of the solution. Near the end, gradually smaller distances are visible when more precise changes are made. Once at the global minimum, the changes are so small that nothing seems to change, and the training can be stopped. ​ Overall, we can update the parameter by taking its current value and subtracting a controlled amount based on the gradient. The training is done over many iterations, gradually improving the model's accuracy. This loop can be stopped once the training criteria are met, such as when the error score is deemed low enough or when the gradient gets close to zero. Quick-Fire Quiz ​ Before we progress into the next section, let's test your knowledge so far with some quick-fire questions! 0/3 Gradient Low Points Step Size Q1. Using the image below, select the answer that best describes the following gradient. The gradient is positive The gradient is negative The arrow is pointing incorectly Q3. Oh no! The training has failed, and the algorithm cannot reach the optimum. Select the potential reason why. The step size is too small The step size is too big The equation is too complex Q2. A low point has been selected by a grey arrow. What term below best describes this point? The Optimum Global Minimum Local Minimum The Background 4.1 - What is the Gradient The gradient is a crucial component of the gradient descent algorithm, as the name suggests. Therefore, it is important to understand how exactly the gradient is calculated. The gradient, also known as the slope, represents a rate of change. As the gradient increases, the rate at which values change over time increases. Take the equation for a straight line: ​ y = mx + c or y = (gradient * x) + bias ​ On a typical straight-line graph, this is demonstrated by a greater gradient creating a steeper line, causing values to rise more rapidly. Alternatively, a lesser gradient would lead to a more shallow line tilt. The bias, or "c", controls the y-intercept, where the line cuts through the y-axis. The bias can be said to raise or lower a line, but as we are purely demonstrating gradient, the bias has been ignored for the examples below. The gradient is typically controlled by a multiplication to one or many input variables. For example, as shown in the image above, the changes to the slope of the line are proportional to the numbers being multiplied to the X term in the equation. For functions utilising multiple input values, each dimension can have an individual gradient applied. 4.2 - Finding the Gradient In many cases, the gradient may not be given. There are a variety of equations avaliable, that can calculate, or at least very closely estimate, the gradient. The simplest and most common method is the rise-over-run equation. The rise-over-run equation calculates the rate of change between two points. This is achieved by dividing the resultant change in the y-axis (Δy) by the measured change in the x-axis (Δx). Applied to a straight-line graph, any two points can be chosen , as the gradient of a straight line is constant . 4.3 - The Rise-Over-Run Approach The rise-over-run equation can look quite daunting at first, so let's work through the following example. ​ We have been provided with a mystery line, the output of an unknown straight-line equation with an unknown gradient and unknown bias. Through applying rise-over-run, we can make short work of achieving these values. ​ The process to take is as follows: Choose any two points (Something with whole number values would be easiest) Calculate the difference in y (y of point 2 - the y of point 1) Calculate the difference in x (x of point 2 - the x of point 1) Divide the change in y by the change in x In the example above, I have chosen two points that conveniently cross on axis lines. Point 1 = (2,2) Point 2 = (6,4) ​ This gives us the x and y values of: X1: 2, Y1: 2 X2: 6, Y2: 4 ​ 4.4 - Calculating the Gradient With two points selected, the next stage is to calculate the differences. The first step is to measure the difference in y values by subtracting the first point's y from the second point's y. ​ Δy = y2 - y1 This provides the result of: ​ Δy = 4 - 2 = 2 ​ The second measurement is the difference of the x values by subtracting the first point's x from the second point's x. ​ Δx = x2 - x1 This provides the result of: ​ Δx = 6 - 2 = 4 From these results, we can conclude that a difference of 4 on the x-axis will lead to a difference of 2 on the y-axis. Finally, dividing these differences gives us the overall rate of change, revealing the missing gradient value. We can conclude that the gradient of the mystery line is equal to 0.5. In other words, the change in y is half that of a change in x. The previous graph shows that the y-intercept, where the line crosses the y-axis, is at the value y = 1, revealing the bias (or "c") to be equal to 1. Putting this to the test, if we choose a random point on the graph, and subtract one from the y value at this point, we should see that the reduced y value is exactly half (0.5 times) that of the x value that produces it, verifying our results. 4.5 - Non-Linear Gradients The rise-over-run method is a great way to calculate the gradient. However, it does have some crucial drawbacks that affect its suitability for use within gradient descent. ​ For example, as stated previously, the gradient of a straight line is constant. That means that anywhere on the line, or for any two points we choose, the gradient will be the same. However, as displayed by the image below, the gradient is different for each point we examine for our scenario's non-linear equation. This effectively makes the rise-over-run incompatible with our specific use case. Bolstered by the fact that any scenario we cover through these tutorials will result in a non-linear equation to minimise, we need to investigate an alternative. 4.6 - Introducing the Derivative Rather than generalising a gradient across the entire function's output, we need to adopt a method of deriving the gradient for a specific point we need to examine. ​ As gradient descent involves improving a given model, we can use the characteristics of the error function and the model's known equation as a foundation to formulate a way of calculating the gradient. Though the solution may not be known, the formula for the model itself should be. ​ The common way of achieving this is by calculating the derivative of the equation - A new equation that will provide the gradient for any value we plug in. ​ The derivative has a complex background concept, so we will only cover what we need to know. However, the idea is primarily based on the rise-over-run method, but the gradient is calculated using "two points" with an almost infinitely small distance between them. This effectively calculates the gradient at a single point, which is exactly what we need. Fortunately, there is a range of popular mathematical "rules" that allow us to simplify this process by bypassing most of this maths. 4.7 - Introducing the Derivative The derivative is used to measure an instantaneous rate of change, such as the gradient at a specific point. It can also be focused on a specific input variable, as we will demonstrate in later tutorials. ​ The following equation calculates the derivative of the target equation with respect to the input variable "x". In other words, this equation asks the question: ​ If I make a small change in x, how much will this change y? ​ In the case of derivatives, the symbol signifying "a change in ..." is the letter "d". 4.8 - Introducing the Power Rule The derivative can be calculated by utilising a selection of tools called the Derivative Rules. These rules act as shortcuts to simplify the mathematics in particular scenarios. Take our quadratic equation: ​ y = x^2 + 2 ​ The rule we will be using in this scenario is the power rule. The power rule is a shortcut specifically designed to differentiate x^n, where n can represent any number the x term is raised to the power off. ​ The process is as follows: Select an x term in the form x^n Next, take that power of n, and multiply it to the term’s coefficient (the number before x). Lastly, we reduce the original power by 1. After these steps, repeat for any remaining terms in the equation in that form. ​ This will produce the new derivative equation that can calculate the rate of change at any given point for our non-linear equation. 4.9 - Additional Useful Rules Many rules can be combined to compute or simplify the derivative. Below is a range of other must-knows to add to your derivation tool kit. We now have an established toolset that can be applied to our original equation to get our full derivative with respect to x. 4.10 - Using the Power Rule Below is the start-to-finish workflow for finding the derivative of our quadratic equation, concluding the background section of this tutorial. In other words, the gradient at a specific point would be twice that of the x value at that point. 4.11 - Demonstrating the Derivative Now that we have the derivative, we can plot this equation as an additional function against the original line. This will allow us to perform some visual tests to ensure the gradient is working as we expect. Selecting some points on the grid lines allows us to read the values on the graph more accurately without printing the values in a table. From these preliminary tests, it can be seen that the gradient is, in fact, double the x value and that the gradient line looks even across and proportional to the example line. ​ More importantly, the gradient line is aligned with the centre of the example line, and we see a negative gradient on the left-hand side and a positive gradient on the right-hand side of the centre. Moreover, the further from the middle the x value is, the greater the rate of change. The characteristics of the gradient match that of the example line, which suggests we have successfully found the derivative and captured the essence of the example line. Quick Fire Quiz #2 ​ Before we progress into the next section, let's test your knowledge so far with some quick-fire questions! 0/3 Power Rule Mystery Rule Complete the Problem Q1. A mistake was made whilst calculating the derivative. Which line did the error occur? Error on Step 2 Error on Step 3 Error on Step 2 and Step 3 Q2. What mathematic rule has been utilised in the image below. The Zero Exponent Rule The Constant Rule The Product Rule Q3. Select the option that correctly completes the answer. 1 x 0 The Code Along 5.1 - Setting up the Environment In this tutorial section, we will progress through a code-along walkthrough of the gradient descent process. ​ To get started, we need to initiate a suitable programming environment to begin coding. I have selected Jupyter Notebook, a free-to-use web-based environment you can install and run locally on your computer. In addition to being free, I have chosen this tool because it permits programs to be created using "cells", small segments of live code that can be run and altered independently, perfect for experimenting with making changes to a larger program. ​ With a new project made, the next step is to import libraries. Libraries are a selection of pre-made programs and functions that we can include in our programs to make our job easier. The libraries I have chosen are: Matplotlib - A free visualisation tool that allows us to create graphs effortlessly. NumPy - A free mathematics library that contains many optimised mathematics tools. Python 3 # Import Essential Libraries # -------------------------- # Import graph library from matplotlib import pyplot as plt # Import mathematics library import numpy as np Libraries can be imported as a broader package or further specified to include specific tools. A tool imported into a program can also be provided with an alias using the "as" keyword. An alias acts as a nickname that you can use in your program. For example, if I want to use the "pyplot" graphing tool within the "matplotlib" package, I can refer to it using the alias "plt" to make my code shorter and easier to read. 5.2 - Declaring the Function The first stage of the process is to establish the model we intend to optimise. We can add the function we intend to use by declaring it as a function. A function has two main sections: the function header and the function body. The function header defines the function's name, along with any inputs we need to provide. The function body is where we place the actual behaviour of our function, in this case, returning the result of a calculation. Python 3 # An example function to optimise def example_function(x_in): # Represents the equation: y = x^2 + 2 return (x_in ** 2) + 2 The quadratic equation we have shown before only takes in one input, the x value, which has been provided as an input parameter named "x_in." The "x_in" variable is known as a local variable, a variable only available within our function, similar to the functions having its own copy of the value we give it. This way, the function is free to work with this variable without affecting any other code outside the function's scope. ​ The equation is then entered in the function body as a return statement, providing us with the answer once it has been calculated. It is worth noting that raising a value to a power is accomplished using double asterisks. 5.3 - Testing the Function A simple 2D function such as this can be tested by plotting it on a graph. This will allow us to visually check that the function is performing the way we expect it to before we attempt to optimise it. Though not a required step, catching issues early will limit confusion if problems occur later. ​ To test the function, we will need to do four things: Create input values for the x-axis Run the function to generate values for the y-axis To plot the function to a graph Display the graph Python 3 # Create x-axis values x = np.linspace(-5, 5, 20) # Generate the y-values by calling the function function_test = example_function(x) # Plot the test line plt.plot(x, function_test, c='b') # Label the line on the graph plt.legend(["y = x^2 + 2"]) # Show the graph plt.show() The chosen equation is the quadratic we have used previously. The equation only takes in one input, the x value, which has been provided as a input paramter names "x_in". The "x_in" variable is known as a local variable, a variable only avaliable within our function, simular to the functions having its own copy of the value we give it, thats its free to work with without effecting any other code ourseide the functions scope. ​ The equation is then entered in the function body, as a return statement to provide us witht he answer once it has been calculated. It is worth noting that raising a value to a power is accomplished using double asterisks. The Derivative - Lab 1 - What are potential dividers? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Calculate Progress Summery 1 - What are potential dividers? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Quiz Simple Medium Hard Quiz 1 No No No Quiz 2 No No No Quiz 3 No No No Quiz 1 Quiz 2 Quiz 3 Bonus Calculate

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Roo mba 521 Batte ry Error 3 Repair In this project, the dreaded “Battery Error 3” charging error was tackled on an old Roomba 521 vacuum robot. This error can be found in older models of Roomba robots and is often referred to as the “Red Ring of Death” for robot vacuums. I'm a Service Name Use this area to describe one of your services. You can change the title to the service you provide and use this text area to describe your service. I'm a Service Name Use this area to describe one of your services. You can change the title to the service you provide and use this text area to describe your service. I'm a Service Name Use this area to describe one of your services. You can change the title to the service you provide and use this text area to describe your service. In this project, the dreaded “Battery Error 3” charging error was tackled on an old Roomba 521 vacuum robot. This error can be found in older models of Roomba robots and is often referred to as the “Red Ring of Death” for robot vacuums. Symptoms The vocal message “Charging Error 3” when attempting to charge the robot through the docking station or by direct connection. A red ring appears when attempting to charge the device. The battery does not recharge regardless of how long plugged in. The battery re-learn feature cancels, reporting the error. Quick Fix’s There are many reasons why an error may occur when charging your robot, from a bad battery, or a loose connection, all the way to a bad motherboard. Below are some initial tips to attempt to remedy the issue. Wipe clean the connections on the charging dock Plug the charger directly into the robot Clean the connections to the battery itself Try another battery (if one is spare) Try a battery reset (Hold down “Dock” and “Spot” buttons for 10-20 seconds and let go - the robot should make a tone) which will let the robot re-learn the battery's charging and running characteristics. The Investiga tion 1 - Overview To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. More Coming Soon

Introduction Roo mba 521 Batte ry Error 3 Repair In this project, the dreaded “Battery Error 3” charging error was tackled on an old Roomba 521 vacuum robot. This error can be found in older models of Roomba robots and is often referred to as the “Red Ring of Death” for robot vacuums. Page Chapters Click on the images to redirect to the corresponding chapter. The Inspection Initial inspection checks. Top-Case Teardown Step-by-step case removal. Motherboard Removal Removing the main board. Under The Microscope A closer look at the damage. The Repair Performing the repair. Putting It To The Test Testing the repairs. In this project, the dreaded “Battery Error 3” charging error was tackled on an old Roomba 521 vacuum robot. This error can be found in older models of Roomba robots and is often referred to as the “Red Ring of Death” for robot vacuums. Symptoms The vocal message “Charging Error 3” when attempting to charge the robot through the docking station or by direct connection. A red ring appears when attempting to charge the device. The battery does not recharge regardless of how long plugged in. The battery re-learn feature cancels, reporting the error. Quick Fix’s There are many reasons why an error may occur when charging your robot, from a bad battery, or a loose connection, all the way to a bad motherboard. Below are some initial tips to attempt to remedy the issue. Wipe clean the connections on the charging dock Plug the charger directly into the robot Clean the connections to the battery itself Try another battery (if one is spare) Try a battery reset (Hold down “Dock” and “Spot” buttons for 10-20 seconds and let go - the robot should make a tone) which will let the robot re-learn the battery's charging and running characteristics. The Investigation The Investigation 1 - Ov er v i ew To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. This robot is a standard Roomba 521 model designed for home use, compatible with most floor types, including tiles, carpet and hardwood. To start the repair, we need to turn the robot over, exposing the bottom side of the robot in order to access the case screws. Taking a closer look, the exact model number can be revealed. 2. Backplate Teardown The backplate contains the battery and battery connections, which are the first point of internal inspection for this error. The first step is to remove the side brush, as it will prevent the separation of the backplate. This brush rotates in normal use allowing for debris to be collected within reach of the main vacuum rollers beneath. This component can be undone by removing the single screw in the centre. To keep things safe, I often refit the screw into the motor so that it will not become lost. With that task completed, the highlighted screws can now be undone. These screws are typically captive screws , which do not fully remove from the outside cover when loose. This is beneficial for often removed access panels, as it reduces the risk of a vital case screw becoming lost. Once undone, the backplate can be lifted off the body to expose the battery compartment and other components. 3. Battery Inspection As this is an error related to charging, the next step is to investigate the connections to the battery, which for this fault, may have corrosion. To remove the battery, simply pull on the two green tabs to lift it evenly from the compartment. With the battery out, the area surrounding the connections can be examined for any potential damage. ​ It can be seen that the left topmost terminal is exhibiting signs of corrosion (green furr), which may very likely be the cause of such an issue. Below is an image taking a closer look. Examining the battery itself, there is no sign of leakage or corrosion. With a fault such as this, it is typically due to the motherboard being exposed to moister such as a spilt drink on the robot, damp storage conditions or cleaning with too wet of a cloth. Corrosion can cause damaged, shorted, or broken connections which will prevent the robot from accurately measuring the charging of the battery, leading the robot to shut down the charging circuit to prevent further damage. This means to correct the issue properly, we will have to closely inspect the motherboard, remove all the corrosion, and repair any damage present. Top-Case Teardown Top-Case Teardown 4. Top-C ase Teardown The motherboard is on the top side of the robot, under the face plate. But first, we have to remove the body panels that restrict access to the screws and the components that block the removal of these panels. The first component to remove is the front bumper plastic. A thin plastic strip helps retain the outer bumper shell to the robot body. To remove the strip, un-do the following highlighted screws. BEWARE! These screws are not captive and will have to be stored safely (such as in a baggie or a part sorter compartment). Tip: Make sure to keep note of what screws went where as you progress through the teardown, as not all screws are guaranteed to be of the same dimensions. Additionally, recording what sizes the screws are will help you find replacements later on if some were already missing. Below is an image of the screw size removed at this step. For this model of robot, a quantity of 10 screws is expected. With the screws undone, the plastic strip should easily lift away. Carefully turning the robot on the other side, the bumper cover should slide off upwards. BEWARE! The top IR sensor for docking and virtual wall detection will still be connected to the motherboard by a thin cable. Take care when removing the bumper, not to damage the cable or connector, as the cable plug may not be easily accessible at this time to unplug. The back bumper is far easier to remove, with only needing to press the button at the rear. This will allow the wastebasket, with an internal suction fan, to effortlessly slide out. The last step before removing the top case cover is taking off the face plate. The face plate is a replaceable cosmetic panel that shields various access ports and case screws. This facia is held in place by clips. BEWARE! These clips can become brittle with age, so take great care when prying the panel loose. Cold plastic clips can also be more likely to break. Robots previously stored in a cold garage, left to match the temperature of a normally warmed room, may be less likely to result in broken housing clips. To release the clips, gently pull the plastic around each clip one at a time, unclipping them from the body. Repeat this process around the face plate until the place becomes free. On this robot, there is no indented tab that can be pushed to release the clips, as common on some other types of devices. With the face plate detached, the remainder of the case screws are revealed, along with the communication port on the right-hand side. The next step is to remove the following highlighted screws. These are the first ring of screws that hold the top section of the case to the bottom section. Below is an image of the screws removed from this section, of which 6 should be expected. Next, the inner ring of screws need to be undone. Below is an image of the screws removed from this section, of which 4 should be expected. Below this ring is an additional single screw of a different size, as highlighted below. The following image shows the size of the sole screw expected in this step The final screws of this stage are the two below the handle. These screws do not pass through to the body underneath and are not required to be removed. However, they are shown below for future reference. The following image shows the size of these two screws. With all the mentioned screws extracted, the top cover should now be withdrawable from the robot's base, exposing the main circuit board (motherboard) below. The wastebasket can be re-fit if desired, as it may help keep the robot level when performing repairs. Motherboard Removal Motherboard Removal 5. Motherboard removal BEWARE! With the motherboard now exposed, it may be at risk of Electro Static Discharge damage. Wearing an ESD strap, as shown below, is recommended, so static energy from your body can be dissipated to ground and not damage your robot. With the top case detached, the plug for the bumper sensor is now accessible. Gently pull on the connector (ideally not on the wires themselves) to disconnect the plug from the socket. Tip: The placement of sockets and the colours of wires can differentiate between different models of devices. It is recommended to take pictures of each plug before disconnecting to ensure that the correct sockets are reconnected later. Additionally, if a wire became loose on a plug, it may help refit that pin in the correct location later. With the bumper fully separated, the front bumper sensors can now be seen. Tip: If the robot's bumper has lost its bounce, or the robot appears to occasionally “bump” into objects that do not exist, slightly bending the large metal tabs shown above forwards may help. These tabs act as springs on this version of the bumper and can become slightly compressed over time from continuous bumping. Pulling the spring tabs slightly further out will put increased pressure on the bumper plastic, allowing the bumper to be properly extended and not misread. Tip: Cleaning the IR LED sensors on the inner bumper will aid in properly detecting objects. The front IR sensors are used to detect upcoming objects so the robot can slow down before hitting them. If these sensors are covered in dust, they may not properly work in operation and cause a symptom of the robot heavily hitting objects. The IR sensors below can also be cleaned to ensure that “cliffs” or other large drops can be properly detected. A plastic sheet may be present to protect the top of the motherboard. To remove this sheet, undo the screw internal to the small plastic pillar, as shown below. Below shows the length of this pillar and the corresponding screw. Now you should be left with a fully revealed motherboard. Before the motherboard can be withdrawn, a few remaining steps need to be followed. First, unplug the robot's speaker cable. This is the speaker that produces the speech and tones. Secondly, remove the charging cable (left) and suction fan cable (right), as shown below. Next, there are 5 retaining screws for the motherboard to be undone, as highlighted below. The size of the screw for this step is shown below. The motherboard should now be loose and can be lifted from the body. BEWARE! The cables below are still connected, so take care not to damage the wires. Additionally, the drive motors have inserted sockets on either side, so lift gently and evenly up to not break these connections. The last step is to unplug the remainder of the plugs from the underside of the motherboard. These plugs are primarily for sensors. Connections to the drive motors should be as small PCB boards inserted into the motherboard sockets, and the connection to the side brush may also be springs, similar to the battery connection. Additionally, if any of the motors appear to work intermittently, carefully wipe clean the connectors to ensure no dirt is preventing a reliable connection. Cautiously unplug each cable one at a time. On this model, each plug should be of a different size, but taking images of each and their corresponding wire colours will prevent any confusion when reconnecting later. With this done, the motherboard should be removed, leaving only another plastic sheet and a metal tray underneath. More Coming Soon Page Chapters Introduction Motherboard Removal Top-Case Teardown The Investigation

Resistors for Beginners Potential Dividers In this chapter, we will be exploring the concept of potential dividers, and how to calculate and utilize them in everyday circuits. Potential Divider Basics 1 - What are potential dividers? A potential divider is a circuit that utilises resistors to reduce a supplied voltage. The output voltage of the circuit is a fraction of the supplied voltage, determined by the values of the resistors in the circuit. In the circuit above, two resistors are used to output a potential difference, based on the ratio of these resistors. 2 - How Are They Used? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. 3 - How D o They Work? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Potential Divider - Lab 1 - What are potential dividers? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Calculate Potential Divider - Summary 1 - What are potential dividers? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Button Chapter Quiz ​ Using your newly gained wisdom of potential dividers, attempt to solve the provided quiz question bellow. Answer = +2v Answer = +2.5V Answer = +5V

Gradient Decent The Practical Way In this chapter, we will be exploring the concept of potential dividers, and how to calculate and utilize them in everyday circuits. Overview 1.1 - What is Gradient Decent? Machine Learning is the autonomous process by which a machine can improve its accuracy when attempting to accomplish a task. The aim is for the device to "learn" for itself rather than a programmer providing a step-by-step solution. This is also useful when solving a problem with no clear answer. ​ For example, how would you teach a robot to tell the difference between pictures of cats and dogs? Would you say that dogs have long ears and cats have round faces? And how reliable will this solution be when new images are shown? Unfortunately, clear, simple rules do not always work. Yet, even with the most simplistic depictions shown above, we can use our human ability to generalise solutions to solve the problem with a glance. This leads us to a big problem: What may be easy for us can be exceptionally difficult for a robot to solve and even more impossible to explain. ​ A potential method would be to analyse thousands of examples from both categories, developing rules and uncovering patterns on a more microscopic level, to formulate a generalised solution to this problem. Then, given a model with a suitable architecture, the more and slightly varying the images we provide the system, the more accurate its prediction should become. ​ Gradient Descent is one of many machine learning algorithms used to iteratively optimise an AI model. It involves making incremental changes to improve the model's accuracy, calculated based on its error. 1.2 - The Problem - Linear Regression One of the simplest problems we can apply gradient descent is Linear Regression. The line blue depicts a predicted relationship between two variables, for which the red dots represent single data samples from a given dataset. The goal is to adjust the properties of the line so that it eventually "fits" the trend of the sample data, resulting in a line of best fit. ​ To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. 1.3 - The Approach To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. 2 - Gradient Test To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. 3 - How D o They Work? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Potential Divider - Lab 1 - What are potential dividers? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Calculate Potential Divider - Summary 1 - What are potential dividers? To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. Button Chapter Quiz ​ Using your newly gained wisdom of potential dividers, attempt to solve the provided quiz question bellow. Answer = +2v Answer = +2.5V Answer = +5V

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ERTE Modular Educational Robot This project aims to create a modular educational robotics kit that teaches robotics through practice and can be expanded on skill level. Be low is the story of developing the initial prototype, ERTE-0. I'm a Service Name Use this area to describe one of your services. You can change the title to the service you provide and use this text area to describe your service. I'm a Service Name Use this area to describe one of your services. You can change the title to the service you provide and use this text area to describe your service. I'm a Service Name Use this area to describe one of your services. You can change the title to the service you provide and use this text area to describe your service. This project aims to create a modular educational robotics kit that teaches robotics through practice and can be expanded on skill level. Be low is the story of my work developing the initial prototype, ERTE-0. Introduction Robotics is a field that I have always been obsessed with. However, I have found it hard to get into. In my experience, affordable robotics kits, such as of the solder-yourself variety, tend to be fairly simplistic and restrictive. For example, If you want a line-follow robot, then you make a robot specific to that function, just as you would buy a particular kit if you wanted a robot to follow the light or navigate a room. Considering that these kits tend to be of low-quality build (lack of wheels, sticky tape instead of brackets and screws, etc....), these robots are only suitable for educational / gimmick use only. The aim of this project is to create an easy-to-follow educational kit, where detailed instructions enable you to start with a basic robot and expand the system with more complicated functionality as you learn more advanced concepts. To start, i believe it be essential to produce a suitable p[latofrm, with wheels, battery case, brackets, and designed to be taken apart many times and expanded. With this suitable platform, a proper foundation for a modular robot will be offered. Goals Below are the main goals listed: Comprised of generic off-the-shelf components Custom PCB's with simple layouts, able to be replicated with prototyping boards PCB's to be well labeled, including component vales, with easily distinguishable traces Each distinct area (Sensors, Control, Motor Driving...) is to be separate and swappable modules Expansion considerations built-in Cross module compatibility A rigid body designed to be repeatedly taken apart to enable modifications Fully detailed instructions with the design well explained One robot - various possible functions and module combinations The Investiga tion 1 - Overview To initiate the repair, we first need to strip down the robot to expose the error, as the basic checks show the fault lies within the robot. Below is a picture of the top face of the robot in question. More Coming Soon