while the validating loss soars.

however, these fluctuations are more severe for the validating data, reflecting what was shown in the last five steps

Additionally, adding more convolutional layers had the opposite effect, increasing both the accuracy of the model and the training time

As you can see, the convolutions are trying to pull out the different features of the nine, notably the tail and the circle

The tiny and small models seemed to do fairly well, with the tiny model producing the smallest discrepancy

the model is very overfit and, therefore, won’t handle potential new data well

so most of the important features remain, but inimportant information is removed

the images are filtered so a distinct feature is highlighted

overfitting the model

helps speed

Mount Rushmore, but with my faces

CNN performed better.

I “see” my “dreams” coming true

why it was important to include a callback in the model

Secretariat With Rider

More specifically, the first row depicts the 7 stored at index 0, the second row depicts the 7 stored at index 17, and the 7 stored at index 26 of the testing images.

the convolutional neural network (CNN) predictions had a slightly higher accuracy value of approximately 0.99

Convolving filters are very useful for computer vision because they process and extract the important features from the image

Interestingly, this filter also produced wavy lines across the sky

As such, the outer edges of the image are not processed because there must be 8 directly adjacent pixels to the current pixel.

Art. Enough said.

the large and medium model became highly overfit

falls then rises and falls again.

I like how the hand came out through it. I’d probably given time filter the contrast of the image so that Earth would be broken up into pieces more like the arm.

but it’s evaluation binary cross entropy skyrocketing

More epochs won’t significantly improve performance. They might in fact reduce it’s performance on a test set by overfitting it.

I can definitively say while faster, the Deep Neural Network was not as effective at predicting on this dataset. The Conv2D and MaxPooling2D layers improved the model greatly.

This method speeds up model predictions, as there is less data to work on, without losing too much information

Each set of four dots is searched for the brightest dot, and that dots value is used as the new value

never able to comprehend but captivated by its beauty nonetheless.

In this case, comparing the 4 models demonstrates the concept: a larger model has more power to generalize more complex amounts of data, but overfits easily if the model is overqualified to generalize the data set.

Adding more convolutions would increase the time it took to train the data even more with minimal returns on the increase to accuracy since the accuracy is already at 99.4%

scipy librar

After Kandinsky Filter

Below, is my image for the Jump Start Data Science T-shirt competition, and also my assignment for Project 2!

The diagonal axis is just the distribution of that variable

The ImageDataGenerator() command is essentially reading the pictures from our source folder.

I think it’s safe to say that the convolutions improved the performance of my model.

Shirt Images

Chemical Orbitals Styled London

I am sorry that you know do

Reversing the Content and Style Images

As the number of layers increases in our models, there is a quicker and quicker drop off in the loss

With convolutional and pooling layers, it appears that more and more of the original image is reduced to these basic outlines of features. That is, there appears to be more information lost, but less noise.

no information appears to have been lost, even though the picture size has been halved from the original 1022x767 to 511x383

misc.face

This stylized image is supposed to be what our vision looks like after sitting at the computer for hours after our bedtime due to various issues with our coding

it seems like the model has been overfit at this point, as the values appear to be stagnant or even increasing

This provides a useful tool for investigating the co-relationship amongst the variables, as we can see how they interact independently from all of the other variables

the letters to take the color scheme of the microchip that happened to also be W&M colors

It was revealed that the tiny model avoids being overfit and the larger models are indeed overfit.

So, the model would work better if we decreased the epochs

because it is smaller, so it takes up less space and the image itself is maintainend so you don’t lose any important features

thinking about what is going on the background is that the pixels are being manipulated in a way that it uses nearby pixels to guess and change so that the result comes out different

This summer was very busy and I thought that choosing this image would remind me to relax and take a step back to experience nature and all of the beautiful things that surround me

I noticed that the tiny one performed the best

It seems that by adding the Conv2D and MaxPooling2D layers, the accuracy of the neural network improved.

Times of Passion

allows us to recognize whether we are overfitting or underfitting

but has maintained all the features of the original image

we could classify an image based on its features

My Design

it’s almost like a highlighter

The images the model trained and tested on are imperfect in comparison to the fashion mnist and numbers mnist datasets so the model is fairly inacturate. We can also tell that the model is overfit because it reached a training score of 100% while the validation score remained at a low ~75%.

additional convolutions may begin to overfit the model

My favorite image is the following

comparison graphs can confirm that a smaller model will not underfit/overfit the model in this case

skewed right

Thus, the model must be either under or overfitted

categorical labels are necessary

I do have questions concerning this method; rescaling an image that potentially does not have the same aspect ratio could distort the image and thus the accuracy

overfitting

Thus, although it was better at 64 filters, too many filters can overfit the data

Although the filter applied was meant to detect edges, our values may have overblown the exposure, thus wiping out a lot of the edges. This filter would probably not be very effective for edge detection.

we notice that convolutions are performing linear transformations on the image

In other words, we are filtering specific characteristics in order to look at the data in the image that is necessary

since the model represents the training data better

overfit

how

The 4 bedroom house is the best deal and the 3 bedroom house is the worse deal. In my model the predicted value was subtracted with the actual price and the lowest number (most negative tells me that that house had the best deal, which was the 4 bedroom house) and vice versa for the 3 bedroom house.

Traditional Programming: you have rules and data come in and answers that come out. It is manual and hardcoded input of rules. Machine Learning: you have answers and data aka labeling come in and rules that match one to the other come out. Algorithm is used so it is not manual.

where the training accuracy still continues to increase.

Produce the following plot for your randomly selected image from the test dataset

As Maroney explains, “if you show them all the shoes, then there’s no point. You’d have to show them some shoes, then let them train by identifying and picking out new things that they’ve never seen before.”

On the other hand, the worst deal is Moon (2 bd 250k) because the predicted price is two-thirds of the actual price, so the actual price would be “way too high” and thus “not a good deal”!

fit price

No, because the model takes steps towards the right answer in different sizes. The epochs (number of loops/iterations) is also limited to 500 so it will not always be the same as it doesn’t always run until 100% completion, but will get pretty close.

Simplified, it’s that traditional programming is rules + data –> answers, vs machine learning is answers + data –> rules.

increased

(60000, 28, 28) 60000 (10000, 28, 28)

The Hudgins house was the best deal based on bedrooms because my program says the 3 bedroom house should be worth 245k, the worst house is the moon house because the price of the house is 250k my model said it should be 123k.

guess

Machine Learning is taking answers and data and trying to find rules, while traditional programming is trying to take rules and data to get answers

I think it is safe to say from this exercise that the amount of rooms in a house cannot be the only determining factor for house price.

probabilities

The machine (watch) uses data and pre-supplied information (answers) in order to determine when the user is performing a certain task or activity (rules). In a traditional sense, the machine should have been given the rules in order to determine the answers but in a situation like this one, that order to operation does not make sense given how user specific the data is.

By training the model on more people, it is possible for the model to identify a wider range of individauls and more accuratley distinguish social distancing violations.

However, if the camera angle is closer to 90 defgrees, the results can be far more accurate, making the detecor a good choice

When the line peaks, this means that the accruacy is no longer increasing, and the model is beginning to become overfit

while the rest of the data is used to see how well the model can predict a value

It seems that the worst deal was the Church house (with 4 bedrooms costing $399,000).

Because of this, it is possible for the values to be different depending on how much error the loss deems from the guess.

This is different from traditional programming, in that with machine learning, the computer/program will be able to define the rules as the output, while traditional programming requires the user to enter the rules as an input.

This value could be an indication that we overpaid for our home.

For example, if we were to input customer demographics and transactions, and then have historical customer churning rates as an output. Using these two characteristics, the algorithm will then create the program and will give you predictions (in this case) based on the data you provided.

Based on my model, the house at Holly Point Rd. presents the best deal as you would be spending $134,365 less than what the model predicted. Meanwhile, the house in Church St. would present the worst deal as you would be paying $98,088 more than the model predicted.

These are different answers because the model is recompiled and relearning the data set and there was no random seed set.

Meanwhile, machine learning involves inputting the answers into a machine and having it figure out the rules for the programmer.

It’s an upward trend as people are getting more reliant on the use of technology.

What is the shape of the images training set (how many and the dimension of each)? 28x28 & 60,000 What is the length of the labels training set? 60,000 What is the shape of the images test set? 10,000

By knowing how accurate the model is, we know how accurate it may perform with new data

By making a neural net model, we can estimate what each house should be priced based on bedroom number. Looking at the output of the code, we see that 160 Holly Point Rd, a house with three bedrooms selling for $97,000, is the best deal. Based on the model, we see that 760 New Point Comfort Hwy, a five bedroom house selling for $577,200, is the worst deal.

This model could have been strengthened by using square footage instead of bedroom count in the input. Thus, it would account for other spaces and bathroom count.

Because it is a stochastic process, the answers will be very close but not often the same

probabilities

Traditional Programming: One inputs rules and data in order to derive answers Machine Learning: One inputs data and answers in order to derive rules

In this graph, the training accuracy increases with each epoch. Validation accuracy peaks again, indicating overfitting.

the model is training too well on the train data

overfitness

(wrote the code)

The optomizer and loss function are used when compiling the model and the ones used, “adam” and “sparse_categorical_crossentropy” are useful when classifying multiple categories.

The Hudgins house presents a great deal, because my neural network estimated that the price of a 3 bedroom house should be around $233,000, but the house is only priced at $97,000. One of the worst deals is the church house, because the network determined that a 4 bedroom house should be priced at around $300,000, but the house is isntead priced at $399,000.

probability

In traditional programming, the programmer will input data along with rules. From the combination of the two, the model will predict the answers. However, machine learning is a reorientation, as the programmer inputs data and answers, and the model instead figures out the rules. For example, it can figure out the relationship and other rules between variables.

overfitting

his wasn’t a question and, consequently, I’m not sure how to answer this, so here’s my code if that helps:

These probabilities add to 1

logits

According to the model (trained on the new house data), the Church St home is the most overvalued (and is therefore the worst deal) at 99k over model price (300k). The best buy would be Holly Point which is undervalued at 138k below model price (235k)

which is due to the stochastic or random nature of neural networks

Machine learning, however, takes answers and data as an input and the model creates (or, perhaps more appropriately, guesses) the rules.

There is not a large impact. The loss is 0.2775 and takes 45s to train. The accuracy is better by about .025 and takes about 15s longer compared to a single Dense layer of 512

There are 10 neurons to match the 10 expected outputs for the network. I get a InvalidArgumentError when I try training the network with 5 instead of 10 neurons in my last Dense laye

I get a ShapeError. This is because our data is currently 28x28 pixel images, and we cannot have a 2D network. We need to flatten the 2D array into a 784 1D array for the model to work

I know this because 2. The 10th element on the list is the biggest, and the ankle boot is labeled 9. It should be noted that the 10th element is actually the digit 9 which represents the ankle boot since the neurons are numbered 0-9 for a length of 10

The home that costs $97,000 with 3 bd and 1 ba is the best deal since it has the greatest difference from the predicted price using the model that was fit to the 6 homes. The home that costs $577,200 with 5 bd and 2 ba is the worst deal since it has the greatest difference from the predicted price using the model. I fit a model on the given home prices and then compared each home’s bedroom to the original given model of 50 + 50x where x is the number of bedrooms. Then the model predicted the price for each bedroom. Then this predicted price was subtracted from the original given model with the same number of bedrooms. The result with the highest and lowest price are the worst and best houses respectively

where the computer guesses and how accurate its first guess is

For machine learning, instead of the programmer writing the rules, the machine will look at both the input and the output and find the rules that govern them

I.D. 1) 60000 images 28 by 28 pixels 2) 60000 3) 10000 images 28 by 28 pixels 4) array output: [[9.6861429e-11 4.3787887e-07 9.9999952e-01 1.0604523e-09 1.2526731e-16 3.6759984e-10 2.3595672e-11 1.7706627e-14 8.6952684e-10 1.8218145e-17]]

I have attached a plot depicting the first test image alongside it’s probability distribution, below. Interestingly, the model was so confident in its prediction that the first test image depicted a 7, that the remaining probabilities don’t even appear on the graph.

Doing so helps keep future positive neuron outputs from being cancelled out by any previous negative neuron outputs

In other words, “relu” removes negative outputs

However, the model then can’t be tested for accuracy on the same training set because it already knows what those images are supposed to depict.

As a result, I determined that the Hudgins house has the best value, at a price of $97,000, because it costs approximately $137,567 less than houses with three bedrooms are predicted to cost. The Hudgins house is thus the most undervalued. On the other hand, the Church house has the worst value, at a price of $399,000, because it costs approximately $99,193 more than houses with four bedroom are predicted to cost. The Church house is thus the most overvalued.

Consequently, the machine may not predict the exact answer of 30 when the input is 7, 100% of the time.

probability

100X

In traditional programming, programmers use rules and data to produce answers. However, machine learning almost reverses this process, as the programmer must know what answer they’re looking to receive and provide the data necessary to reach this answer. Then, the machine/computer will generate the rules necessary to reach that answer. In short, traditional programming yields answers based on rules and data, while machine learning yields rules based on answers and data.

After a while, the validation accuracy actually starts to decrease, as the model is getting overfit

Loss is the ‘inaccuracy’ of the prediction, so minimizing loss increases the accuracy of the model

D. Using the mnist drawings dataset (the dataset with the hand written numbers with corresponding labels) answer the following questions. 60000, 28, 28 60000 10000, 28, 28 ```python import tensorflow as tf import numpy as np class Callback(tf.keras.callbacks.Callback): def on_epoch_end(self,epoch,logs={}): if(logs.get(‘accuracy’)>0.99): print(‘\nReached 99% accuracy so cancelling training!’) self.model.stop_training = True mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train/255.0, x_test/255.0 callbacks = Callback() model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(512,activation=tf.nn.relu), tf.keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’]) model.fit(x_train,y_train,epochs=10,callbacks=[callbacks]) probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()]) predictions = probability_model.predict(x_test) random_image = np.random.randint(0,len(x_test)) print(‘Random Image Number:’,random_image) print(predictions[random_image]) 5. ```python print(np.argmax(predictions[random_image])) ```python def plot_value_array(i, predictions_array, true_label): predictions_array, true_label = predictions_array, true_label[i] plt.grid(False) plt.xticks(range(10)) plt.yticks([]) thisplot = plt.bar(range(10), predictions_array, color=”#777777”) plt.ylim([0, 1]) predicted_label = np.argmax(predictions_array) thisplot[predicted_label].set_color(‘red’) thisplot[true_label].set_color(‘blue’) numbers = [‘0’,’1’,’2’,’3’,’4’,’5’,’6’,’7’,’8’,’9’] plot_value_array(1, predictions[random_image], y_test) _ = plt.xticks(range(10), numbers, rotation=45) plt.show() ```

If we simply did this with training data, the model has already seen this data, so we could run into the issue of the model simply memorizing the classification of the training data and not actually being able to classify

f we collected testing data but did not know its classification, we would have no way of telling how well the model actually works

The Hudgins house is a 3 bedroom house that costs about $100K, but the model predicts that it would cost $215K, more than double the price.

The two answers are not the same, but they are very close. This is because the model trains on the data by guessing and then using the loss function’s output with the optimizer to produce smaller and smaller losses. The prediction converges to the same value (of 22 in this case), so the difference is very minimal, but the output is always slightly different.

According to Maroney, the difference between traditional programming and machine learning is that traditional programming involves inputting rules and data for the computer to produce the answers as the output, but machine learning takes data and answers as the input and the computer tries to determine the rules based on the data and answers.

For example, mean squared error

cause errors

The data is split into two sets to reduce overfitting and to better verify its accuracy. The model is only trained off of the training data. It’s accuracy on new data can be tested using the test data, as it has never seen the test data. The test data stands in for what we would be trying to predict, but with answers so you can verify it.

Moon is the worst deal, with my model predicting that you’d pay $109,710 than you should. Hudgins was the best deal, with my model predicting that you’d pay $125,186 less than it should cost.

The answers are different because the neural network is retrained and fit on the data. If I had set a random seed the answers might have been the same

In traditional programming you use rules you right and data to get answers. Machine learning takes data and answers to make a set of rules for predicting on future data.

We also see the model becoming overfit in the accuracy graph

but we use sparse_cateforical-crossentropy here because it is suited to finding “wrongness” in computer vision cases

Based off of the model Maroney’s exercise outlines, only the house on Holly Point Rd (file name hudgins) is a good deal (3 bedrooms is predicted to mean the house is around 200k, while this house is selling at near 100k). The worst deal is the house on Church St (file name church), which sells at 399k, about 150k above its modeled price.

The episode introduces machine learning as answers and data generating rules/relationships instead of a programmer figuring out rules to compute answers. This means that activity that is hard to find a set of rules for as a programmer can be modeled by a network of “neurons” and then can generate predictions based on that network. He demonstrates this idea by setting + up a single neuron machine learner which is used to generate the function of a line from a list of values derived from said function. Having only a signle neuron presents a very + simple situation where we see only one rule that presumably will lead to a lot of rules being generated from a large set of neurons interconnected.

This graph displays the accuracy of the model in predicting the connotation of a movie review compared to the number of epochs the model cycled through. The positive trend is typical as the optimizer is adjusting the cost function with the returns from the loss function. There seems to be a platue at 4 epochs, meaning after 4 epochs we are gaining less returns in run time versus accruacy and may be overfitting the model.

The loss function gives penalties to the model predictions that are incorrect. These penalities are fed into the optimizer that adjusts the weights for each neuron based upon the size of the penality from the loss function.

He splits the data into training and testing because if we only trained the model on one set of data, it will get overfit. It allows for the verification of the model’s accuracy in identifyng images it has not seen before in training.

When subtracting the predicted cost from the posted cost, house 2 yields the largest negative value, meaning it is the largest difference between the estimated price and what you would actually pay. This is the best deal because the buyinng offer is the lowest compared to the predicted price. On the contrary, house 5, when the predicted price is subtracted from the buying price, yields the largest positive value. This means the asking price is over the amount that is predicted based upon other offers and therefore is the worst deal.

The best deal is house 2 ($97,000 for 3 bedrooms) and the worst deal is house 5 ($250,000 for 2 bedrooms) This is explained when comparing the prices offered to the prices predicted by the 1 layer 1 neuron nueral network that are posted below: 2 beds - 169.26517 3 beds - 234.5404 4 beds - 299.81564 5 beds - 365.09088

22.002247 & 21.999706 These answers are different because the algorithm answers in probabilities of the best answer.

Traditional programming had the inputs of the data and rules which gave an answer. Machine learning takes the data and answers as an input and outputs the rules needed.

I’ve noticed that a large gap seen in the literature, is that scientists are failing to recognize how a number of different covariates work in tandem with one another to spread the disease.

To accurately distribute resources and further understand how rates are changing, various geospatial data methods investigating the impact natural disasters have on nutrition rates were analyzed and compared, including: DHS data, spatial video, geographically weighted regression (GWR), and ordinary least squares regression models (OLS).

In 2010, a 7.0 magnitude earthquake struck Haiti and devastated millions. As an immediate effect of this natural disaster, child malnutrition became a relevant developmental issue in Haiti. The disaster substantially impacted sectoral conditions such as drinking water, sanitation, energy/fuel, food supply, healthcare, and clearing of debris by the disaster. Hence, these conditions note to have a disruption on the direct development of Haiti and stunted the process. When considering solutions, humanitarian aid narrowed in on improving these sectoral and household conditions to lessen the rate.

Nutrition is a quintessential sustainable development goal. Child malnutrition can be a cause of long-term effects such as inadequate dietary intake and diseases, as well as short term effects like natural disasters and political turmoil.

more often than not their institutions are given less credit than they deserve

Where Russia differs is localized regions within the country that experience different natures of hardship, thus a universal or average solution does not work.

analyzed and displayed geospatially as rasterized data

Columbia’s annual manufacturing survey which provides information such as sales, wages, employment, capital, input prices and output for companies with at least ten employees. They also used the Registry of Violence which provides direct information about internally displaced people, such as their original municipality, the date they left, new municipality, and socioeconomic status

household statistics to create counterfactual analysis and identify which attributes were effective in eliminating economic inequality

Gini index

OLS regression tests

counterfactual samples to make predictions on what factors such as tax rates, government revenue, and job creation had on the profit of these businesses

distance to the nearest roads and the average road density of the cities the businesses reside in

geospatial analysis of the General Enterprise Census and National Survey on Employment and the Informal Sector to map out the areas of informal and formal businesses and performed cross section testing to identify what types of areas were most profitable and in what industry

formal and informal businesses, with one being taxed and regulated by the Cameroon government while the latter is in a gray area which is not taxed nor regulated by the government

the informal economy is not a cause, but rather a consequence of existing poor conditions in LMIC’s such as Cameroon

rural-urban migration where those who work in informal sector in the rural side move to the urban side either temporarily or permanently in the search of a job that much higher pay than their current one (Todaro)

size of the informal economy is also an indirect indicator of the conditions of many regions

Cameroon’s large informal economy exploits the current rampant economic inequality that exists in Cameroon, causing unfair competition with formal businesses and discourages economic growth in rural regions, which is ultimately a result of the significant lack of resources in the rural regions such as access to high-quality infrastructure, job opportunities, and education.

Altogether, spider charts precisely display the characteristics of the twelve cities. Therefore, the analysis answers the author’s question on India’s urban growth types.

classifying urban types

object-oriented hierarchical approach works well on measuring changes in the urban extension

multitemporal remote sensing for analyzing rapid urban changes. Time-series of Landsat data

Taubenböck, H.

urban dynamics