fix: change images structure

one_exercise_per_file/week01/day03/ex01/audit/readme.md

@@ -2,4 +2,4 @@
 
 ![alt text][logo]
 
-[logo]: ../images/w1day03_ex1_plot1.png "Bar plot ex1"
+[logo]: ../w1day03_ex1_plot1.png "Bar plot ex1"

one_exercise_per_file/week01/day03/ex01/readme.md

@@ -19,7 +19,7 @@ Here is the data we will be using:
 
 ![alt text][logo]
 
-[logo]: images/w1day03_ex1_plot1.png "Bar plot ex1"
+[logo]: ./w1day03_ex1_plot1.png "Bar plot ex1"
 
 The plot has to contain:
 

one_exercise_per_file/week01/day03/ex02/audit/readme.md

@@ -3,4 +3,4 @@ You should also observe that the older people are, the the more children they ha
 
 ![alt text][logo_ex2]
 
-[logo_ex2]: ../images/w1day03_ex2_plot1.png "Scatter plot ex2"
+[logo_ex2]: ../w1day03_ex2_plot1.png "Scatter plot ex2"

one_exercise_per_file/week01/day03/ex02/readme.md

@@ -17,7 +17,7 @@ The goal of this exercise is to learn to create plots with use Pandas. Panda's `
 
 ![alt text][logo_ex2]
 
-[logo_ex2]: images/w1day03_ex2_plot1.png "Scatter plot ex2"
+[logo_ex2]: ./w1day03_ex2_plot1.png "Scatter plot ex2"
 
 The plot has to contain:
 

one_exercise_per_file/week01/day03/ex03/audit/readme.md

@@ -9,4 +9,4 @@
 
 ![alt text][logo_ex3]
 
-[logo_ex3]: ../images/w1day03_ex3_plot1.png "Scatter plot ex3"
+[logo_ex3]: ../w1day03_ex3_plot1.png "Scatter plot ex3"

one_exercise_per_file/week01/day03/ex03/readme.md

@@ -6,7 +6,7 @@ The goal of this plot is to learn to use Matplotlib to plot data. As you know, M
 
 ![alt text][logo_ex3]
 
-[logo_ex3]: images/w1day03_ex3_plot1.png "Scatter plot ex3"
+[logo_ex3]: ./w1day03_ex3_plot1.png "Scatter plot ex3"
 
 The plot has to contain:
 

one_exercise_per_file/week01/day03/ex04/audit/readme.md

@@ -10,6 +10,6 @@ The plot has to contain:
 
 ![alt text][logo_ex4]
 
-[logo_ex4]: ../images/w1day03_ex4_plot1.png "Twin axis ex4"
+[logo_ex4]: ../w1day03_ex4_plot1.png "Twin axis ex4"
 
 https://matplotlib.org/gallery/api/two_scales.html

one_exercise_per_file/week01/day03/ex04/readme.md

@@ -14,7 +14,7 @@ x_axis = [0.0, 1.0, 2.0, 3.0, 4.0]
 
 ![alt text][logo_ex4]
 
-[logo_ex4]: images/w1day03_ex4_plot1.png "Twin axis plot ex4"
+[logo_ex4]: ./w1day03_ex4_plot1.png "Twin axis plot ex4"
 
 The plot has to contain:
 

one_exercise_per_file/week01/day03/ex05/audit/readme.md

@@ -11,6 +11,6 @@ The plot has to contain:
 
 ![alt text][logo_ex5]
 
-[logo_ex5]: ../images/w1day03_ex5_plot1.png "Subplots ex5"
+[logo_ex5]: ../w1day03_ex5_plot1.png "Subplots ex5"
 
 Check that the plot has been created with a for loop.

one_exercise_per_file/week01/day03/ex05/readme.md

@@ -6,7 +6,7 @@ The goal of this exercise is to learn to use Matplotlib to create subplots.
 
 ![alt text][logo_ex5]
 
-[logo_ex5]: images/w1day03_ex5_plot1.png "Subplots ex5"
+[logo_ex5]: ./w1day03_ex5_plot1.png "Subplots ex5"
 
 The plot has to contain:
 

one_exercise_per_file/week01/day03/ex06/audit/readme.md

@@ -8,7 +8,7 @@ The plot has to contain:
 
 ![alt text][logo_ex6]
 
-[logo_ex6]: ../images/w1day03_ex6_plot1.png "Time series ex6"
+[logo_ex6]: ../w1day03_ex6_plot1.png "Time series ex6"
 
 2.This question is validated if the plot is in the image is reproduced using `plotly.graph_objects` given those criteria:
 
@@ -20,4 +20,4 @@ The plot has to contain:
 
 ![alt text][logo_ex6]
 
-[logo_ex6]: ../images/w1day03_ex6_plot1.png "Time series ex6"
+[logo_ex6]: ../w1day03_ex6_plot1.png "Time series ex6"

one_exercise_per_file/week01/day03/ex06/readme.md

@@ -21,7 +21,7 @@ df = pd.DataFrame(zip(dates, price),
 
 ![alt text][logo_ex6]
 
-[logo_ex6]: images/w1day03_ex6_plot1.png "Time series ex6"
+[logo_ex6]: ./w1day03_ex6_plot1.png "Time series ex6"
 
 The plot has to contain:
 

one_exercise_per_file/week01/day03/ex07/audit/readme.md

@@ -7,7 +7,7 @@ The plot has to contain:
 
 ![alt text][logo_ex7]
 
-[logo_ex7]: ../images/w1day03_ex7_plot1.png "Box plot ex7"
+[logo_ex7]: ../w1day03_ex7_plot1.png "Box plot ex7"
 
 ```python
 import plotly.graph_objects as go

one_exercise_per_file/week01/day03/ex07/readme.md

@@ -14,7 +14,7 @@ y2 = np.random.randn(50) + 2
 
 ![alt text][logo_ex7]
 
-[logo_ex7]: images/w1day03_ex7_plot1.png "Box plot ex7"
+[logo_ex7]: ./w1day03_ex7_plot1.png "Box plot ex7"
 
 The plot has to contain:
 

one_exercise_per_file/week02/day01/ex02/audit/readme.md

@@ -2,7 +2,7 @@
 
 ![alt text][q1]
 
-[q1]: ../images/w2_day1_ex2_q1.png "Scatter plot"
+[q1]: ../w2_day1_ex2_q1.png "Scatter plot"
 
 2. This question is validated if the equation of the fitted line is: `y = 42.619430291366946 * x + 99.18581817296929`
 
@@ -10,7 +10,7 @@
 
 ![alt text][q3]
 
-[q3]: ../images/w2_day1_ex2_q3.png "Scatter plot + fitted line"
+[q3]: ../w2_day1_ex2_q3.png "Scatter plot + fitted line"
 
 4. This question is validated if the outputted prediction for the first 10 values are:
 

one_exercise_per_file/week02/day01/ex02/readme.md

@@ -16,7 +16,7 @@ X, y, coef = make_regression(n_samples=100,
 
 ![alt text][q1]
 
-[q1]: images/w2_day1_ex2_q1.png "Scatter plot"
+[q1]: ./w2_day1_ex2_q1.png "Scatter plot"
 
 2. Fit a LinearRegression from Scikit-learn on the generated data and give the equation of the fitted line. The expected output is: `y = coef * x + intercept`
 
@@ -24,7 +24,7 @@ X, y, coef = make_regression(n_samples=100,
 
 ![alt text][q3]
 
-[q3]: images/w2_day1_ex2_q3.png "Scatter plot + fitted line"
+[q3]: ./w2_day1_ex2_q3.png "Scatter plot + fitted line"
 
 4. Predict on X
 

one_exercise_per_file/week02/day01/ex05/audit/readme.md

@@ -2,7 +2,7 @@
 
 ![alt text][ex5q1]
 
-[ex5q1]: ../images/w2_day1_ex5_q1.png "Scatter plot "
+[ex5q1]: ../w2_day1_ex5_q1.png "Scatter plot "
 
 2. This question is validated if the output is: `11808.867339751561`
 
@@ -20,7 +20,7 @@ array([158315.41493175, 158001.96852692, 157689.02212209, 157376.57571726,
 
 ![alt text][ex5q5]
 
-[ex5q5]: ../images/w2_day1_ex5_q5.png "MSE"
+[ex5q5]: ../w2_day1_ex5_q5.png "MSE"
 
 6. This question is validated if the point returned is
 `array([42.5, 99. ])`. It means that `a= 42.5` and `b=99`.
@@ -36,7 +36,7 @@ Intercept (b): 99.18581814447936
 
 ![alt text][ex5q8]
 
-[ex5q8]: ../images/w2_day1_ex5_q8.png "MSE + Gradient descent"
+[ex5q8]: ../w2_day1_ex5_q8.png "MSE + Gradient descent"
 
 9. This question is validated if the coefficients and intercept returned are:
 

one_exercise_per_file/week02/day01/ex05/readme.md

@@ -21,7 +21,7 @@ X, y, coef = make_regression(n_samples=100,
 
 ![alt text][ex5q1]
 
-[ex5q1]: images/w2_day1_ex5_q1.png "Scatter plot "
+[ex5q1]: ./w2_day1_ex5_q1.png "Scatter plot "
 
 As a reminder, fitting a Linear Regression on this data means finding (a,b) that fits well the data points.
 
@@ -103,7 +103,7 @@ The expected output is:
 
 ![alt text][ex5q5]
 
-[ex5q5]: images/w2_day1_ex5_q5.png "MSE "
+[ex5q5]: ./w2_day1_ex5_q5.png "MSE "
 
 6. From the `losses` list, find the optimal value of a and b and plot the line in the scatter point of question 1.
 
@@ -119,6 +119,6 @@ In a nutshel, Gradient descent is an optimization algorithm used to minimize som
 
 ![alt text][ex5q8]
 
-[ex5q8]: images/w2_day1_ex5_q8.png "MSE + Gradient descent"
+[ex5q8]: ./w2_day1_ex5_q8.png "MSE + Gradient descent"
 
 9. Use Linear Regression from Scikit-learn. Compare the results.

one_exercise_per_file/week02/day02/ex02/audit/readme.md

@@ -2,4 +2,4 @@
 
 ![alt text][ex2q1]
 
-[ex2q1]: ../images/w2_day2_ex2_q1.png "Scatter plot"
+[ex2q1]: ../w2_day2_ex2_q1.png "Scatter plot"

one_exercise_per_file/week02/day02/ex02/readme.md

@@ -14,4 +14,4 @@ The plot should look like this:
 
 ![alt text][ex2q1]
 
-[ex2q1]: images/w2_day2_ex2_q1.png "Scatter plot"
+[ex2q1]: ./w2_day2_ex2_q1.png "Scatter plot"

one_exercise_per_file/week02/day02/ex03/audit/readme.md

@@ -2,7 +2,7 @@
 
 ![alt text][ex3q1]
 
-[ex3q1]: ../images/w2_day2_ex3_q1.png "Scatter plot"
+[ex3q1]: ../w2_day2_ex3_q1.png "Scatter plot"
 
 2. This question is validated if the coefficient and the intercept of the Logistic Regression are:
 
@@ -15,7 +15,7 @@ Coefficient:  [[1.18866075]]
 
 ![alt text][ex3q2]
 
-[ex3q2]: ../images/w2_day2_ex3_q3.png "Scatter plot"
+[ex3q2]: ../w2_day2_ex3_q3.png "Scatter plot"
 
 4. This question is validated if `predict_probability` outputs the same probabilities as `predict_proba`. Note that the values have to match one of the class probabilities, not both. To do so, compare your output with: `clf.predict_proba(X)[:,1]`. The shape of the arrays is not important.
 
@@ -25,7 +25,7 @@ Coefficient:  [[1.18866075]]
 
 ![alt text][ex3q6]
 
-[ex3q6]: ../images/w2_day2_ex3_q5.png "Scatter plot + Logistic regression + predictions"
+[ex3q6]: ../w2_day2_ex3_q5.png "Scatter plot + Logistic regression + predictions"
 
 As mentioned, it is not required to shift the class prediction to make the plot easier to understand.
 
@@ -33,4 +33,4 @@ As mentioned, it is not required to shift the class prediction to make the plot
 
 ![alt text][ex3q7]
 
-[ex3q7]: ../images/w2_day2_ex3_q6.png "Logistic regression decision boundary"
+[ex3q7]: ../w2_day2_ex3_q6.png "Logistic regression decision boundary"

one_exercise_per_file/week02/day02/ex03/readme.md

@@ -34,7 +34,7 @@ The plot should look like this:
 
 ![alt text][ex3q1]
 
-[ex3q3]: images/w2_day2_ex3_q3.png "Scatter plot"
+[ex3q3]: ./w2_day2_ex3_q3.png "Scatter plot"
 
 2. Fit a Logistic Regression on the generated data using scikit learn. Print the coefficients and the interception of the Logistic Regression.
 
@@ -42,7 +42,7 @@ The plot should look like this:
 
 ![alt text][ex3q3]
 
-[ex3q1]: images/w2_day2_ex3_q1.png "Scatter plot + Logistic regression"
+[ex3q1]: ./w2_day2_ex3_q1.png "Scatter plot + Logistic regression"
 
 4.  Create a function `predict_probability` that takes as input the data point and the coefficients and that returns the predicted probability. As a reminder, the probability is given by: `p(x) = 1/(1+ exp(-(coef*x + intercept)))`.  Check you have the same results as the method `predict_proba` from Scikit-learn.
 
@@ -67,7 +67,7 @@ The plot should look like this:
 
 ![alt text][ex3q6]
 
-[ex3q6]: images/w2_day2_ex3_q5.png "Scatter plot + Logistic regression + predictions"
+[ex3q6]: ./w2_day2_ex3_q5.png "Scatter plot + Logistic regression + predictions"
 
 ## 2 dimensions
 
@@ -92,7 +92,7 @@ The plot should look like this:
 
 ![alt text][ex3q7]
 
-[ex3q7]: images/w2_day2_ex3_q6.png "Logistic regression decision boundary"
+[ex3q7]: ./w2_day2_ex3_q6.png "Logistic regression decision boundary"
 
 ```python
 xx, yy = np.mgrid[-5:5:.01, -5:5:.01]

one_exercise_per_file/week02/day04/ex04/audit/readme.md

@@ -36,6 +36,6 @@
 
 ![alt text][logo_ex4]
 
-[logo_ex4]: ../images/w2_day4_ex4_q3.png "ROC AUC "
+[logo_ex4]: ../w2_day4_ex4_q3.png "ROC AUC "
 
 Having a 99% ROC AUC is not usual. The data set we used is easy to classify. On real data sets, always check if there's any leakage while having such a high ROC AUC score.

one_exercise_per_file/week02/day04/ex04/readme.md

@@ -31,6 +31,6 @@ classifier.fit(X_train_scaled, y_train)
 
 ![alt text][logo_ex4]
 
-[logo_ex4]: images/w2_day4_ex4_q3.png "ROC AUC "
+[logo_ex4]: ./w2_day4_ex4_q3.png "ROC AUC "
 
 - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_roc_curve.html

one_exercise_per_file/week02/day05/ex04/audit/readme.md

@@ -2,7 +2,7 @@
 
 ![alt text][logo_ex5q1]
 
-[logo_ex5q1]: ../images/w2_day5_ex5_q1.png "Validation curve "
+[logo_ex5q1]: ../w2_day5_ex5_q1.png "Validation curve "
 
 The code that generated the data in the plot is:
 
@@ -24,4 +24,4 @@ train_scores, test_scores = validation_curve(clf,
 
 ![alt text][logo_ex5q2]
 
-[logo_ex5q2]: ../images/w2_day5_ex5_q2.png "Learning curve "
+[logo_ex5q2]: ../w2_day5_ex5_q2.png "Learning curve "

one_exercise_per_file/week02/day05/ex04/readme.md

@@ -29,7 +29,7 @@ The plot should look like this:
 
 ![alt text][logo_ex5q1]
 
-[logo_ex5q1]: images/w2_day5_ex5_q1.png "Validation curve "
+[logo_ex5q1]: ./w2_day5_ex5_q1.png "Validation curve "
 
 The interpretation is that from max_depth=10, the train score keeps increasing but the test score (or validation score) reaches a plateau. It means that choosing max_depth = 20 may lead to have an over fitted model.
 
@@ -49,7 +49,7 @@ More details:
 
 ![alt text][logo_ex5q2]
 
-[logo_ex5q2]: images/w2_day5_ex5_q2.png "Learning curve "
+[logo_ex5q2]: ./w2_day5_ex5_q2.png "Learning curve "
 
 - **Note Plot Learning Curves**: The learning curves is detailed in the first resource.
 

one_exercise_per_file/week03/day01/ex02/readme.md

@@ -11,7 +11,7 @@ Notice that the neuron **o1** in the output layer takes as input the output of t
 In exercise 1, you implemented this neuron.
 ![alt text][neuron]
 
-[neuron]: images/w3_day1_neuron.png "Plot"
+[neuron]: ./w3_day1_neuron.png "Plot"
 
 Now, we add two more neurons: 
 
@@ -21,7 +21,7 @@ Now, we add two more neurons:
 
 ![alt text][nn]
 
-[nn]: images/w3_day1_neural_network.png "Plot"
+[nn]: ./w3_day1_neural_network.png "Plot"
 
 1. Implement the function `feedforward` of the class `OurNeuralNetwork` that takes as input the input data and returns the output y. Return the output for these neurons:
 

one_exercise_per_file/week03/day05/ex03/audit/readme.md

@@ -12,4 +12,4 @@
 
 ![alt text][logo]
 
-[logo]: ../images/w3day05ex1_plot.png "Plot"
+[logo]: ../w3day05ex1_plot.png "Plot"

one_exercise_per_file/week03/day05/ex03/readme.md

@@ -12,6 +12,6 @@ The goal of this exercise is to learn to use SpaCy embedding on a document.
  
 ![alt text][logo]
 
-[logo]: images/w3day05ex1_plot.png "Plot"
+[logo]: ./w3day05ex1_plot.png "Plot"
 
 https://medium.com/datadriveninvestor/cosine-similarity-cosine-distance-6571387f9bf8