Update README.md

peterbonnesoeur · andrewekhalel · commit c26e6670c30c · 2021-10-31T15:44:05.000+01:00
Provided proposition of answers for the questions : 8,10,11,13,15,17,18,20
diff --git a/README.md b/README.md
@@ -40,38 +40,68 @@ Imagine a network with random initialized weights ( or normalised ) and almost 5
 
 #### 8) Given stride S and kernel sizes  for each layer of a (1-dimensional) CNN, create a function to compute the [receptive field](https://www.quora.com/What-is-a-receptive-field-in-a-convolutional-neural-network) of a particular node in the network. This is just finding how many input nodes actually connect through to a neuron in a CNN. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+The receptive field are defined portion of space within an inputs that will be used during an operation to generate an output.
+
+Considering a CNN filter of size k, the receptive field of a peculiar layer is only the number of input used by the filter, in this case k, multiplied by the dimension of the input that is not being reduced by the convolutionnal filter a. This results in a receptive field of k*a.
+
+More visually, in the case of an image of size 32x32x3, with a CNN with a filter size of 5x5, the corresponding recpetive field will be the the filter size, 5 multiplied by the depth of the input volume (the RGB colors) which is the color dimensio. This thus gives us a recpetive field of dimension 5x5x3.
+
 #### 9) Implement [connected components](http://aishack.in/tutorials/labelling-connected-components-example/) on an image/matrix. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
 
 #### 10) Implement a sparse matrix class in C++. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+[[Answer]](https://www.geeksforgeeks.org/sparse-matrix-representation/)
 
 #### 11) Create a function to compute an [integral image](https://en.wikipedia.org/wiki/Summed-area_table), and create another function to get area sums from the integral image.[[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+[[Answer]](https://www.geeksforgeeks.org/submatrix-sum-queries/)
 
 #### 12) How would you remove outliers when trying to estimate a flat plane from noisy samples? [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
 Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence on the values of the estimates.
 [[src]](https://en.wikipedia.org/wiki/Random_sample_consensus)
 
+
+
 #### 13) How does [CBIR](https://www.robots.ox.ac.uk/~vgg/publications/2013/arandjelovic13/arandjelovic13.pdf) work? [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
 [[Answer]](https://en.wikipedia.org/wiki/Content-based_image_retrieval)
+Content-based image retrieval is the concept of using images to gather metadata on their content. Compared to the current image retrieval approach based on the keywords associated to the images, this technique generates its metadata from computer vision techniques to extract the relevant informations that will be used during the querying step. Many approach are possible from feature detection to retrieve keywords to the usage of CNN to extract dense features that will be associated to a known distribution of keywords. 
+
+With this last approach, we care less about what is shown on the image but more about the similarity between the metadata generated by a known image and a list of known label and or tags projected into this metadata space.
 
 #### 14) How does image registration work? Sparse vs. dense [optical flow](http://www.ncorr.com/download/publications/bakerunify.pdf) and so on. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
 #### 15) Describe how convolution works. What about if your inputs are grayscale vs RGB imagery? What determines the shape of the next layer? [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+[[Answer]](https://dev.to/sandeepbalachandran/machine-learning-convolution-with-color-images-2p41)
+
 #### 16) Talk me through how you would create a 3D model of an object from imagery and depth sensor measurements taken at all angles around the object. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
 #### 17) Implement SQRT(const double & x) without using any special functions, just fundamental arithmetic. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+The taylor series can be used for this step by providing an approximation of sqrt(x):
+
+[[Answer]](https://math.stackexchange.com/questions/732540/taylor-series-of-sqrt1x-using-sigma-notation)
+
 #### 18) Reverse a bitstring. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+If you are using python3 :
+
+```
+data = b'\xAD\xDE\xDE\xC0'
+my_data = bytearray(data)
+my_data.reverse()
+```
 #### 19) Implement non maximal suppression as efficiently as you can. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+
+
 #### 20) Reverse a linked list in place. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+[[Answer]](https://www.geeksforgeeks.org/reverse-a-linked-list/)
+
 #### 21) What is data normalization and why do we need it? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)]
 Data normalization is very important preprocessing step, used to rescale values to fit in a specific range to assure better convergence during backpropagation. In general, it boils down to subtracting the mean of each data point and dividing by its standard deviation. If we don't do this then some of the features (those with high magnitude) will be weighted more in the cost function (if a higher-magnitude feature changes by 1%, then that change is pretty big, but for smaller features it's quite insignificant). The data normalization makes all features weighted equally.
 
