DLP_HW_1_Choijamts Bataa

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IST 691: Deep Learning in Practice Homework 01 Instructions: Answer the following questions in no more than one page per question. In addition to the accuracy of your responses, the clarity, coherence, and concision of your writing are critical factors to earning full credit for this assignment. Cite any source you use outside of lecture notes. This includes the textbook. Reproducing or even paraphrasing an answer from a generative AI tool, such as ChatGPT, is not allowed on this assignment. Submit your responses in single Word or pdf document. 1. In traditional machine learning (non-NN methods), we use feature engineering to model complex relationships between observed variables (features) and a target variable (response). When using deep learning methods, should we design and incorporate feature engineering processes? Explain why or why not. When using deep learning methods, there is no need to design and incorporate feature engineering process because deep learning creates rules algorithmically while feature engineering requires great amount of human intervention (Deep Learning in Visual Approach, Chapter 1, p4). 2. Explain in words or equations why we should introduce nonlinearity in neural networks. When fully connected network applies only addition and multiplication or linear function, it collapses. To prevent this collapse, activation functions with nonlinear operations are used (Deep Learning in Visual Approach, Chapter 13, p331). 3. You are training a deep learning model to predict sentiment of Twitter posts — the model predicts whether a post is “happy” or “sad”. Your model achieves 0.95 accuracy on the dataset you used to train the model. But when you take new posts from Twitter and use your model to predict the sentiment, the model performs much worse. What might have happened? What should you do to improve your model? The model showed excellent performance on training dataset, but it underperformed on new data, which means that there is overfitting. It might have happened that the model used specific information in the training dataset rather than learning general rules. We can improve the model by finding the moment when rules apply specific data and stop learning at that moment. We can also improve the model by using regularization techniques. (Deep Learning in Visual Approach, Chapter 9, p196). 4. The MNIST dataset consists of images of dimension 28x28 pixels with one color channel (28x28x1), with each image corresponding to a label between 1 and 10. To build a classifier, we implement a multi-layer perceptron model with 3 hidden layers. The first two hidden layers have 100 perceptrons each, and the third hidden layer has 30 perceptrons. Calculate how many weights will be updated for each iteration of gradient descent. Show your work. To calculate how many weights will be updated for each iteration of gradient descent in a
IST 691: Deep Learning in Practice Homework 01 multi-layer perceptron (MLP) model, I will use the notion that each neuron is connected to all neurons in the previous layer and each connection has its own weights (Deep Learning in Visual Approach, Chapter 13, p328). Firstly, in terms of input layer, the image has 28x28 neurons and there is no need for weights. Next, there are three hidden layers, and first hidden layer has 100 perceptrons. Each perceptron is connected to all 28x28 neurons in the input layer. In terms of the second hidden layer, it also has 100 perceptrons, each connected to all 100 neurons of the first hidden layers. For the last hidden layer, it has 30 perceptrons, each connected to all 100 neurons of the second hidden layer. Lastly, in terms of the output layer, there are 10 neurons, each connected to 30 neurons of the 3 rd hidden layer. Therefore, the total number of weights updated during each iteration of gradient descent is 100 perceptrons * 28x28 weights+100 perceptrons * 100 weights +30 perceptrons * 100 weights + 10 neurons * 30 weights = 797,300 weights 5. Answer the following questions based on a close reading of this article and possibly additional research (remember to cite your sources). https://www.nytimes.com/2023/06/28/technology/facial-recognition-shoplifters-britain.html a) What is the technology being discussed in this article? How does it relate to deep learning? In this article, a solution on how security cameras are applied to combat petty crime is discussed. When shoplifters enter a store, the face recognition software recognizes, and alerts based on data from security camera. Way how the AI tool works is simply about deep learning. b) What are some ethical concerns discussed in the article? Do you share these concerns? Why or why not? Facewatch included people’s biometric data into software without informing them or making proper investigation on what actually had occurred. Due to this practice, woman who is purchasing milk was recognized as barred shoplifter. Also, it categorized problematic customers the same as shoplifters. Actually, they are different. c) Are these concerns addressed by the company deploying this technology? How? Facewatch amended its policy on when faces of offenders are added into the program. It used to add offenders who even did single minor offenses, but the company changed its policy and as a result, they would share information of only serious and violent offenders and broadcast alert only repeat offenders.
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