The main objective of this assignment is to offer students hands-on experience with neural networks using TensorFlow Playground, an interactive web-based tool. Through practical experimentation, you will explore various aspects of neural network design, including different architectures, activation functions, and hyperparameters. The goal is to help you grasp how these choices impact the neural network's behavior and performance. The assignment comprises three main questions, each focusing on distinct datasets and configurations. For each experiment, you are required to provide details of the parameters used (if it's not specified as a fix amount in the question), training and testing loss values, the number of epochs needed to reach the desired outcome (loss below 0.15), or the number of epochs when the loss stabilizes. Additionally, you are encouraged to include an image of their best output, which may be cropped to show the losses. For all questions, you should evaluate the experiments using learning rates of 0.01, 0.1, 0.3, 1, and 3, and you are expected to choose the most effective learning rate and comment on any intriguing behavior observed during the experiments (ex: I didn't observe significant differences when changing the learning rate from 0.01 to 0.1. However, upon setting it to 0.3, the model exhibited faster boundary identification, but when I changed it to 3 ...)./nQ1. Create a neural network with following setup (initial configuration): (8/20 Points) • Data: Dataset: Gausian Ratio of training to test data: 60% 。 Noise: 20 。 Batch size: 10 Features: X1, X2 Number of hidden layers: 0 1.1. Experiment with the linear activation function and choose a learning rate that suits your preference. Evaluate whether this network configuration can effectively find the decision boundary for the given dataset. Present your findings and results. (1 Point) 1.2. Switch the dataset to "Exclusive Or" while keeping all other parameters unchanged. Execute the model and assess its capability to identify the decision boundary. Provide reasons for why it can or cannot find the decision boundary. (1 Point) 1.3. In your experimentation, incorporate the sigmoid and tanh activation functions separately. Determine whether these network structures can successfully discover the decision boundary. (1 Point) 1.4. Introduce an additional hidden layer with two neurons to the network, utilizing both the tanh and sigmoid activation functions. Report the outcome and describe any observed effects resulting from this modification. (1 Point) 1.5. Increase the number of neurons in the hidden layer and elaborate on the noticeable changes you observe in the model's performance. (1 Point)/n1.6. Remove the neurons added in the previous step, and instead, add another hidden layer containing two neurons (resulting in two hidden layers, each with two neurons). Provide a detailed report on the results obtained and offer explanations for any significant observations. (1 Point) 1.7. Remove all the hidden layers and select the linear activation function. use engineered features as follows: 1.7.1. Use X and X2 and report your result. (1 Point) 1.7.2. Use X1 X2 and report your result. (1 Point)

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cgsc 3601 b artificial intelligence and cognitive science late summer

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The main objective of this assignment is to offer students hands-on experience with neural networks using TensorFlow Playground, an interactive web-based tool. Through practical experimentation, you will explore various aspects of neural network design, including different architectures, activation functions, and hyper