Deep learning coursera github
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Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition. If the satisficing metric is not met, then the model is not good enough. Chapter 1 Preface This repository contains materials to help you learn about Deep Learning with the and Microsoft Azure. It assumes you have taken a first course in machine learning, and that you are at least familiar with supervised learning methods. Overall, this course has the best programming assignments. Spend a few days collecting more data using the front-facing camera of your car, to better understand how much data per unit time you can collect. Setting up your optimization problem Normalizing inputs Figure 6.

You are employed by a startup building self-driving cars. You need to score 70% to pass. Suppose img is a 32,32,3 array, representing a 32x32 image with 3 color channels red, green and blue. The Coursera course by Geoffrey Hinton. Each node in Neural Network does the same thing as the node in Logistic Regression. You should implement mini-batch gradient descent without an explicit for-loop over different mini-batches, so that the algorithm processes all mini-batches at the same time vectorization.

True because it is the largest category of errors. All that is just the gradient for a single training example, we can vectorize to work with m training example: Figure 5. True because it is greater than the other error categories added together 8. You are working on an automated check-out kiosk for a supermarket, and are building a classifier for apples, bananas and oranges. Question 12 After working on this project for a year, you finally achieve: Human-level performance 0.

If you are very new to the field and willing to devote some time to studying deep learning in a more systematic way, I would recommend you to start with the book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Normalization helps gradient descent run faster. Run at random initialization, perhaps again after some training after some number of iterations. This is an intermediate to advanced level course. You will build a cat classifier that recognizes cats with 70% accuracy! The goal is to recognize which of these objects appear in each image.

True False 1 point 8. Be able to recognize the basics of when deep learning will or will not work well. Caviar6 min Batch Normalization Normalizing activations in a network8 min Fitting Batch Norm into a neural network12 min Why does Batch Norm work? Suppose we want to make a system that can recognize faces of different people in an image. Deep learning models, in simple words, are large and deep artificial neural nets. Which of the following are true? If the satisficing metric is met we choose the higher accuracy model. The assignments where I got to build my own models step by step with numpy were absolutely amazing.

What do you tell your colleague? Theano is probably the best tool currently avaiable for AutoDiff. You should get a bigger test set. If the mini-batch size is m, you end up with stochastic gradient descent, which is usually slower than mini-batch gradient descent. The architecture of a generative adversarial network. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. If the mini-batch size is m, you end up with batch gradient descent, which has to process the whole training set before making progress. The forward propagation process means that we compute the graph from left to the right in this picture.

If the mini-batch size is 1, you lose the benefits of vectorization across examples in the mini-batch. How quickly you can go around this cycle? So layer 1 has four hidden units, layer 2 has 3 hidden units and so on. The number of hidden layers is 4. No, because this shows your variance is higher than your bias. This principle is sometimes called Orthogonalization, and this is the idea that you want to think about 1 task at a time. This makes deep learning an extremely powerful tool for modern machine learning.

Rethink the appropriate metric for this task, and ask your team to tune to the new metric. If we solve this as a typical machine learning problem, we will define facial features such as eyes, nose, ears etc. Alternative method: using L2 regularization, then you can just train the neural network as long as possible. The convolutional layers for the larger image are the same as those for the cropped out image, so that each sliding window on the larger image will correspond to a cell in the output volume. Question 3 You are carrying out error analysis and counting up what errors the algorithm makes. You initialize the weights to relative large values, using np.

You have underfit to the dev set. It has been officially promoted in the ;- Fig 6. This course also covers transfer learning, multi-task learning, and end-to-end learning. Problem with a high Bayes error. Note that decision line between two classes have form of circle, since that we can add quadratic features to make the problem linearly separable.