deep learning coursera notes

19 Sep deep learning coursera notes

Depends on Its momentum makes it keep going in the previous direction.As a concrete example, suppose that “actress” is closer to “babysit” than “actor.” By applying neutralizing to “babysit” we can reduce the gender-stereotype associated with babysitting. Lets introduce a new notation: Let us first present the formula and then explain it:One way to build a speech recognition system is to use the seq2seq model with attention that we explored previously, where the audio clip (divided into small timeframes) is the input and the audio transcript is the outputOur task is to take an audio clip, possibly perform preprocessing to compute a spectrogram and then the features that we will eventually pass to an RNN, and predict at which timesteps the wake word was uttered. Defensive Programming with Matrixes; Activation Functions; Notable Quiz Questions For an average pooling layer, it gets equally distributed to the inputs. Practical Aspects of Deep Learning: Learning OutcomesThe terms $ e(t, t’) $ are passed into a softmax layer to ensure that for every fix value of t, $ \alpha^{\langle t, t^{\prime} \rangle} $ will sum to one.Not recommended as it couples both objectives of optimising cost function J and solving overfitting (as opposed to gradient descent for J and regularisation for overfitting).Most computational music algorithms use some post-processing because it is difficult to generate music that sounds good without such post-processing. Then, you would look at all of the output sentences and score them against our objective: argmax_y \frac{1}{T_y^{\alpha}} \sum_{t=1}^{T_y} \log P(y^{} | x, y^{<1>}, ..., y^{},\hat y^{<2>}, ... \hat y^{}| x)Our model has to two main components: our encoder-decoder architecture (which we will simply refer to as our 'RNN' from here on out) and the beam search algorithm applied to the outputs of this RNN. Ideally, $ \Gamma_u = 1 $ for In this equation, 1{.} is the indicator function.Lastly, most of the terms are in the range 0 < x < 1, thus they are exponentially negative. Notes from Coursera Deep Learning courses by Andrew Ng By Abhishek Sharma Posted in Kaggle Forum 3 years ago. Written on
Human ear doesn’t process raw wave forms, but has physical structures that measures the amounts of intensity of different frequencies.We get even better results by combining this representation from multiple different layers. Thus the more terms there are, the more negative the joint probability. Case 2: P(y* | x) \le P(\hat y | x)P(y* | x) \le P(\hat y | x) This is because $ log(P(y \mid x)) $ is a non-decreasing monotone graph, thus a value of y that maximises $ P(y \mid x) $ will also maximise $ log(P(y \mid x)) $.Summary of Notations for Convolution OperationNeed to (labourously) specify each landmarkThe difference between gradient descent, mini-batch gradient descent and stochastic gradient descent is Suppose you are using YOLO on a 19x19 grid, on a detection problem with 20 classes, and with 5 anchor boxes. it is also tempting in this case to increase the beam width (again, it never seems to hurt!). In the first portion of the course, you will know how to evaluate your deep learning model and note down the hyper-parameter tuning technique in different situations. Hotness. Mini-batch size performs better than stochastic as it leverages vectorisation.Output is a softmax layer of the entire corpusPossible range: 0 < magnitude of $ b_x, b_y $ < 1; magnitude of $ b_h, b_w $ > 1 (this is why boundary box sizes can differ)Intuitively, GD with Momentum (a) dampens oscillations in directions of high curvature by cancelling out gradients with opposite signs and (b) builds up velocity in directions (towards min) with a gentle but consistent gradient (since they don’t cancel each other out).This algorithm “only looks once” at the image in the sense that it requires only one forward propagation pass through the network to make predictions. 1 Neural Networks We will start small and slowly build up a neural network, step by step.

If countclip(1-gram), it’ll return the individual words of the sentence.Powers of two are often chosen to be the mini-batch size, e.g., 16, 32, 64, 128.Relation to face encoding: Face encoding takes in an image and outputs a 128D vector (and compares this 128D vector with another 128D vector to determine if the images are the same).

Table of Contents.

However, instead of the initial hidden state being greedy: \text{"Jane is going to be visiting Africa in September"} We will also look at attention models.Notice that the decoder network looks pretty much identical to the language model.

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