# Tag Info

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I suggest you take a look at Chris Olah's blog. Has several interesting post including ones on visualizing weights and interpretability. Most of his papers also have Google Colab links so you can reproduce the results. If you want something more similar to the model.summary() method you mention, TensorBoard Graph Dashboard might help.

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The most usual case of bias=False is in layers before/after Batch Normalization with no activators in between. The BatchNorm layer will re-center the data anyway, removing the bias and making it a useless trainable parameter. Quoting the original BatchNorm paper: Note that, since we normalize $Wu+b$, the bias $b$ can be ignored since its effect will be ...

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We do it experimentally; you're able to look at what each layer is learning by tweaking various values throughout the network and doing gradient ascent. For more detail, watch this lecture: https://www.youtube.com/watch?v=6wcs6szJWMY&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&index=12 it provides many methods used for understanding exactly what your ...

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Let's assume you would like to work with a classic DQN. You need to train the Q-network where inputs are the states and actions. The DQN is a function of Q(states, actions). The network is supposed to predict Q-value. The agent must pick up the action that produces the highest Q by giving the all possible actions to the network, in your case. Let's assume ...

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You are correct. The main conceptual difference is that optimization is about finding the set of parameters/weights that maximizes/minimizes some objective function (which can also include a regularization term), while regularization is about limiting the values that your parameters can take during the optimization/learning/training, so optimization with ...

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ReLU is piecewise linear function that outputs the received input directly if it's positive, or outputs a zero. i.e., $max(0, x)$ How significant is adding relu to full connected layers? ReLU, being an activation function, will determine what the output of the nodes in your FCs are. Since it's a non-linear function, one significance is it will allow the ...

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This type of connections are called skip or residual connections. There are numerous works which employs this type of mechanism, for example: ResNet, SkipRNN. In addition here you can find a paper that empirically explores the skip connections for sequential tagging, or this one for speech enhancement.

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This is akin to asking "Why do we need more than one instance of sine to represent any repeating function" or "why can't we represent any polynomial with an equivalent polynomial of just the first degree?" There are many, many problems... I'd even want to say most... that will require more than one layer to solve because the higher ...

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Actually, the hierarchical learning explanation given by mindcrime is not that acceptable anymore (This was also indicated by Ian Goodfellow). Since there are neural networks with 150 layers or more, and this explanation does not make sense for such neural networks. However, we can think of it as solving the knots of high dimensional manifolds, i.e. we ...

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Depth maps are created using principles of photometry (method of measuring light). The depth maps (rather images) you took from the website are "images" not exact depth "maps". So by default when you pull out a png image from a webpage, it will be saved in "RGB". That is the reason you got an array with 3 layers. In practice, it ...

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This is a really hard question to answer, as there's no telling just how much each company spends specifically on AI. It helps that Google (or rather Alphabet Inc) has a specific subsidiary company specialising in AI (DeepMind), but even with this Google may have it's own division that works on other AI projects. You're questions are vague and vary massively ...

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This tells me that because the initial model was performing so poorly without any fine-tuning, most of the learning that led to the 90% accuracy was only because of the additional layers, not the layers that were transferred from the model trained on the D1 dataset. Is this a correct inference? This is a possibility, but not the only one. If you were re-...

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I can say "Stable Learning" of a supervised machine learner is as follows: A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. You can follow this link to know more in details about how can we measure the stability in the context of computational learning theory.

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You are correct about your first assumption, but not about your second assumption. More layers does not always mean better pattern detection. The analogy that in the deeper layers the network learns more complex features is an oversimplified one. It is true to some extent, although it is not enough to explain very complex architectures like GoogLeNet. ...

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It depends on what your outputs are. For example, if both outputs are similar then you can use one output branch. However, what if the two outputs are different? With two output branches you can used two different loss functions. Now your model will optimize the two branches separately. Imagine if you have a model that has to output a class label for the ...

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The network architecture is relevant to this question. Convolutional neural network architectures enforce the building up of features because the neurons in earlier layers have access to a small number of input pixels. Neurons in deeper layers are connected (indirectly) to more and more pixels, so it makes sense that they identify larger and larger features. ...

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The simple answer to your question is "No" with a caveat. The caveat is that there are signs that your network is never going to perform well. For example, the epoch accuracy fails to improve or even consistently declines over the first several epochs, or the validation accuracy is flat or declining. It could be that the validation loss starts ...

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I think you are misreading the relevant passage here. Since you do not specify exact excerpt(s), I take that by "implicit assumption" you refer to the equation (2) (application of a ReLU) and the corresponding text explanation (bold emphasis mine): We apply a ReLU to the linear combination of maps because we are only interested in the features ...

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This is not necessarily the only way to do this but it would be the approach I'd take. Assuming your agents position is a vector in $\mathbb{R}^d$, then I would have the network take as input this position vector and pass it through a fully connected layer. I would also take as input the matrix and pass it through a convolutional layer(s) and flatten the ...

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