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Deeper models can have advantages (in certain cases) Most people will answer "yes" to your question, see e.g. Why are neural networks becoming deeper, but not wider? and Why do deep neural networks work well?. In fact, there are cases where deep neural networks have certain advantages compared to shallow ones. For example, see the following papers The ...


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Have you read the book The Master Algorithm: by Pedro Domingos? He discusses the present day machine learning algorithms... Their strengths, weaknesses and applications... Deep Neural Network Genetic Algorithm Bayesian Network Support Vector Machine Inverse Deduction


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Why has this merge proven beneficial? If you think about the shared Value/Policy network as consisting of a shared component (the Residual Network layers) with a Value and Policy component on top rather than Separation of Concerns it makes more sense. The underlying premise is that the shared part of the network (the ResNet) provides a high-level ...


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First of all, you need to realise that you will not be able to do it. Google is a multi-billion dollar company, with a large number of very bright and well-funded researchers. That tells me that it is not something a single person can do by themselves. Then, you already have some pre-conceptions about it. You want to use a machine learning approach, using ...


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This sounds like a problem that might be solvable with a LSTM-DQN approach, as described in Language Understanding for Text-based Games using Deep Reinforcement Learning by Narasimhan et al., 2015, and then extended to a domain very similar to your problem in Deep Reinforcement Learning for Syntactic Error Repair in Student Programs by Gupta et al., 2019. ...


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A more efficient way would be creating a multi input model, with something like this: ___________ _____________ |__Image__| |Other input| _____|_____ _____|_____ |___CNN___| |__Dense___| _____|______ _____|______ |_Features1_| |_Features2_| __|_____|__ |__Merge___| _____|______ |___Dense__| ...


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As I understand, I think you wish to directly try out some deep learning stuff and things like library downloading, tools downloading, and managing all these really stop you from even starting to try out deep learning experiments.If this is what you asked for: Google Colab I think this is the best place for you. Anyone with a Google Drive account can sign ...


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I've read all the papers about PReLU, LeakyReLU (...) and all the claims how it improves this and that but the little dirty secret is: most of the time it doesn't matter at all and you can't go much wrong with ReLU - empirically proven. I've personally tried all of them in many different problems (from training small networks from scratch through changing ...


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Deep learning is actually pretty useful (relative to other techniques) precisely when there is no simple mapping between input and output, and features from the raw input need to be aggregated and combined in complex ways by successive layers to form the output. As I pointed out in my answer to the AI SE decompilation question, there is recent DL research ...


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I skimmed through your question and understood that the state/action space is finite, so in this case, RL would be a good option for storage. The most basic RL technique will keep track of a matrix Q ∈ ℝs×a, where s is number of possible states, and a is number of possible actions. In addition to a small overhead of agent's parameters: &...


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Architecture describes a general approach to a ML problem, and the parameterization of that approach. For example, a neural net architecture would define the number and size of different layers, the type of each layer, and so on. A model is one specific instance of a given architecture, trained on a given dataset. For the example of neural nets, the model ...


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AI is a two step process: use data to learn a model, and then use the model to make predictions using new data. So an AI model is the result of the learning process, and the architecture is the detail of how the learning is achieved.


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It mostly seems to be a personal preference type of thing. But in my readings, AI architecture typically means a large scale structural difference (connectionist / GOFAI; deep stack / recurrent, while AI models are finer distinctions between methods in a common architecture (say, the AlexNet vs other CNNs)


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Well simply put AI model can be seen just as a flowchart showing how the control flow moves where it moves how it moves why it moves etc. However AI architecture refers to the next step after building an AI model AI architecture involves representation of the functions that you use in your program. It also involved declaration of the variables you're going ...


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The question is really broad---as stated by @thecomplexitytheorist---so difficult to give a meaningful answer. The following is about a clarification about the problem, and some directions. A model-based reflex agent is a blueprint describing the key components necessary to build that agent. It is an abstract architecture to guide the creation of concrete ...


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The input to the neural network is a $19 × 19 × 17$ image stack comprising $17$ binary feature planes. $8$ feature planes $X_t$ consist of binary values indicating the presence of the current player’s stones ($X^i_t = 1$ if intersection $i$ contains a stone of the player’s colour at time-step $t$; $0$ if the intersection is empty, contains an opponent ...


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There is indeed an investigation in progress, regarding this topic. A first publication from last march noted that modularity has been done, although not explicitly, since some time ago, but somehow training keeps being monolithic. This paper assess some primary questions about the matter and compares training times and performances on modular and heavily ...


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A benchmark comparison of systems comprised of separately trained networks relative to single deeper networks would not likely reveal a universally applicable best choice.1 We can see in the literature the increase in the number of larger systems where several artificial networks are combined, along with other types of components. It is to be expected. ...


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A RBM (restricted Boltzmann machine) can be trained to extract document features. The same resulting machine can extract features of two or more documents. Because documents can be just as easily processed in series using the same machine parameters and CPU (saving the feature results) as documents could be processed in parallel using separate CPUs, the ...


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In the perceptron design generally used in Artificial Neural Networks, we know precisely what a single neuron is capable of computing. It can compute a function $$f(x) = g(w^{\top} x),$$ where $x$ is a vector of inputs (may also be vector of activation levels in previous layer), $w$ is a vector of learned parameters, and $g$ is an activation function. We ...


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Deeper networks have more learning capacity in the sense that they can fit to more complex data. But at the same time, they are also more prone to overfitting the training data and therefore fails to generalize to the test set. Apart from overfitting, exploding/vanishing gradients is another problem which hampers convergence. This can be addressed by ...


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My experience from a tactical standpoint is to start out with a smaller simple model first. Train the model and observe the training accuracy and validation loss and validation accuracy. My observation is that, to be a good model, your training accuracy should achieve a value of at least 95%. If it does not, then try to optimize some of the hyper-parameters. ...


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First of all, there is no real 'intelligence' innate to artificial Neural Networks (NNs). All they do is trying to approximate a mathematical function with a certain degree of generalization (hopefully without learning a given dataset by heart, i.e. hopefully without overfitting). The more nodes (or neurons) you include into the network, the more complex a ...


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It depends on whether the action is part of the input or output of a neural network estimating the Q-value(state, action). The network on the left has the state as input and outputs one scalar value for each of the categorical actions. It has the advantage of being easy to setup and only needs one network evaluation to predict the Q-value for all actions. ...


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I am assuming the images you gave the model all contain sheep. This is what i understand from your question. Any model that you build will be based on the data that you give (training data) and your code. In your case, if you only give images that contains sheeps, and then you test it with no sheep and a background the model hasn't seen, it will search ...


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In my opinion, there are many functions in our brain. Surely much more than the artificial neural network nowadays. I guess this is the field of brain science or cognitive psychology. Some brain structures may help for certain applications, but not all. Neural network though is a simplest form of our brain, but has the most general usages. On the other ...


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(1) $X_t$ and $H_{t-1}$ are concatenated. The blog you cited explained its notation "Lines merging denote concatenation". For example, if $X_t=[1,2,3]$ and $H_{t-1}=[4,5,6,7]$, then their concatenation is $[1,2,3,4,5,6,7]$ (2) When you say "input weights" or "weights of the input of the previous time step", are you referring to the $W_i$ in your cited blog? ...


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In theory, deeper architectures can encode more information than shallower ones because they can perform more transformations of the input which results in better results at the output. The training is slower because back propagation is quite expensive, as you increase the depth, you increase the number of parameters and gradients that need to be computed. ...


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As far as I know, more than 3 channel is perfectly fine, since, 3 channels are what we use for images and that's enough since we can only see this many colors, but I don't see why more than that wouldn't work Your 2nd question is like asking whether or not you will be good at a sport... Just try it For your 3rd question, I've never seen any language AI ...


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This number refers to the number of kernels (or feature maps) that are convolved with the input. So, for example, in the first convolutional layer, $64$ $3 \times 3$ kernels are convolved with the image. The ResNet presented in Deep Residual Learning for Image Recognition is used for image classification. Furthermore, note that your diagram already contains ...


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