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Neuroevolution Through Augmenting Topologies or NEAT may be what you are referring to. The original paper by Kenneth O. Stanley is here NEAT combines a neural network and a genetic algorithm. Instead of using back propagation or gradient descent to "train" your network, NEAT creates a population of very simple neural networks (no connections) and evolves ...


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Generally researchers (Ghandar et al, Michalewicz, Lam) have used the profit or return on investment (ROI) as a reward (fitness) function. $ROI = \frac{ \left[\sum_{t=1}^T (Price_t - sc) \times I_s(t) \right] - \left[ \sum_{t=1}^T (Price_t + bc) \times I_b(t) \right] }{ \left[ \sum_{t=1}^T (Price_t + bc) \times I_b(t) \right] }$ where $I_b(t)$ and $I_s(t)$ ...


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"Artificial Intelligence: A Modern Approach" (AIMA) by Russel and Norvig is a general introductory book on AI. That means it not only covers sub-symbolic AI (like machine learning) but also symbolic AI. Therefore, it can "only" give you an overview of each topic (I put only in quotation marks since it is actually quite ambitious to cover all topics of AI in ...


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Let's define your problem from another point of view. Let's say that in this RL problem you have two agents (agent1 and agent2) that compete with each other in order to accomplish their own goal, i.e., wining connect4 game. Therefore, we could say that from agent1's point of view, he is player1 and the player2 is agent2. The same way, from agent2's point of ...


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What you are calling 'analyzing the surroundings' is generally referred to as perception. Self-driving cars sense their surroundings using cameras, radars, lidars often combining or fusing more than one sensor to paint a picture of the environment. A lot of algorithms get used for fusing the sensor data and then deriving an understanding of the surrounding. ...


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Self-driving cars use a combination of both supervised as well as reinforcement learning. Huge amounts of sensor data are recorded in real-time. This data can be used to train all sorts of supervised classifiers, e.g. for predicting rain or switching on lights. You can also set up a model to predict pedestrians and other cars. This is supervised learning. ...


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Training time depends on a lot of parameters. Some of them are: Size of each image (resolution) Color/Monochrome image (color image has 3 times data if you consider RGB image) Like you mentioned on the type of DNN. No. of layers of DNN. No. of neurons in each layer. Total no. of images in the dataset. (2.6 million here) GPU you are using (you didn't ...


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Overview As it has already been observed, your main problem, beside the training related issues like fixing the learning rate, is you have basically no chance to learn such a big model woth such a small dataset ... from scratch So focusing on the real problem, here are some techniques you could use dataset augmentation transfer learning from a ...


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Try lowering the learning rate. Such a loss curve can be indicative of a high learning rate. Due to a high learning rate the algorithm can take large steps in the direction of the gradient and miss the local minima. Then it will try to come back to the minima in the next step and overshoot it again. You may also try switching to a momentum-based GD ...


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I haven't read any relevant paper about this, but I have seen some implementations based on what you are describing, arbitrarily called DGNN (Dynamic Growing Neural Network). Hope this term can help your search.


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In Don't Decay the Learning Rate, Increase the Batch Size, Smith et al. train ResNet-50 on ImageNet to 76.1% with only 2500 updates. Has anyone done it in less? In The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent Sankararaman et al. present the concept of gradient confusion which slows convergence, and ...


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In this case, you have an ontology and want to learn the ontology. There are many researches in this topic that you can find. However, the data could be the most challenging part. Some of the researches: Ontology learning for the Semantic Web Ontology Learning Also, as these are some frameworks to ontology learning, you can use deep networks such as RNN‌ ...


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I try to answer the things I know for sure: One effect of bigger images is the increasing computation time due to more pixels (input to your training) 4.Grayscaling reduces the information, which might decrease training time, but also model performance (accuracy, precision, recall). What I have seen is that grayscaling is used in for example face detection ...


1

Here's a link to some benchmarks that should give you some insights. In my experience (I've used systems with both 1080s and V100s) I've found that as of about a year ago, a lot of the common tools couldn't use the V100s well. Until we started doing some manual optimization, the 1080s were comparable if not better on common tasks. Of course, once we put ...


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It's highly plausible that you don't need anything neural. For model selection, the following image is always a good rule of thumb.


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Both of them using the end-to-end approach for speech recognition. However, because of the code complexity in DeepSpeech, you can't tune the model for your work. Kaldi could be configured in a different manner and you have access to the details of the models and indeed it is a modular tool. I think Kaldi could be a better tool academically and also ...


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Can you use them commercially? Yes. Is Google able to sue you any time they want? Yes. Will they do that... Probably not. Google isn't a known patent bully, I would give them the benefit of the doubt in this kind of situation and say, unless you start really giving them real trouble, they wouldn't do anything. Some companies/people know an idea can ...


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The first neural network machine was the stochastic neural analog reinforcement calculator (SNARC), built in the 1950s. As you can see, it's pretty old. After that, there were several advances regarding backpropagation and the vanishing gradient problem. However, the ideas itself are not novel. Simply put, we have the data and processing power today that we ...


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You need to perform Hyperparameter Tuning to identify - Number of hidden layers. Number of neurons in each of the hidden layers. Dropout The activation function you use in each of your hidden layers. There parameters are only related to how you build your model. There are others that relate to training like batch size, number of epochs and so on. Your ...


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Nicola Bernini's answer is quite comprehensive. Here are my insights. First of all, think whether you really need neural networks to solve your problem. Think whether traditional computer vision operations like edge detection/ region-based methods help you to solve your problem (OpenCV can help you here). Think about your data again. In case you decide to ...


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Data scaling or normalization is a process of making model data in a standard format so that the training is improved, accurate, and faster. So you just have to scale the data once. Doesn't matter what scaler you are using. Just make sure to initialize the scaler with the training data and then use the same parameters to scale the test data. The z-score ...


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YELP Dataset (200k images) used to take 5 hr for training to identify Five (5) classes on GPU - Nvidia 1080 Ti with 11 GB RAM. So I guess in your case it will take days. Again it will depend on the type of your GPU configuration and type of Architecture you will be using.


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In general, you can learn any parameter of the network, provided you can find the partial derivative of the loss function with respect to the desired parameter. Given that $\rho$ is assumed to be differentiable (as the authors state in the paper), you can take the partial derivative of the loss function with respect to the parameter $\alpha$. In this paper, ...


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General structure of an Artificial Neural Network Input Layer + Hidden Layers + Output Layer If there are more hidden layers in the artificial neural network, then the neural network is called as Deep Neural network. How many exactly constitute a deep neural network is a point of debate, but in general, the more the hidden layers, the deep is the neural ...


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