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I want to reframe your question. Don't think about switching, think about adding. In data science you'll be able to go very far with either python or r but you'll go farthest with both. Python and r integrate very well, thanks to the reticulate package. I often tidy data in r because it is easier for me, train a model in python to benefit from superior ...


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Machine learning has been defined by many people in different ways. One definition says that machine learning (ML) is the field of study that gives computers the ability to learn without being explicitly programmed. Given the above definition, we might say that machine learning is geared towards problems for which we have (lots of) data (experience), from ...


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Introduction The term self-supervised learning (SSL) has been used (sometimes differently) in different contexts and fields, such as representation learning [1], neural networks, robotics [2], natural language processing, and reinforcement learning. In all cases, the basic idea is to automatically generate some kind of supervisory signal to solve some task (...


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The terms strong and weak don't actually refer to processing, or optimization power, or any interpretation leading to "strong AI" being stronger than "weak AI". It holds conveniently in practice, but the terms come from elsewhere. In 1980, John Searle coined the following statements: AI hypothesis, strong form: an AI system can think and have a mind (in the ...


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TLDR: The convolutional-neural-network is a subclass of neural-networks which have at least one convolution layer. They are great for capturing local information (e.g. neighbor pixels in an image or surrounding words in a text) as well as reducing the complexity of the model (faster training, needs fewer samples, reduces the chance of overfitting). See ...


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Of course, this type of questions will also lead to primarily opinion-based answers. Nonetheless, it is possible to enumerate the strengths and weakness of each language, with respect to machine learning, statistics, and data analysis tasks, which I will try to list below. R Strengths R was designed and developed for statisticians and data analysts, so it ...


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The difference is mostly in the number of layers. For a long time, it was believed that "1-2 hidden layers are enough for most tasks" and it was impractical to use more than that, because training neural networks can be very computationally demanding. Nowadays, computers are capable of much more, so people have started to use networks with more layers and ...


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However, both approaches appear identical to me i.e. predicting the maximum reward for an action (Q-learning) is equivalent to predicting the probability of taking the action directly (PG). Both methods are theoretically driven by the Markov Decision Process construct, and as a result use similar notation and concepts. In addition, in simple solvable ...


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You don't need a powerful language for programming AI. Most of the developers are using libraries like Keras, Torch, Caffe, Watson, TensorFlow, etc. Those low level libraries are highly optimized and handle all the tough work. They are built with high-performance languages, like C, C++. Python is just there for high level task like describing the neural ...


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What's the difference between model-free and model-based reinforcement learning? In Reinforcement Learning, the terms "model-based" and "model-free" do not refer to the use of a neural network or other statistical learning model to predict values, or even to predict next state (although the latter may be used as part of a model-based algorithm and be called ...


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Model-based reinforcement learning has an agent try to understand the world and create a model to represent it. Here the model is trying to capture 2 functions, the transition function from states $T$ and the reward function $R$. From this model, the agent has a reference and can plan accordingly. However, it is not necessary to learn a model, and the ...


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Losing games to computers because of mistakes made under time pressure was probably a thing about 20 years ago, when Kasparov lost to DeepBlue after such a mistake(correction: it was Kramnik with the blunder, not Kasparov (see edit 2)). But after Kramnik's loss in early 2000s, no world champion ever tried to play against a computer (to my knowledge). ...


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Machine learning is a subset of artificial intelligence. Roughly speaking, it corresponds to its learning side. There is no "official" definitions, boundaries are a bit fuzzy.


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Definitions of Artificial Intelligence can be categorized into four categories, Thinking Humanly, Thinking Rationally, Acting Humanly and Acting Rationally. The following picture (from Artificial Intelligence: A Modern Approach) will shed light on over these definitions: The definition which I like is by John McCarthy, "It is the science and engineering ...


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Deep learning is a specific variety of a specific type of machine learning. So it's possible to learn about deep learning without learning all of machine learning, but it requires learning some machine learning (because it is some machine learning). Machine learning refers to any technique that focuses on teaching the machine how it can learn statistical ...


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Code in AI is not in principle different from any other computer code. After all, you encode algorithms in a way that computers can process them. Having said that, there are a few points where your typical "AI Code" might be different: A lot of (especially early) AI code was more research based and exploratory, so certain programming languages were favoured ...


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Q-learning and A* can both be viewed as search algorithms, but, apart from that, they are not very similar. Q-learning is a reinforcement learning algorithm, i.e. an algorithm that attempts to find a policy or, more precisely, value function (from which the policy can be derived) by taking stochastic moves (or actions) with some policy (which is different ...


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Rick Briggs refers to the difficulty an artificial intelligence would have in detecting the true meaning of words spoken or written in one of our natural languages. Take for example an artificial intelligence attempting to determine the meaning of a sarcastic sentence. Naturally spoken, the sentence "That's just what I needed today!" can be the expression ...


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Convolutional Neural Networks (CNNs) are neural networks with architectural constraints to reduce computational complexity and ensure translational invariance (the network interprets input patterns the same regardless of translation— in terms of image recognition: a banana is a banana regardless of where it is in the image). Convolutional Neural Networks ...


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How does this method differ from the experience replay, as they both use past information in the training? What's the typical application of both techniques? Using a recurrent neural network is one way for an agent to build a model of hidden or unobserved state in order to improve its predictions when direct observations do not give enough information, but ...


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It's all about Return On Investment. If DL is "worth doing", it's not overkill. If the cost of using DL (computer cycles, storage, training time) is acceptable, and the data available to train it is plentiful, and if the marginal advantage over alternative algorithms is valuable, then DL is a win. But, as you suggest, if your problem is amenable to ...


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The machine learning is a sub-set of artificial intelligence which is only a small part of its potential. It's a specific way to implement AI largely focused on statistical/probabilistic techniques and evolutionary techniques.Q Artificial intelligence Artificial intelligence is 'the theory and development of computer systems able to perform tasks normally ...


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Deep learning is powerful but it is not a superior method than bayesian. They work well in what they are designed to do: Use deep learning: Cost for computation is much cheaper than cost of sampling (e.g: natural language processing) If you have highly non-linear problem If you want to simplify feature engineering If you don't have prior distribution (e.g: ...


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C++ is actually one of the most popular languages used in the AI/ML space. Python may be more popular in general, but as others have noted, it's actually quite common to have hybrid systems where the CPU intensive number-crunching is done in C++ and Python is used for higher level functions. Just to illustrate: http://mloss.org/software/language/c__/ http:...


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Actor-Critic is not just a single algorithm, it should be viewed as a "family" of related techniques. They're all techniques based on the policy gradient theorem, which train some form of critic that computes some form of value estimate to plug into the update rule as a lower-variance replacement for the returns at the end of an episode. They all perform "...


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Active learning (AL) is a weakly supervised learning (WSL) technique where you can have both labelled and unlabelled data [1]. The main idea behind AL is that the learner (or learning algorithm) can query an "oracle" (e.g. a human) to label some unlabelled instances. AL is similar to semi-supervised learning (SSL), which is also a WSL technique, ...


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Many terms have 'mostly' the same meanings, and so the differences are just in emphasis, perspective, or historical descent. People disagree as to which label refers to the superset or the subset; there are people who will call AI a branch of ML and people who will call ML a branch of AI. I typically hear Machine Learning used as a form of 'applied ...


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In reinforcement learning (RL), there is an agent which interacts with an environment (in time steps). At each time step, the agent decides and executes an action, $a$, on an environment, and the environment responds to the agent by moving from the current state (of the environment), $s$, to the next state (of the environment), $s'$, and by emitting a scalar ...


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There is always a lot of confusion about this concept, because the naming is misleading, given that both tree and graph searches produce a tree (from which you can derive a path) while exploring the search space, which is usually represented as a graph. Differences Firstly, we have to understand that the underlying problem (or search space) is almost ...


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Self-supervised learning is when you use some parts of the samples as labels for a task that requires a good degree of comprehension to be solved. I'll emphasize these two key points, before giving an example: Labels are extracted from the sample, so they can be generated automatically, with some very simple algorithm (maybe just random selection). The task ...


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