Where and why is linear and non-linear transformations is useful. What are use cases in Machine learning and in deep learning especially for computer vision.
A neural net can be seen as framework for linear transformation.
Think of the math operations which are defining the linear transformation and compare it with a neural net.
What Machine Learning Method should i Use to predict Prices like Stocks,gold and etc?
I Prefer using Python but I Can't Find the Starting Point as it Seems so Complicated to me and I've no Clue How to Start it.
Talking about the machine learning method, Regression Method is used for Price prediction as it is used to predict a continuous variable. There are wide range of techniques for regression in machine learning. Starting from simple linear regression, SVR, RandomForest, CatBoost to RNN. Based on target problem, available datasets and computing resources, one of the algorithms can be used.
Yes, Python is the best language to get started into machinbre learning. And definitely, Linear Regression is the best way to start for this regression task if you are new. Gradually, you can start exploring other techniques in scikit-learn before directly jumping into RNN. Scikit-learn is the best machine learning library from beginners to professionals.
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What's the difference between reinforcement learning, deep learning, and deep reinforcement learning? Where does Q-learning fit in?
Reinforcement learning is about teaching an agent to navigate an environment using rewards. Q-learning is one of the primary reinforcement learning methods.
Deep learning uses neural networks to achieve a certain goal, such as recognizing letters and words from images.
Deep reinforcement learning is a combination of the two, using Q-learning as a base. But instead of using actual state-value pairs, this is often used in environments where the state-action space is so large that it would take too long for Q-learning to converge. By using neural networks, we can find other state-action pairs that are similar. This “function approximation” allows effective learning in environments with very large state-action spaces.
Deep learning is a method using neural networks to make function approximators to solve various problems.
Ex: Learning a function which takes an image as input and output the bounding boxes of objects in the image.
Reinforcement learning is a field in which we have an agent and we want that agent to perform a task i.e, goal based problems where we use trial and error learning methods.
Ex: Agent learning to move from one position on grid world to a goal position without falling in a pit present in between.
Deep reinforcement learning is a way to solve goal based problems using neural networks. This is because, when we want agents to perform task in real world or current games, the state space is very big.
It takes agent very long time to even visit each state once and we cannot use look up tables to store the value functions.
So, to tackle this problem we use neural networks to approximate the state to generalize the learning process
Ex: We use DQN to solve many atari games.
Q-learning : It is a temporal difference learning method, where we have a Q-table to look for best action possible in the current state based on Q value function.
For learning Q values we use the reward and the maximum possible next state Q value.
Q-learning basically falls under Reinforcement learning and its deep reinforcement learning analog is Deep Q network (DQN).
The goal of machine learning methods is to learn rules from data and make predictions and/or decisions based on them.
The learning process can be done in a(n) supervised, semi-supervised, unsupervised, reinforcement learning fashion.
In reinforcement learning (RL), an agent interacts with an environment and learns an optimal policy, by trial and error (using reward points for successful actions and penalties for errors). It is used in sequential decision making problems [1].
Deep learning as a sub-field of machine learning is a mathematical framework for learning latent rules in the data or new representations of the data at hand. The term "deep" refer to the number of learning layers in the framework. Deep learning can be used with any of aforementioned learning strategies, i.e., supervised, semi-supervised, unsupervised, and reinforcement learning.
A deep reinforcement learning technique is obtained when deep learning is utilized by any of the components of reinforcement learning [1]. Note that Q-learning is a component of RL used to tell an agent that what action needs to be taken in what situation. Detailed information can be found in [1].
[1] Li, Yuxi. "Deep reinforcement learning: An overview." arXiv preprint arXiv:1701.07274 (2017).
Reinforcement learning refers to finish -oriented algorithms, which learn how to attain a coordination compound objective (goal) or maximize along a particular dimension over many steps. The basic theme behind Reinforcement learning is that an agentive role will learn from the environment by interacting with it and getting rewards for performing actions.
Deep Learning uses multiple layers of nonlinear processing units to extract feature and transformation
Deep Reinforcement Learning approach introduces deep neural networks to solve Reinforcement Learning problems thus they are named “deep.”
There's more distinction between reinforcement learning and supervised learning, both of which can use deep neural networks aka deep learning. In supervised learning - training set is labeled by a human (e.g. AlphaGo). In reinforcement learning (e.g. AlphaZero)- the algorithm is self-taught.
To put it in simple words,
Deep Learning - It's uses the model of neural network(mimicking the brain , neurons) and deep learning is used in image classification , data analyzing and in reinforcement learning too.
Reinforcement learning - This is a branch of machine learning, that revolves around an agent (ex: clearing robot) taking actions(ex: moving around searching trash) in it's environment(ex:home) and getting rewards(ex: collecting trash)
Deep-Reinforcement learning - This is one among the list of algorithms reinforcement learning has , this algorithm utilizes deep learning concepts.
Reinforcement learning (RL) is a type of machine learning that is mainly motivated by the feedback control of systems. RL is usually considered a type of optimal control that learns through interacting with a system/environment and getting feedback. RL usually replaces the computationally expensive dynamic programming methods with single time-step/multi time-step learning rule. Popular temporal difference methods in RL are considered somewhere in between dynamic programming and monte carlo methods. Classic RL methods use tabular algorithms that are not that scalable.
Deep learning (DL) is considered crucial part of modern machine learning (classical machine learning usually mean SVM, liner regression etc.). DL uses deep multilayered neural networks (NN) with backpropagation for learning. By using well designed deep NN networks complex input-output relations can be learned. Because of this property of approximating very complex functions DL have been extremely popular in recent years (2010-ish), especially in natural language tasks and computer vision tasks. One of the attractive aspect of DL is that these models can be end-to-end, meaning we do not need to do manual feature engineering. There are numerous types of DL algorithms, like Deep neural networs, convolutional neural networks, GRU, LSTM, GAN, attention, transfromer etc.
Deep RL uses deep NN architectures to replace the tabular methods for very high dimensional problems. Informally speaking, the controller is no longer a table look-up rather we use a deep NN as the controller. Because of leveraging deep NN in RL this is commonly known as deep RL.
roughly speaking:
deep learning uses deep neural networks to approximate complicated functions.
reinforcement learning is a branch in machine learning where your learner learns through interaction with environment. It is different from supervised or unsupervised learning.
if you use deep learning to approximate functions in reinforcement learning you call it deep reinforcement learning.
Reinforcement learning is a type of artificial intelligence that aims to model human-like decision-making. It's based on the idea that humans learn from their actions and reward themselves for doing things that are good, and punish themselves for doing things that are bad. Reinforcement learning algorithms try to replicate this process by changing the value of some variable in response to an action.
Deep learning is a type of machine learning model which uses multiple layers of processing to solve problems more effectively than traditional approaches. Deep learning models can be used for image recognition, speech recognition, and translation.
Deep reinforcement learning is a type of deep learning model that tries to solve problems by using sequences of actions called episodes to improve over time as well as by comparing results from different episodes. It's also known as Q-learning because it was first described by Richard Sutton in 1997 using the Q function (the fourth derivative).
Q-learning is a particular type of deep reinforcement learning algorithm that makes use of Q values (quantified measures) instead of actual rewards or penalties, which means it can be used without having access to real data or rewards/penalties yet still produce useful results
Is this process correct?
Suppose We have a bunch of data such as MNIST.
We just feed all these data(without label) to RBM and resample each data from trained model.
Then output can be treated as new data for classification.
Do I understand it correctly?
What is the purpose of using RBM?
You are correct, RBMs are a form of unsupervised learning algorithm that are commonly used to reduce the dimensionality of your feature space. Another common approach is to use autoencoders.
RBMs are trained using the contrastive divergence algorithm. The best overview of this algorithm comes from Geoffrey Hinton who came up with it.
https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf
A great paper about how unsupervised learning improves performance can be found at http://jmlr.org/papers/volume11/erhan10a/erhan10a.pdf. The paper shows that unsupervised learning provides better generalization and filters (if using CRBMs)
If I were to train an artificial neural network's weights using a genetic algorithm what type of learning would this be classed as? I believe it's unsupervised but does it have a name? It seems like it could fall under reinforcement learning but I'm not too sure.
Genetic algorithm itself is an optimization algorithm rather than a learning algorithm. And you probably don't want to ignore the performance of neural network and only consider the weight changes. So what type of learning is a combination of neural network and genetic algorithm depends on the learning type of neural network. A neural network can be used for supervised learning, reinforcement learning, and even unsupervised learning.
It seems such a combination applies more in reinforcement, because genetic algorithm is slower than most backpropagation-based optimization algorithms with gradient information. Updating the weights with genetic algorithm is also called neuroevolution. This post cited many research on neuroevolution over traditional neural network in case you may be interested in.
It is also possible to apply genetic algorithm to unsupervised neural network. In Shibata et al's Nonlinear backlash compensation using recurrent neural network. Unsupervised learning by genetic algorithm, the authors applied the genetic algorithm to determine the weights of the recurrent neural networks, and the approach does not need the teaching signals.