Deep Reinforcement Learning

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Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is an perfect area of machine learning, where an audience get training by interacting with its environment to achieve aim.

Best Deep Reinforcement Learning institute in Chennai, course helps you to be introduced to the universal of reinforcement learning. You will learn how to frame reinforcement learning difficulties and start tackling classic examples like, learning to navigate in a grid-world, balancing a cart-pole and news recommendation

Reinforcement Learning coaching institute in Chennai contains such as:

  • RL is a general-purpose framework for artificial intelligence
  • The essence of an intelligent agent
  • Powerful RL requires powerful representations

THE NEURAL NETWORK:
1.building intelligent machines
2.the limits of traditional computer programs
3.the mechanics of machine learning
4.the neuron
5.expressing linear perceptrons as neurons
6.feed-forward neural networks
7.linear neurons and their limitations
8.sigmoid,tanh,and ReLU neurons
9.softmax output layers
10.looking forward

TRAINING FEED-FORWARD NEURAL NETWORKS:
1.the fast-food problem
2.gradient descent
3.the delta rule and learning rates
4.gradient descent with sigmoidal neurons
5.the backpropagation algorithm
6.stochastic and minibatch gradient descent
7.test sets,validation sets,and overfitting
8.preventing overfitting in deep neural networks
9.summary

IMPLEMENTING NEURAL NETWORKS IN TENSORFLOW:
1.what is tensorflow?
2.how does tensorflow compare to alternatives?
3.installing tensorflow
4.creating and manipulating tensorflow variables
5.tensorflow operations
6.placeholder tensors
7.sessions in tensorflow
8.navigating variable scopes and sharing variables
9.managing models over the CPU and GPU
10.specifying the logistic regression model in tensorflow
11.logging and training the logistic regression model
12.leveraging tensor board to visualize computation graphs and learning
13.building a multilayer model for MNIST in tensorflow
14.summary

BEYOND GRADIENT DESCENT:
1.the challenges with gradient descent
2.local minima in the error surface to deep networks
3.model identifiablity
4.how pesky are spurious lical minima in deep networks ?
5.falt regions in the error surface
6.when the gradient points in the wrong directions
7.momentum -based optimization
8.A brief view of second- order methods
9.learning rate adptation
10.Adagrad-accumulating historical gradients
11.RMSprop-exponentially weighted moving average of gradients
12.Adam-combining momentum and RMSprop
13. the philosophy behind optimizer selection
14.summary

CONVOLUTIONAL NEURAL NETWORKS:
1.neurons in human vision
2.the shortcomings of feature selection
3.vanilla deep neural networks don’t scale
4.filters and feature maps
5.full description of the convolutional layer
6.max pooling
7.full architectural description of convolutional networks
8.closing the loops on MNIST with convolutional networks
9.image preprocessing pipelines enble more robust models
10.accelerating training with batch noramalization
11.building a convolutional networks for CIFAR -10
12.visualizing learning in convolutional networks
13.leveraging convolutional filters to replicate artistic styles
14.learning convolutional filters for other problem domains
15.summary

EMBEDDING AND REPRESENTATION LEARNING:
1.learning lower -dimensional representations
2.principal component analysis
3.motivating the autoencoder architecture
4.implementing an autoencoder in tensorflow
5.denoising to force robust representations
6.sparsity in autoencoders
7.when context is more informative than the input vector
8.the world2 vec framework
9.implementing the skip -gram architecture
10.summary

MODELS FOR SEQUENCE ANALYSIS
1.analysing variable -lenghth inputs
2.tackling seq2seq with neural N-grams
3.implementing a part-of-speech tagger
4.dependency parsing and syntaxnet
5.beam search and global normalization
6.A case for stateful deeo learning models
7.recurrent neural networks
8.the challenges with vanishing gradients
9.log short-term memory units
10.tensorflow primitives for RNN models
11.solving seq2seq tasks with recurrent neural networks
12.augmenting recurrent networks with attention
13.dissecting a neural translation network
14.summary

MEMORY AUGMENTED NEURAL NETWORKS:
1.neural turing machines
2.attention -based memory access
3.NTM memory addressing machanism
4.differentiable neural computers
5.interference -free writing in DNCs
6.DNC memory reuse
7.temporal linking of DNC writes
8.understanding the DNC read head
9.the DNC controller network
10.visualizing the DNC in action
11.implementing the DNC in tensorflow
12.teaching a DNC to read and comprehead
13.summary

DEEP REINFORCEMENT LEARNING:
1.deep reinforcement learning masters atari games
2.what is reinforcement learning ?
3.markov desicion processes (MDP)
4.policy
5.future return
6.discounted future return
7.explore versus exploit
8.policy versus value learning
9.policy learning via policy gradients
10.pole-cart with policy gradients
11.opem AI gym
12.creating an agent
13.building the model and optimizer
14.sampling actions
15.keeping track of history
16.policyb gradient main function
17.PG agent performance on pole-cart
18.Q-learning and deep Q-networks
19.the bellman equation
20.issues with value iteration
21.approximating the Q- function
22.deep Q -network(DQN)
23.training DQN
24.learning stability
25.target Q-network
26.experience replay
27.from Q-function to policy
28.DQN and the markov assumption
29.DQN’s solution to the markov assumption
30.playing breakout with DQN
31.building our architecture
32.stacking frames
33.setting up training operations
34.updating our target Q-network
35.implementing experience replay
36.DQN main loop
37.DQN agent results on breakout
38.improving and moving beyond DQN
39.deep recurrent Q-networks (DRQN)
40.asynchronous advantage actor-critic agent(A3C)
41.UNsupervised REinforcement and auxiliary learning (UN REAL)
42.summary