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1
Deep Reinforcement Learning with Python - Second Edition
PACKT PUBLISHING LIMITED
SUDHARSAN RAVICHANDIRAN
policy
function
network
๐๐
๐ ๐
compute
optimal
method
reward
parameter
๐ ๐ ๐
gradient
figure
critic
target
update
๐๐
episode
probability
dqn
algorithm
equation
step
values
preceding
select
iteration
๐๐
reinforcement
initialize
input
๐๐
random
maximum
gym
๐๐
methods
shown
define
epsilon
layer
objective
๐ ๐ ๐ก๐ก
replay
shows
buffer
computing
bellman
carlo
understanding
๋ :
2020
์ธ์ด:
english
ํ์ผ:
PDF, 27.31 MB
๊ฐ์ธ ํ๊ทธ:
5.0
/
5.0
english, 2020
2
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
Rowel Atienza
network
figure
generator
input
discriminator
function
equation
layer
mnist
inputs
labels
output
networks
listing
fake
latent
encoder
activation
policy
adversarial
anchor
boxes
dataset
gradient
dense
outputs
shown
models
shows
feature
vector
digit
kernel_size
batch
algorithm
shape
๐๐
decoder
accuracy
image_size
๐๐
batch_size
segmentation
functions
cnn
๐ฅ๐ฅ
filters
relu
parameters
reward
๋ :
2020
์ธ์ด:
english
ํ์ผ:
PDF, 19.06 MB
๊ฐ์ธ ํ๊ทธ:
0
/
5.0
english, 2020
3
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
Packt Publishing
Rowel Atienza
network
figure
generator
input
discriminator
function
equation
layer
mnist
inputs
labels
output
networks
listing
fake
latent
encoder
activation
policy
adversarial
anchor
boxes
dataset
gradient
dense
outputs
shown
models
shows
feature
vector
digit
kernel_size
batch
algorithm
shape
๐๐
decoder
accuracy
image_size
๐๐
batch_size
segmentation
functions
cnn
๐ฅ๐ฅ
filters
relu
parameters
reward
๋ :
2020
์ธ์ด:
english
ํ์ผ:
PDF, 20.08 MB
๊ฐ์ธ ํ๊ทธ:
5.0
/
5.0
english, 2020
4
Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
Packt Publishing
Enes Bilgin
policy
reward
methods
function
๐ ๐
reinforcement
step
values
network
๐๐
๐๐
algorithm
figure
algorithms
gradient
inventory
exploration
estimate
rllib
models
sample
policies
approach
method
markov
observations
config
environments
rewards
challenges
generalization
optimal
simulation
update
context
probability
ray
grid
๐๐๐ก๐ก
meta
approaches
discuss
neural
๐๐
random
parameters
๐๐๐๐
observation
networks
info
๋ :
2020
์ธ์ด:
english
ํ์ผ:
PDF, 14.13 MB
๊ฐ์ธ ํ๊ทธ:
5.0
/
0
english, 2020
1
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