|
RAR-files |
| jgt-9781789950748.rar |
15,000,000 |
17A40FE4 |
| jgt-9781789950748.r00 |
15,000,000 |
7F3A0125 |
| jgt-9781789950748.r01 |
15,000,000 |
818EFA44 |
| jgt-9781789950748.r02 |
15,000,000 |
D4098765 |
| jgt-9781789950748.r03 |
15,000,000 |
C288F85C |
| jgt-9781789950748.r04 |
15,000,000 |
194E604D |
| jgt-9781789950748.r05 |
15,000,000 |
9A1E0E87 |
| jgt-9781789950748.r06 |
15,000,000 |
0F26007C |
| jgt-9781789950748.r07 |
15,000,000 |
142C08E8 |
| jgt-9781789950748.r08 |
15,000,000 |
FA296CEF |
| jgt-9781789950748.r09 |
15,000,000 |
C2B5066A |
| jgt-9781789950748.r10 |
15,000,000 |
1C5193FF |
| jgt-9781789950748.r11 |
15,000,000 |
5BB8AE24 |
| jgt-9781789950748.r12 |
15,000,000 |
44EBD90B |
| jgt-9781789950748.r13 |
15,000,000 |
089E604F |
| jgt-9781789950748.r14 |
15,000,000 |
FB5EC82E |
| jgt-9781789950748.r15 |
15,000,000 |
853729D2 |
| jgt-9781789950748.r16 |
15,000,000 |
130BE711 |
| jgt-9781789950748.r17 |
15,000,000 |
2ED63C16 |
| jgt-9781789950748.r18 |
15,000,000 |
817FD3E8 |
| jgt-9781789950748.r19 |
15,000,000 |
2B191322 |
| jgt-9781789950748.r20 |
15,000,000 |
F612C028 |
| jgt-9781789950748.r21 |
15,000,000 |
1361BD3B |
| jgt-9781789950748.r22 |
15,000,000 |
AF811B37 |
| jgt-9781789950748.r23 |
15,000,000 |
30D10104 |
| jgt-9781789950748.r24 |
15,000,000 |
99842601 |
| jgt-9781789950748.r25 |
15,000,000 |
A2A2FFDB |
| jgt-9781789950748.r26 |
15,000,000 |
D50759F7 |
| jgt-9781789950748.r27 |
1,768,640 |
09A5A6CA |
|
| Total size: |
421,768,640 |
|
|
| Archived
files |
|
Packt Reinforcement Learning with TensorFlow & TRFL\01.Introduction and Classic Reinforcement Learning\0101.Course Overview.mp4
[f3c379c85572d3b2]
|
57,008,670 |
2767226D |
|
Packt Reinforcement Learning with TensorFlow & TRFL\01.Introduction and Classic Reinforcement Learning\0102.Set Up and Installation.mp4
[25923b05ef10ee8f]
|
9,134,459 |
D1647D3E |
|
Packt Reinforcement Learning with TensorFlow & TRFL\01.Introduction and Classic Reinforcement Learning\0103.Getting Started with TD Learning.mp4
[b5589c06b782082a]
|
14,993,709 |
D3C66B58 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\01.Introduction and Classic Reinforcement Learning\0104.Exploiting Off-policy Efficiency Using Q Learning.mp4
[3c4fea12c3c6c0fa]
|
11,178,077 |
CFDD30A9 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\01.Introduction and Classic Reinforcement Learning\0105.Comparing On-policy Methods with SARSA and SARSE.mp4
[6ca6f2f44662f5f3]
|
10,219,276 |
ECF47F96 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\02.Deep Reinforcement Learning with Deep Q Networks and Enhancements\0201.Implementing a Deep Q Network and Applying Target Network Updates.mp4
[14361f3fb79a3cda]
|
19,660,385 |
A1E13923 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\02.Deep Reinforcement Learning with Deep Q Networks and Enhancements\0202.Modifying a DQN with Double DQN, Persistent DQN, and Huber Loss.mp4
[61695428c6f24e9f]
|
10,195,848 |
2BFD6C01 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\02.Deep Reinforcement Learning with Deep Q Networks and Enhancements\0203.Improving a DQN with Distributional Q Learning.mp4
[a07ddfebff8e3a06]
|
15,769,699 |
04D42902 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\03.Fundamentals of Deep RL Policy Gradient Methods\0301.Utilizing Policy Gradient Methods.mp4
[e90e0d69f5410e26]
|
18,799,892 |
5B219E07 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\03.Fundamentals of Deep RL Policy Gradient Methods\0302.Increasing Exploration with Policy Entropy Loss.mp4
[da0c4062a0afd940]
|
14,847,875 |
13B0D0F3 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\03.Fundamentals of Deep RL Policy Gradient Methods\0303.Applying Actor-Critic with A3C and A2C.mp4
[9df4f3c0a85c12b4]
|
11,776,339 |
D7ED3FA7 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\03.Fundamentals of Deep RL Policy Gradient Methods\0304.Performing Deterministic Policy Gradients.mp4
[10d442c294b73c5c]
|
12,427,526 |
244B4CB4 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\04.Essential RL TD(λ) Learning\0401.Deploying TD(λ).mp4
[1a7028ec8bebc579]
|
9,915,616 |
D6E971B9 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\04.Essential RL TD(λ) Learning\0402.Balancing Bias and Variance with Generalized Λ Returns.mp4
[aaaed28766187a7d]
|
9,143,139 |
AFF609E6 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\04.Essential RL TD(λ) Learning\0403.Applying Q(λ).mp4
[fd1fcfe1f8e8a418]
|
7,079,165 |
5CF46D83 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\04.Essential RL TD(λ) Learning\0404.Working with Multi-step Forward View.mp4
[38b792e69b32561c]
|
5,487,223 |
4EC6F3B9 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\05.Cutting Edge RL with Impala and Unreal\0501.Using Importance Sampling with Retrace (λ).mp4
[51f6f2be3d32163]
|
79,510,163 |
C2B066CD |
|
Packt Reinforcement Learning with TensorFlow & TRFL\05.Cutting Edge RL with Impala and Unreal\0502.Getting Started with Impala with V-Trace.mp4
[910f897bdd887354]
|
84,301,621 |
14F2F2E8 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\05.Cutting Edge RL with Impala and Unreal\0503.Augmenting an Agent with Unreal and Pixel Control.mp4
[5d23975f4c763b0e]
|
15,341,209 |
6D1777F7 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\Exercise Files\exercise_files.zip |
785,762 |
4440B742 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\01.Introduction and Classic Reinforcement Learning |
0 |
00000000 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\02.Deep Reinforcement Learning with Deep Q Networks and Enhancements |
0 |
00000000 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\03.Fundamentals of Deep RL Policy Gradient Methods |
0 |
00000000 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\04.Essential RL TD(λ) Learning |
0 |
00000000 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\05.Cutting Edge RL with Impala and Unreal |
0 |
00000000 |
|
Packt Reinforcement Learning with TensorFlow & TRFL\Exercise Files |
0 |
00000000 |
|
Packt Reinforcement Learning with TensorFlow & TRFL |
0 |
00000000 |
|
| Total size: |
417,575,653 |
|
|
