Adventuresinmachinelearning.com
United States
| No | Meta Name | Value |
| 1 | viewport | width=device-width, initial-scale=1.0 |
| 2 | robots | index, follow |
| Type | Status |
| HTML 5 |  |
| Responsive Website | |
| No | Text |
| 1 | A recap of deep q learning |
| 2 | The problem with deep q learning |
| 3 | An introduction to double q reinforcement learning |
| 4 | Tensorflow 2 metrics |
| 5 | Tensorflow 2 summaries |
| 6 | Tensorflow 2 metrics and summaries – cnn example |
| 7 | Tensorflow 2 keras metrics and summaries |
| No | Text |
| 1 | Policy gradient reinforcement learning in tensorflow 2 |
| 2 | Policy gradients and their theoretical foundation |
| 3 | Finding the policy gradient |
| 4 | Calculating the policy gradient |
| 5 | Policy gradient reinforcement learning in tensorflow 2 and keras |
| 6 | Prioritised experience replay in deep q learning |
| 7 | Prioritised experience replay |
| 8 | ımplementing prioritised experience replay in tensorflow 2 |
| 9 | Prioritised experience replay atari space ınvader training results |
| 10 | Sumtree introduction in python |
| 11 | Weighted sampling and sorting |
| 12 | ıntroduction to the sumtree |
| 13 | The sumtree in python |
| 14 | Trying out the sumtree |
| 15 | Atari space ınvaders and dueling q rl in tensorflow 2 |
| 16 | Double and dueling q learning recap |
| 17 | Considerations for training in an atari environment |
| 18 | Atari space ınvaders tensorflow 2 implementation |
| 19 | Miscellaneous functions |
| 20 | The dueling q / double q training function |
| 21 | The main atari training loop |
| 22 | Space ınvader atari training results |
| 23 | Dueling q networks in tensorflow 2 |
| 24 | A recap of double q learning |
| 25 | Dueling q introduction |
| 26 | Dueling q network in tensorflow 2 |
| 27 | ıntroduction to resnet in tensorflow 2 |
| 28 | ıntroduction to the resnet architecture |
| 29 | Building resnet in tensorflow 2 |
| 30 | Resnet training and validation results |
| 31 | Double q reinforcement learning in tensorflow 2 |
| 32 | The double dqn network |
| 33 | A double q network example in tensorflow 2 |
| 34 | Double q results for a deterministic case |
| 35 | Double q results for a stochastic case |
| 36 | Transfer learning in tensorflow 2 tutorial |
| 37 | What are the benefits of transfer learning? |
| 38 | How to create a transfer learning model |
| 39 | Transfer learning in tensorflow 2 |
| 40 | An introduction to global average pooling in convolutional neural networks |
| 41 | Global average pooling |
| 42 | Global average pooling with tensorflow 2 and cats vs dogs |
| 43 | Standard fully connected cla***ifier results |
| 44 | Global average pooling results |
| 45 | Metrics and summaries in tensorflow 2 |
| No | Text |
| 1 | Eager to build deep learning systems in tensorflow 2? get the book here (14) |
| 2 | Prioritised experience replay in a double q setting |
| 3 | Drawing prioritised samples |
| 4 | ımportance sampling |
| 5 | Sampling and the sumtree data structure |
| 6 | Double q recap |
| 7 | Dueling q recap |
| 8 | Converting atari images to greyscale and reducing the image size |
| 9 | Stacking image frames |
| 10 | Model definition (2) |
| 11 | The memory cla*** |
| 12 | Other functions |
| 13 | The main dueling q training loop |
| 14 | Dueling q vs double q results |
| 15 | The degradation problem |
| 16 | The resnet solution |
| 17 | Eager to build deep learning systems? get the book here (6) |
| 18 | A smaller cnn model |
| 19 | Resnet50 transfer learning example |
| 20 | Comparing the models |
| No | Text |
| 1 | here (20) |
| 2 | Advantage function A(s, a): |
| 3 | Value function V(s): |
| 4 | It speeds up learning |
| 5 | It needs less data: |
| 6 | You can leverage the expert tuning of state-of-the-art models |
| 7 | training |
| No | Text |
| 1 | a (14) |
| 2 | s (12) |
| 3 | policy |
| 4 | directly. |
| 5 | Policy Gradient |
| 6 | expected |
| 7 | x (7) |
| 8 | trajectory |
| 9 | T (3) |
| 10 | given |
| 11 | log |
| 12 | total length of the episode |
| 13 | all subsequent states |
| 14 | full episode |
| 15 | output (3) |
| 16 | reduce_sum |
| 17 | . |
| 18 | network, |
| 19 | discounted_rewards |
| 20 | target (9) |
| 21 | rewards (2) |
| 22 | rewards[::-1] |
| 23 | for (5) |
| 24 | (discounted_rewards.reverse()). |
| 25 | train_on_batch (5) |
| 26 | done (2) |
| 27 | network (3) |
| 28 | update_network (2) |
| 29 | train_writer |
| 30 | experience replay |
| 31 | Prioritised Experience Replay (PER) |
| 32 | uniformly |
| 33 | should be |
| 34 | i. |
| 35 | primary network |
| 36 | i |
| 37 | expected value |
| 38 | skewing or biasing |
| 39 | importance sampling |
| 40 | N |
| 41 | size |
| 42 | curr_write_idx |
| 43 | update (4) |
| 44 | adjust_priority |
| 45 | available_samples |
| 46 | num_samples |
| 47 | is_weights |
| 48 | states (4) |
| 49 | next_states (4) |
| 50 | error |
| 51 | get_per_error (2) |
| 52 | append |
| 53 | all |
| 54 | value (14) |
| 55 | value = |
| 56 | left, right |
| 57 | parent (2) |
| 58 | left (2) |
| 59 | right (2) |
| 60 | create_leaf (2) |
| 61 | is_leaf |
| 62 | True |
| 63 | leaf_nodes |
| 64 | nodes (5) |
| 65 | next() |
| 66 | pair |
| 67 | next node |
| 68 | (node_1, node_2 |
| 69 | (node_3, node_4 |
| 70 | ) |
| 71 | node_1, node_2 |
| 72 | node_1 |
| 73 | node_2 |
| 74 | while len(nodes) > 1 |
| 75 | nodes[0] |
| 76 | node |
| 77 | node.left.value |
| 78 | change (3) |
| 79 | propagate_changes |
| 80 | itself |
| 81 | does |
| 82 | input |
| 83 | create_tree |
| 84 | root_node (3) |
| 85 | tree_total |
| 86 | retrieve |
| 87 | max |
| 88 | primary (2) |
| 89 | argmax (3) |
| 90 | identifiability |
| 91 | hard |
| 92 | direction |
| 93 | call (4) |
| 94 | max_memory (2) |
| 95 | self._i (3) |
| 96 | rand_idxs |
| 97 | idx – 1 – NUM_FRAMES |
| 98 | idx – 1 |
| 99 | idx – 1. |
| 100 | idx |
| 101 | choose_action |
| 102 | epsilon-greedy |
| 103 | eps, |
| 104 | train (4) |
| 105 | prim_qt (7) |
| 106 | prim_qtp1 |
| 107 | qtp1 |
| 108 | t + 1 (2) |
| 109 | updates (2) |
| 110 | valid_idxs (2) |
| 111 | target_network (5) |
| 112 | N(2, 1). |
| 113 | b (6) |
| 114 | N(1, 4) |
| 115 | a, b, |
| 116 | c. |
| 117 | b. |
| 118 | c (2) |
| 119 | t (2) |
| 120 | target network (2) |
| 121 | advantage |
| 122 | s*, |
| 123 | n (2) |
| 124 | dueling |
| 125 | dense1 |
| 126 | dense2 |
| 127 | adv_dense |
| 128 | adv_out (2) |
| 129 | (adv_out). |
| 130 | num_actions |
| 131 | v_dense |
| 132 | lambda x: x – tf.reduce_mean(x) |
| 133 | combined |
| 134 | primary_network (2) |
| 135 | degradation |
| 136 | not |
| 137 | training |
| 138 | and training |
| 139 | H(x) (3) |
| 140 | at least |
| 141 | at worst, |
| 142 | pa*** through (3) |
| 143 | make things worse |
| 144 | option |
| 145 | F(x) (4) |
| 146 | F(x) + x |
| 147 | incremental |
| 148 | Res |
| 149 | res_net_block |
| 150 | r (3) |
| 151 | E[r]. |
| 152 | biased |
| 153 | A (4) |
| 154 | E[r] |
| 155 | Ra |
| 156 | np.zeros() |
| 157 | Rc (2) |
| 158 | Rd (2) |
| 159 | max_result |
| 160 | and |
| 161 | choice |
| 162 | evaluation (2) |
| 163 | a* (2) |
| 164 | extracted from network B |
| 165 | actions |
| 166 | num_episodes |
| 167 | choose_network |
| 168 | env.step() |
| 169 | double_q (2) |
| 170 | while |
| 171 | next_state (4) |
| 172 | target_q (5) |
| 173 | batch_idxs, |
| 174 | arange |
| 175 | q_from_target (2) |
| 176 | transfer learning |
| 177 | tensorflow_datasets (3) |
| 178 | tfds.load() (2) |
| 179 | info (2) |
| 180 | cat_train, cat_valid and cat_test |
| 181 | tf.image.resize (2) |
| 182 | tf.float32 (2) |
| 183 | tf.keras.applications |
| 184 | weights |
| 185 | include_top |
| 186 | tl_model.summary() |
| 187 | somewhere, |
| 188 | fit (4) |
| 189 | update_state() (3) |
| 190 | result() (4) |
| 191 | reset_states() (2) |
| 192 | with (4) |
| 193 | step |
| 194 | iterations |
| 195 | avg_loss |
| 196 | avg_acc |
| 197 | spa**** |
| 198 | log_freq |
| 199 | epoch |
| 200 | summary_writer.as_default() |
| 201 | compile |
| 202 | keras.layers.Layer |
| 203 | layer_to_log.variables[0] |
| No | Text |
| 1 | s, independent |
| 2 | F(x) = H(x) – x. |
| 3 | while True |
| 4 | target_q |
External Link Count
: 52
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