Deep_Learning.svg

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stroke="rgb(127, 127, 127)" stroke-width="1.5"></path><path class="markmap-link" data-depth="5" data-path="1.9.28.29.30" d="M1168,-894.375C1208,-894.375,1208,-907.375,1248,-907.375" stroke="rgb(140, 86, 75)" stroke-width="1.5"></path><path class="markmap-link" data-depth="5" data-path="1.9.28.29.31" d="M1168,-894.375C1208,-894.375,1208,-882.375,1248,-882.375" stroke="rgb(227, 119, 194)" stroke-width="1.5"></path><path class="markmap-link" data-depth="5" data-path="1.9.28.32.33" d="M1228,-851.375C1268,-851.375,1268,-851.875,1308,-851.875" stroke="rgb(188, 189, 34)" stroke-width="1.5"></path><path class="markmap-link" data-depth="5" data-path="1.9.28.34.35" d="M1281,-820.875C1321,-820.875,1321,-821.375,1361,-821.375" stroke="rgb(31, 119, 180)" stroke-width="1.5"></path><path class="markmap-link" data-depth="5" data-path="1.9.28.36.37" d="M1342,-790.375C1382,-790.375,1382,-790.875,1422,-790.875" stroke="rgb(44, 160, 44)" stroke-width="1.5"></path><path class="markmap-link" data-depth="5" data-path="1.9.28.38.39" d="M1277,-759.875C1317,-759.875,1317,-760.375,1357,-760.375" stroke="rgb(148, 103, 189)" stroke-width="1.5"></path><g data-depth="5" data-path="1.9.28.38.39" transform="translate(1357, -780.375)" class="markmap-node"><line x1="-1" x2="413" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="395" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Often provides fast convergence and good generalization</div></foreignObject></g><g data-depth="5" data-path="1.9.28.36.37" transform="translate(1422, -810.875)" class="markmap-node"><line x1="-1" x2="291" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="273" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Useful for training with large batch sizes</div></foreignObject></g><g data-depth="5" data-path="1.9.28.34.35" transform="translate(1361, -841.375)" class="markmap-node"><line x1="-1" x2="469" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="451" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Maintains a slow weights copy, potentially improving convergence</div></foreignObject></g><g data-depth="5" data-path="1.9.28.32.33" transform="translate(1308, -871.875)" class="markmap-node"><line x1="-1" x2="309" y1="20" y2="20" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="291" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Often performs better than standard Adam</div></foreignObject></g><g data-depth="5" data-path="1.9.28.29.31" transform="translate(1248, -902.375)" class="markmap-node"><line x1="-1" x2="271" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="253" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Default choice for many practitioners</div></foreignObject></g><g data-depth="5" data-path="1.9.28.29.30" transform="translate(1248, -927.375)" class="markmap-node"><line x1="-1" x2="351" y1="20" y2="20" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="333" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Combines ideas from RMSprop and momentum</div></foreignObject></g><g data-depth="5" data-path="1.9.10.21.22" transform="translate(1220, -1066.875)" class="markmap-node"><line x1="-1" x2="240" y1="20" y2="20" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="222" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Smooth, non-monotonic function</div></foreignObject></g><g data-depth="5" data-path="1.9.10.19.20" transform="translate(1519, -1097.375)" class="markmap-node"><line x1="-1" x2="267" y1="20" y2="20" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="249" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Particularly useful for deep networks</div></foreignObject></g><g data-depth="5" data-path="1.9.10.17.18" transform="translate(1465, -1127.875)" class="markmap-node"><line x1="-1" x2="341" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="323" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Smooth function, can produce negative outputs</div></foreignObject></g><g data-depth="5" data-path="1.9.10.14.15" transform="translate(1515, -1184.375)" class="markmap-node"><line x1="-1" x2="266" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="248" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Addresses the dying ReLU problem</div></foreignObject></g><g data-depth="5" data-path="1.9.10.11.13" transform="translate(1185, -1214.875)" class="markmap-node"><line x1="-1" x2="351" y1="20" y2="20" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="333" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Cons: "Dying ReLU" problem for negative inputs</div></foreignObject></g><g data-depth="5" data-path="1.9.10.11.12" transform="translate(1185, -1239.875)" class="markmap-node"><line x1="-1" x2="511" y1="20" y2="20" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="493" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Pros: No vanishing gradient for positive values, computationally efficient</div></foreignObject></g><g data-depth="4" data-path="1.123.158.161" transform="translate(848, 1656.125)" class="markmap-node"><line x1="-1" x2="537" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="519" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Contribute to open-source projects to learn from and collaborate with others</div></foreignObject></g><g data-depth="4" data-path="1.123.158.160" transform="translate(848, 1631.125)" class="markmap-node"><line x1="-1" x2="601" y1="20" y2="20" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="583" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Join discussion forums like /r/MachineLearning or participate in Kaggle competitions</div></foreignObject></g><g data-depth="4" data-path="1.123.158.159" transform="translate(848, 1606.125)" class="markmap-node"><line x1="-1" x2="431" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="413" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Follow top conferences (NeurIPS, ICML, ICLR, CVPR, ACL)</div></foreignObject></g><g data-depth="4" data-path="1.123.154.157" transform="translate(894, 1576.125)" class="markmap-node"><line x1="-1" x2="459" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="441" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Consider the environmental impact of large-scale model training</div></foreignObject></g><g data-depth="4" data-path="1.123.154.156" transform="translate(894, 1551.125)" class="markmap-node"><line x1="-1" x2="392" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="374" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Implement fairness constraints in your training process</div></foreignObject></g><g data-depth="4" data-path="1.123.154.155" transform="translate(894, 1526.125)" class="markmap-node"><line x1="-1" x2="282" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="264" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Regularly audit your models for biases</div></foreignObject></g><g data-depth="4" data-path="1.123.150.153" transform="translate(837, 1496.125)" class="markmap-node"><line x1="-1" x2="447" y1="20" y2="20" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="429" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Consider using Docker containers for consistent environments</div></foreignObject></g><g data-depth="4" data-path="1.123.150.152" transform="translate(837, 1471.125)" class="markmap-node"><line x1="-1" x2="394" y1="20" y2="20" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="376" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Record software versions and hardware specifications</div></foreignObject></g><g data-depth="4" data-path="1.123.150.151" transform="translate(837, 1446.125)" class="markmap-node"><line x1="-1" x2="354" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="336" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Set random seeds for all sources of randomness</div></foreignObject></g><g data-depth="4" data-path="1.123.146.149" transform="translate(910, 1416.125)" class="markmap-node"><line x1="-1" x2="460" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="442" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Document your experiments thoroughly, including failed attempts</div></foreignObject></g><g data-depth="4" data-path="1.123.146.148" transform="translate(910, 1391.125)" class="markmap-node"><line x1="-1" x2="365" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="347" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Implement version control for datasets and models</div></foreignObject></g><g data-depth="4" data-path="1.123.146.147" transform="translate(910, 1366.125)" class="markmap-node"><line x1="-1" x2="568" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="550" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use tools like MLflow, Weights &amp; Biases, or Neptune.ai for tracking experiments</div></foreignObject></g><g data-depth="4" data-path="1.123.142.145" transform="translate(991, 1336.125)" class="markmap-node"><line x1="-1" x2="502" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="484" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Consider neural architecture search for hardware-aware model design</div></foreignObject></g><g data-depth="4" data-path="1.123.142.144" transform="translate(991, 1311.125)" class="markmap-node"><line x1="-1" x2="360" y1="20" y2="20" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="342" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Explore lottery ticket hypothesis for model pruning</div></foreignObject></g><g data-depth="4" data-path="1.123.142.143" transform="translate(991, 1286.125)" class="markmap-node"><line x1="-1" x2="508" y1="20" y2="20" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="490" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use techniques like quantization-aware training for efficient deployment</div></foreignObject></g><g data-depth="4" data-path="1.123.138.141" transform="translate(887, 1256.125)" class="markmap-node"><line x1="-1" x2="665" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="647" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Consider using TFRecords (for TensorFlow) or Lightning Data Modules (for PyTorch Lightning)</div></foreignObject></g><g data-depth="4" data-path="1.123.138.140" transform="translate(887, 1231.125)" class="markmap-node"><line x1="-1" x2="345" y1="20" y2="20" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="327" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Implement prefetching and parallel data loading</div></foreignObject></g><g data-depth="4" data-path="1.123.138.139" transform="translate(887, 1206.125)" class="markmap-node"><line x1="-1" x2="501" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="483" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use data loading libraries like DALI for GPU-accelerated data loading</div></foreignObject></g><g data-depth="4" data-path="1.123.134.137" transform="translate(880, 1176.125)" class="markmap-node"><line x1="-1" x2="435" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="417" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Explore counterfactual explanations for individual predictions</div></foreignObject></g><g data-depth="4" data-path="1.123.134.136" transform="translate(880, 1151.125)" class="markmap-node"><line x1="-1" x2="525" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="507" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use SHAP (SHapley Additive exPlanations) values for feature importance</div></foreignObject></g><g data-depth="4" data-path="1.123.134.135" transform="translate(880, 1126.125)" class="markmap-node"><line x1="-1" x2="318" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="300" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Implement Grad-CAM for CNN visualization</div></foreignObject></g><g data-depth="4" data-path="1.123.130.133" transform="translate(981, 1096.125)" class="markmap-node"><line x1="-1" x2="521" y1="20" y2="20" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="503" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use tools like DeepCheck for systematic testing of deep learning models</div></foreignObject></g><g data-depth="4" data-path="1.123.130.132" transform="translate(981, 1071.125)" class="markmap-node"><line x1="-1" x2="403" y1="20" y2="20" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="385" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Implement unit tests for custom layers and loss functions</div></foreignObject></g><g data-depth="4" data-path="1.123.130.131" transform="translate(981, 1046.125)" class="markmap-node"><line x1="-1" x2="424" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="406" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use gradient and activation histograms to diagnose issues</div></foreignObject></g><g data-depth="4" data-path="1.123.127.129" transform="translate(908, 1016.125)" class="markmap-node"><line x1="-1" x2="319" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="301" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Be aware of potential numerical instabilities</div></foreignObject></g><g data-depth="4" data-path="1.123.127.128" transform="translate(908, 991.125)" class="markmap-node"><line x1="-1" x2="498" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="480" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use FP16 or bfloat16 to speed up training and reduce memory usage</div></foreignObject></g><g data-depth="4" data-path="1.123.124.126" transform="translate(944, 961.125)" class="markmap-node"><line x1="-1" x2="443" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="425" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Consider multi-fidelity optimization techniques like Hyperband</div></foreignObject></g><g data-depth="4" data-path="1.123.124.125" transform="translate(944, 936.125)" class="markmap-node"><line x1="-1" x2="409" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="391" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Use Bayesian optimization tools like Optuna or Ray Tune</div></foreignObject></g><g data-depth="4" data-path="1.106.119.122" transform="translate(820, 905.125)" class="markmap-node"><line x1="-1" x2="559" y1="21" y2="21" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="541" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Memory-based approaches</strong>: Store and replay examples from previous tasks</div></foreignObject></g><g data-depth="4" data-path="1.106.119.121" transform="translate(820, 879.125)" class="markmap-node"><line x1="-1" x2="502" y1="21" y2="21" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="484" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Progressive Neural Networks</strong>: Grow network capacity for new tasks</div></foreignObject></g><g data-depth="4" data-path="1.106.119.120" transform="translate(820, 853.125)" class="markmap-node"><line x1="-1" x2="509" y1="21" y2="21" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="491" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Elastic Weight Consolidation (EWC)</strong>: Prevent catastrophic forgetting</div></foreignObject></g><g data-depth="4" data-path="1.106.115.118" transform="translate(828, 823.125)" class="markmap-node"><line x1="-1" x2="293" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="275" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Differential privacy in federated learning</div></foreignObject></g><g data-depth="4" data-path="1.106.115.117" transform="translate(828, 798.125)" class="markmap-node"><line x1="-1" x2="225" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="207" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Secure aggregation protocols</div></foreignObject></g><g data-depth="4" data-path="1.106.115.116" transform="translate(828, 773.125)" class="markmap-node"><line x1="-1" x2="327" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="309" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Techniques for training on decentralized data</div></foreignObject></g><g data-depth="4" data-path="1.106.111.114" transform="translate(922, 742.125)" class="markmap-node"><line x1="-1" x2="570" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="552" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Once-for-All Networks</strong>: Train a single network to support multiple sub-networks</div></foreignObject></g><g data-depth="4" data-path="1.106.111.113" transform="translate(922, 716.125)" class="markmap-node"><line x1="-1" x2="289" y1="21" y2="21" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="271" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>ProxylessNAS</strong>: Memory-efficient NAS</div></foreignObject></g><g data-depth="4" data-path="1.106.111.112" transform="translate(922, 690.125)" class="markmap-node"><line x1="-1" x2="312" y1="21" y2="21" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="294" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>DARTS</strong>: Differentiable architecture search</div></foreignObject></g><g data-depth="4" data-path="1.106.107.110" transform="translate(791, 659.125)" class="markmap-node"><line x1="-1" x2="271" y1="21" y2="21" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="253" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Reptile</strong>: Simplified version of MAML</div></foreignObject></g><g data-depth="4" data-path="1.106.107.109" transform="translate(791, 633.125)" class="markmap-node"><line x1="-1" x2="390" y1="21" y2="21" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="372" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Prototypical Networks</strong>: Few-shot learning technique</div></foreignObject></g><g data-depth="4" data-path="1.106.107.108" transform="translate(791, 607.125)" class="markmap-node"><line x1="-1" x2="562" y1="21" y2="21" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="544" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Model-Agnostic Meta-Learning (MAML)</strong>: Learn to quickly adapt to new tasks</div></foreignObject></g><g data-depth="4" data-path="1.90.101.105" transform="translate(1051, 576.125)" class="markmap-node"><line x1="-1" x2="530" y1="21" y2="21" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="512" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>AlphaZero</strong>: Self-play reinforcement learning for perfect information games</div></foreignObject></g><g data-depth="4" data-path="1.90.101.104" transform="translate(1051, 550.125)" class="markmap-node"><line x1="-1" x2="456" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="438" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Rainbow DQN</strong>: Combination of multiple improvements to DQN</div></foreignObject></g><g data-depth="4" data-path="1.90.101.103" transform="translate(1051, 524.125)" class="markmap-node"><line x1="-1" x2="529" y1="21" y2="21" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="511" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Soft Actor-Critic (SAC)</strong>: Off-policy algorithm for continuous action spaces</div></foreignObject></g><g data-depth="4" data-path="1.90.101.102" transform="translate(1051, 498.125)" class="markmap-node"><line x1="-1" x2="499" y1="21" y2="21" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="481" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Proximal Policy Optimization (PPO)</strong>: Stable policy gradient method</div></foreignObject></g><g data-depth="4" data-path="1.90.96.100" transform="translate(1080, 467.125)" class="markmap-node"><line x1="-1" x2="409" y1="21" y2="21" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="391" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>ELECTRA</strong>: Efficiently learning an encoder for NLP tasks</div></foreignObject></g><g data-depth="4" data-path="1.90.96.99" transform="translate(1080, 441.125)" class="markmap-node"><line x1="-1" x2="270" y1="21" y2="21" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="252" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>T5</strong>: Text-to-Text Transfer Transformer</div></foreignObject></g><g data-depth="4" data-path="1.90.96.98" transform="translate(1080, 415.125)" class="markmap-node"><line x1="-1" x2="337" y1="21" y2="21" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="319" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>GPT series</strong>: Autoregressive language models</div></foreignObject></g><g data-depth="4" data-path="1.90.96.97" transform="translate(1080, 389.125)" class="markmap-node"><line x1="-1" x2="413" y1="21" y2="21" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="395" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>BERT and its variants</strong>: RoBERTa, ALBERT, DistilBERT</div></foreignObject></g><g data-depth="4" data-path="1.90.91.95" transform="translate(1046, 358.125)" class="markmap-node"><line x1="-1" x2="516" y1="21" y2="21" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="498" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>MoCo and SimCLR</strong>: Self-supervised learning for visual representations</div></foreignObject></g><g data-depth="4" data-path="1.90.91.94" transform="translate(1046, 332.125)" class="markmap-node"><line x1="-1" x2="493" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="475" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Swin Transformer</strong>: Hierarchical Transformer for various vision tasks</div></foreignObject></g><g data-depth="4" data-path="1.90.91.93" transform="translate(1046, 306.125)" class="markmap-node"><line x1="-1" x2="582" y1="21" y2="21" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="564" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>DETR (DEtection TRansformer)</strong>: End-to-end object detection with Transformers</div></foreignObject></g><g data-depth="4" data-path="1.90.91.92" transform="translate(1046, 280.125)" class="markmap-node"><line x1="-1" x2="480" y1="21" y2="21" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="462" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Vision Transformers (ViT)</strong>: Applying Transformers to image tasks</div></foreignObject></g><g data-depth="4" data-path="1.79.86.89" transform="translate(867, 249.125)" class="markmap-node"><line x1="-1" x2="477" y1="21" y2="21" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="459" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Bayesian Neural Networks</strong>: Explicitly model weight uncertainties</div></foreignObject></g><g data-depth="4" data-path="1.79.86.88" transform="translate(867, 223.125)" class="markmap-node"><line x1="-1" x2="452" y1="21" y2="21" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="434" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Deep Ensembles</strong>: Train multiple models for robust predictions</div></foreignObject></g><g data-depth="4" data-path="1.79.86.87" transform="translate(867, 197.125)" class="markmap-node"><line x1="-1" x2="566" y1="21" y2="21" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="548" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Monte Carlo Dropout</strong>: Use dropout at inference time for uncertainty estimation</div></foreignObject></g><g data-depth="4" data-path="1.79.80.85" transform="translate(967, 166.125)" class="markmap-node"><line x1="-1" x2="647" y1="21" y2="21" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="629" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Mixup</strong> and <strong>CutMix</strong>: Data augmentation techniques that combine multiple training examples</div></foreignObject></g><g data-depth="4" 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y2="21" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="398" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Dropblock</strong>: Structured dropout for convolutional networks</div></foreignObject></g><g data-depth="4" data-path="1.79.80.81" transform="translate(967, 62.125)" class="markmap-node"><line x1="-1" x2="528" y1="21" y2="21" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="510" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Spectral Normalization</strong>: Constrains the spectral norm of weight matrices</div></foreignObject></g><g data-depth="4" data-path="1.63.75.78" transform="translate(1062, 32.125)" class="markmap-node"><line x1="-1" x2="274" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="256" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Scaling learning rates with batch size</div></foreignObject></g><g data-depth="4" data-path="1.63.75.77" transform="translate(1062, 7.125)" class="markmap-node"><line x1="-1" x2="259" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="241" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Effective batch size considerations</div></foreignObject></g><g data-depth="4" data-path="1.63.75.76" transform="translate(1062, -17.875)" class="markmap-node"><line x1="-1" x2="353" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="335" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Techniques for training with limited GPU memory</div></foreignObject></g><g data-depth="4" data-path="1.63.70.74" transform="translate(951, -48.875)" class="markmap-node"><line x1="-1" x2="496" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="478" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Weight Standardization</strong>: Standardize weights instead of activations</div></foreignObject></g><g data-depth="4" data-path="1.63.70.73" transform="translate(951, -74.875)" class="markmap-node"><line x1="-1" x2="572" y1="21" y2="21" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="554" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Group Normalization</strong>: Compromise between Layer and Instance Normalization</div></foreignObject></g><g data-depth="4" data-path="1.63.70.72" transform="translate(951, -100.875)" class="markmap-node"><line x1="-1" x2="399" y1="21" y2="21" stroke="rgb(127, 127, 127)" 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training stability in Transformers</div></foreignObject></g><g data-depth="4" data-path="1.40.52.54" transform="translate(985, -479.875)" class="markmap-node"><line x1="-1" x2="718" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="700" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Positional Encoding</strong>: Techniques beyond sinusoidal encoding (e.g., learned positional embeddings)</div></foreignObject></g><g data-depth="4" data-path="1.40.52.53" transform="translate(985, -505.875)" class="markmap-node"><line x1="-1" x2="592" y1="21" y2="21" stroke="rgb(188, 189, 34)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="574" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Multi-Head Attention</strong>: Allows attending to different parts of the input simultaneously</div></foreignObject></g><g data-depth="4" 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x2="615" y1="21" y2="21" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="597" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Quasi-Recurrent Neural Networks (QRNNs)</strong>: Combine benefits of CNNs and RNNs</div></foreignObject></g><g data-depth="4" data-path="1.40.47.48" transform="translate(1068, -614.875)" class="markmap-node"><line x1="-1" x2="638" y1="21" y2="21" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="620" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Attention Mechanisms in RNNs</strong>: Allows focusing on specific parts of the input sequence</div></foreignObject></g><g data-depth="4" data-path="1.40.41.46" transform="translate(1118, -645.875)" class="markmap-node"><line x1="-1" x2="520" y1="21" y2="21" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="502" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Inverted Residuals</strong>: Used in MobileNetV2 for efficient feature extraction</div></foreignObject></g><g data-depth="4" data-path="1.40.41.45" transform="translate(1118, -671.875)" class="markmap-node"><line x1="-1" x2="570" y1="21" y2="21" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="552" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Squeeze-and-Excitation Blocks</strong>: Model interdependencies between channels</div></foreignObject></g><g data-depth="4" data-path="1.40.41.44" transform="translate(1118, -697.875)" class="markmap-node"><line x1="-1" x2="478" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="460" height="21" style="opacity: 1;"><div 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for efficiency</div></foreignObject></g><g data-depth="4" data-path="1.9.28.38" transform="translate(900, -780.875)" class="markmap-node"><line x1="-1" x2="379" y1="21" y2="21" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="377" cy="21" r="6" stroke="rgb(214, 39, 40)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="361" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Ranger</strong>: Combines Rectified Adam and Lookahead</div></foreignObject></g><g data-depth="4" data-path="1.9.28.36" transform="translate(900, -811.375)" class="markmap-node"><line x1="-1" x2="444" y1="21" y2="21" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="442" cy="21" r="6" stroke="rgb(255, 127, 14)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="426" height="21" style="opacity: 1;"><div 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Loss</strong>: Useful for image segmentation tasks</div></foreignObject></g><g data-depth="4" data-path="1.9.23.24" transform="translate(979, -1036.375)" class="markmap-node"><line x1="-1" x2="432" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="414" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Focal Loss</strong>: Addresses class imbalance in object detection</div></foreignObject></g><g data-depth="4" data-path="1.9.10.21" transform="translate(936, -1067.375)" class="markmap-node"><line x1="-1" x2="206" y1="21" y2="21" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="204" cy="21" r="6" stroke="rgb(227, 119, 194)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="188" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Swish</strong>: f(x) = x * sigmoid(x)</div></foreignObject></g><g data-depth="4" data-path="1.9.10.19" transform="translate(936, -1097.875)" class="markmap-node"><line x1="-1" x2="505" y1="21" y2="21" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="503" cy="21" r="6" stroke="rgb(148, 103, 189)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="487" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Scaled Exponential Linear Unit (SELU)</strong>: Self-normalizing properties</div></foreignObject></g><g data-depth="4" data-path="1.9.10.17" transform="translate(936, -1128.375)" class="markmap-node"><line x1="-1" x2="451" y1="21" y2="21" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="449" cy="21" r="6" stroke="rgb(44, 160, 44)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="433" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Exponential Linear Unit (ELU)</strong>: f(x) = x if x &gt; 0, else α(e^x - 1)</div></foreignObject></g><g data-depth="4" data-path="1.9.10.16" transform="translate(936, -1158.875)" class="markmap-node"><line x1="-1" x2="488" y1="21" y2="21" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="470" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Parametric ReLU (PReLU)</strong>: Learns the α parameter during training</div></foreignObject></g><g data-depth="4" data-path="1.9.10.14" transform="translate(936, -1184.875)" class="markmap-node"><line x1="-1" x2="501" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="499" cy="21" r="6" stroke="rgb(23, 190, 207)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="483" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>Leaky ReLU</strong>: f(x) = max(αx, x), where α is a small constant (e.g., 0.01)</div></foreignObject></g><g data-depth="4" data-path="1.9.10.11" transform="translate(936, -1227.875)" class="markmap-node"><line x1="-1" x2="171" y1="21" y2="21" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="169" cy="21" r="6" stroke="rgb(227, 119, 194)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="153" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml"><strong>ReLU</strong>: f(x) = max(0, x)</div></foreignObject></g><g data-depth="4" data-path="1.2.6.8" transform="translate(941, -1269.875)" class="markmap-node"><line x1="-1" x2="342" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="324" height="20" style="opacity: 1;"><div 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class="markmap-node"><line x1="-1" x2="290" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><foreignObject class="markmap-foreign" x="8" y="0" width="272" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Cybenko's theorem and its implications</div></foreignObject></g><g data-depth="3" data-path="1.123.158" transform="translate(620, 1630.625)" class="markmap-node"><line x1="-1" x2="150" y1="21" y2="21" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="148" cy="21" r="6" stroke="rgb(214, 39, 40)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="132" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="237,238"><strong>Staying Updated</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.154" transform="translate(620, 1550.625)" class="markmap-node"><line x1="-1" x2="196" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="194" cy="21" r="6" stroke="rgb(23, 190, 207)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="178" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="232,233"><strong>Ethical Considerations</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.150" transform="translate(620, 1470.625)" class="markmap-node"><line x1="-1" x2="139" y1="21" y2="21" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="137" cy="21" r="6" stroke="rgb(140, 86, 75)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="121" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="227,228"><strong>Reproducibility</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.146" transform="translate(620, 1390.625)" class="markmap-node"><line x1="-1" x2="212" y1="21" y2="21" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="210" cy="21" r="6" stroke="rgb(255, 127, 14)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="194" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="222,223"><strong>Experiment Management</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.142" transform="translate(620, 1310.625)" class="markmap-node"><line x1="-1" x2="293" y1="21" y2="21" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="291" cy="21" r="6" stroke="rgb(127, 127, 127)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="275" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="217,218"><strong>Model Distillation and Compression</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.138" transform="translate(620, 1230.625)" class="markmap-node"><line x1="-1" x2="189" y1="21" y2="21" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="187" cy="21" r="6" stroke="rgb(214, 39, 40)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="171" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="212,213"><strong>Efficient Data Pipeline</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.134" transform="translate(620, 1150.625)" class="markmap-node"><line x1="-1" x2="182" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="180" cy="21" r="6" stroke="rgb(23, 190, 207)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="164" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="207,208"><strong>Model Interpretability</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.130" transform="translate(620, 1070.625)" class="markmap-node"><line x1="-1" x2="283" y1="21" y2="21" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="281" cy="21" r="6" stroke="rgb(140, 86, 75)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="265" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="202,203"><strong>Debugging Deep Neural Networks</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.127" transform="translate(620, 1003.125)" class="markmap-node"><line x1="-1" x2="210" y1="21" y2="21" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="208" cy="21" r="6" stroke="rgb(44, 160, 44)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="192" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="198,199"><strong>Mixed Precision Training</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.123.124" transform="translate(620, 948.125)" class="markmap-node"><line x1="-1" x2="246" y1="21" y2="21" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="244" cy="21" r="6" stroke="rgb(23, 190, 207)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="228" height="21" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">
<p data-lines="194,195"><strong>Hyperparameter Optimization</strong>:</p></div></foreignObject></g><g data-depth="3" data-path="1.106.119" transform="translate(593, 879.625)" class="markmap-node"><line x1="-1" x2="149" y1="20" y2="20" stroke="rgb(148, 103, 189)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="147" cy="20" r="6" stroke="rgb(148, 103, 189)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="131" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Continual Learning</div></foreignObject></g><g data-depth="3" data-path="1.106.115" transform="translate(593, 798.125)" class="markmap-node"><line x1="-1" x2="157" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="155" cy="20" r="6" stroke="rgb(31, 119, 180)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="139" height="20" style="opacity: 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style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Natural Language Processing: Advanced Methods</div></foreignObject></g><g data-depth="3" data-path="1.90.91" transform="translate(636, 319.625)" class="markmap-node"><line x1="-1" x2="332" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="330" cy="20" r="6" stroke="rgb(227, 119, 194)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="314" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Computer Vision: State-of-the-Art Techniques</div></foreignObject></g><g data-depth="3" data-path="1.79.86" transform="translate(615, 223.625)" class="markmap-node"><line x1="-1" x2="174" y1="20" y2="20" stroke="rgb(255, 127, 14)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="172" cy="20" r="6" stroke="rgb(255, 127, 14)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="156" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Uncertainty Estimation</div></foreignObject></g><g data-depth="3" data-path="1.79.80" transform="translate(615, 114.625)" class="markmap-node"><line x1="-1" x2="274" y1="20" y2="20" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="272" cy="20" r="6" stroke="rgb(140, 86, 75)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="256" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Advanced Regularization Techniques</div></foreignObject></g><g data-depth="3" data-path="1.63.75" transform="translate(631, 7.125)" class="markmap-node"><line x1="-1" x2="353" y1="20" y2="20" stroke="rgb(31, 119, 180)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="351" cy="20" r="6" stroke="rgb(31, 119, 180)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="335" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Gradient Accumulation and Large Batch Training</div></foreignObject></g><g data-depth="3" data-path="1.63.70" transform="translate(631, -87.375)" class="markmap-node"><line x1="-1" x2="242" y1="20" y2="20" stroke="rgb(140, 86, 75)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="240" cy="20" r="6" stroke="rgb(140, 86, 75)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="224" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Batch Normalization Alternatives</div></foreignObject></g><g data-depth="3" data-path="1.63.64" transform="translate(631, -209.375)" class="markmap-node"><line x1="-1" x2="353" y1="20" y2="20" stroke="rgb(23, 190, 207)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="351" cy="20" r="6" stroke="rgb(23, 190, 207)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="335" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Learning Rate Schedules: Advanced Techniques</div></foreignObject></g><g data-depth="3" data-path="1.40.58" transform="translate(595, -331.375)" class="markmap-node"><line x1="-1" x2="362" y1="20" y2="20" stroke="rgb(214, 39, 40)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="360" cy="20" r="6" stroke="rgb(214, 39, 40)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="344" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Graph Neural Networks (GNNs): Advanced Topics</div></foreignObject></g><g data-depth="3" data-path="1.40.52" transform="translate(595, -453.375)" class="markmap-node"><line x1="-1" x2="312" y1="20" y2="20" stroke="rgb(127, 127, 127)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="310" cy="20" r="6" stroke="rgb(127, 127, 127)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="294" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Transformer Architecture: In-depth Analysis</div></foreignObject></g><g data-depth="3" data-path="1.40.47" transform="translate(595, -575.375)" class="markmap-node"><line x1="-1" x2="395" y1="20" y2="20" stroke="rgb(44, 160, 44)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="393" cy="20" r="6" stroke="rgb(44, 160, 44)" fill="var(--markmap-circle-open-bg)"></circle><foreignObject class="markmap-foreign" x="8" y="0" width="377" height="20" style="opacity: 1;"><div xmlns="http://www.w3.org/1999/xhtml">Recurrent Neural Networks: Beyond LSTMs and GRUs</div></foreignObject></g><g data-depth="3" data-path="1.40.41" transform="translate(595, -697.375)" class="markmap-node"><line x1="-1" x2="445" y1="20" y2="20" stroke="rgb(227, 119, 194)" stroke-width="1.5"></line><circle stroke-width="1.5" cx="443" 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