Phase 24: Advanced Deep Learning Catalog

• Total notebooks: 39

• Markdown guides: 1

Use this page to jump into the main sections of the phase.

Root Notebooks

• 00_START_HERE.ipynb

• 01_gan_mathematics.ipynb

• 02_wgan_theory_implementation.ipynb

• 03_variational_autoencoders_advanced.ipynb

• 04_neural_ode.ipynb

• 05_info_gan.ipynb

• 06_vision_transformers.ipynb

• 07_hierarchical_vae.ipynb

• 08_conditional_gan.ipynb

• 09_vq_vae.ipynb

• 10_normalizing_flows.ipynb

• 11_diffusion_models.ipynb

• 12_bert_architecture.ipynb

• 13_gpt_architecture.ipynb

• 14_efficient_transformers.ipynb

• 15_graph_neural_networks.ipynb

• 16_maml_meta_learning.ipynb

• 17_prototypical_networks.ipynb

• 18_neural_radiance_fields.ipynb

• 19_stylegan.ipynb

• 20_contrastive_learning.ipynb

• 21_adversarial_robustness.ipynb

• 22_knowledge_distillation.ipynb

• 23_point_cloud_networks.ipynb

• 24_cyclegan.ipynb

• 25_progressive_gan.ipynb

• 26_model_interpretability.ipynb

• 27_curriculum_learning.ipynb

• 28_continual_learning.ipynb

• 29_neural_architecture_search.ipynb

• 30_attention_mechanisms.ipynb

• 31_memory_networks.ipynb

• 32_capsule_networks.ipynb

• 33_score_based_models.ipynb

• 34_energy_based_models.ipynb

• 35_mixture_of_experts.ipynb

• 36_implicit_neural_representations.ipynb

• 37_gaussian_processes.ipynb

• 38_bayesian_neural_networks.ipynb