A lot depends on what you're interested in. Some papers that are runnable on a laptop CPU (so long as you stick to small image sizes/tasks): 1) Generative Adv

A lot depends on what you're interested in. Some papers that are runnable on a laptop CPU (so long as you stick to small image sizes/tasks): 1) Generative Adversarial Networks (https://arxiv.org/abs/1406.2661 https://arxiv.org/abs/1406.2661). Good practice to have a custom training loops, different optimisers and networks etc. 2) Neural Style Transfer (https://arxiv.org/abs/1508.06576 https://arxiv.org/abs/1508.06576). Nice to be able to manipulate pretrained networks and intercept intermediate layers. 3) Deep Image Prior (https://arxiv.org/abs/1711.10925 https://arxiv.org/abs/1711.10925). Nice low-data exercise in building out an autoencoder. 4) Physics Informed Neural Networks (https://arxiv.org/abs/1711.10561 https://arxiv.org/abs/1711.10561). If you're interested scientific applications, this might be fun. It's good exercise in calculating higher order derivatives of neural networks and using these in loss functions. 5) Vanilla Policy Gradient (https://arxiv.org/abs/1604.06778 https://arxiv.org/abs/1604.06778) is the easiest reinforcement learning algorithm to implement and can be used as a black-box optimiser in a lot of settings. 6) Deep Q Learning (https://arxiv.org/abs/1312.5602 https://arxiv.org/abs/1312.5602) is also not too hard to implement and was the first time I had heard about DeepMind, as well as being a foundational deep reinforcement learning paper . Open AI gym (https://github.com/openai/gym https://github.com/openai/gym) would help get started with the latter two.