The rise of artificial intelligence is grounded in the success of deep learning. Neural networks are a broad family of algorithms that have formed the basis for deep learning.Deep learning and deep reinforcement learning have as of late been effectively connected in an extensive variety of real-world problems.Here are 15 online courses and tutorials in deep learning and deep reinforcement learning, and applications in natural language processing (NLP), computer vision, and control systems.
The courses cover the basics of neural networks, convolutional neural networks, recurrent networks and variants, difficulties in training deep networks, unsupervised learning of representations, deep belief networks, deep Boltzmann machines, deep Q-learning, value function estimation and optimization, and Monte Carlo tree search.
Deep Learning has accomplished huge increases over other machine learning approaches on numerous troublesome learning assignments, prompting cutting edge execution crosswise over a wide range of areas.
Deep Learning does successful automatic feature extraction, reducing the need for guesswork and heuristics on this key issue.
Current programming gives adaptable designs that can be adjusted for new areas effectively.
- Deep Learning can require immense amount of training information.
- Deep Learning can require colossal amount of processing power.
- Designs can be unpredictable and regularly should be customized to a particular application.
- The subsequent models may not be effectively interpretable.