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Lecture 10: Combining multiple neural networks to improve generalization Lecture 9: Ways to make neural networks generalize better

Lecture 8: More recurrent neural networks Lecture 6: Optimization: How to make the learning go faster

Lecture 5: Object recognition with neural nets Lecture 4: Learning feature vectors for words Lecture 3: The backpropagation learning proccedure Lecture 2: The Perceptron learning procedure The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods.

Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. Genre: eLearning Video / Artificial Neural Network, Machine Learning (ML) AlgorithmsLearn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. WEBRip | English | MP4 + PDF slides | 960 x 540 | AVC ~25.2 kbps | 15 fpsĪAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | ~17 hours | 979 MB Coursera - Neural Networks for Machine Learning (University of Toronto)

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