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Related lectures (32)
Deep Learning: Convolutional Neural Networks
Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.
Neural Networks: Logic and Applications
Explores the logic of neuronal function, the Perceptron model, deep learning applications, and levels of abstraction in neural models.
Neural Networks for NLP
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Neural Networks: Perceptron Model and Backpropagation Algorithm
Covers the perceptron model and backpropagation algorithm in neural networks.
Multi-layer Neural Networks
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Deep Learning: Data Representations and Neural Networks
Explores data representations, histograms, neural networks, and deep learning concepts.
Neural Networks: Perceptron and Backpropagation
Covers the basics of neural networks, including the perceptron model and backpropagation.
Splines and Machine Learning
Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.
Feedforward Neural Networks: Activation Functions and Backpropagation
Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.
Neural Networks: Learning Features & Linear Prediction
Explores neural networks' ability to learn features and make linear predictions, emphasizing the importance of data quantity for effective performance.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
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