hinton-ml
size: 542.01 MB
www.changgo.xyz/?s=1chjkVu
|- 9.Why the learning works [5 min].srt - 6.00 kB
|- 9.Why the learning works [5 min].mp4 - 5.90 MB
|- 8.A geometrical view of perceptrons [6 min].srt - 8.00 kB
|- 8.A geometrical view of perceptrons [6 min].mp4 - 7.30 MB
|- 74.Shallow autoencoders for pre-training [7 mins].srt - 10.00 kB
|- 74.Shallow autoencoders for pre-training [7 mins].mp4 - 8.30 MB
|- 73.Learning binary codes for image retrieval [9 mins].srt - 13.00 kB
|- 73.Learning binary codes for image retrieval [9 mins].mp4 - 11.50 MB
|- 72.Semantic Hashing [9 mins].srt - 11.00 kB
|- 72.Semantic Hashing [9 mins].mp4 - 10.00 MB
|- 71.Deep auto encoders for document retrieval [8 mins].srt - 11.00 kB
|- 71.Deep auto encoders for document retrieval [8 mins].mp4 - 10.20 MB
|- 70.Deep auto encoders [4 mins].srt - 5.00 kB
|- 70.Deep auto encoders [4 mins].mp4 - 4.90 MB
|- 69.From PCA to autoencoders [5 mins].srt - 10.00 kB
|- 69.From PCA to autoencoders [5 mins].mp4 - 9.70 MB
|- 67.Modeling real-valued data with an RBM [10 mins].srt - 12.00 kB
|- 67.Modeling real-valued data with an RBM [10 mins].mp4 - 11.20 MB
|- 66.What happens during discriminative fine-tuning - 10.20 MB
|- 66(1).What happens during discriminative fine-tuning - 11.00 kB
|- 65.Discriminative learning for DBNs [9 mins].srt - 13.00 kB
|- 65.Discriminative learning for DBNs [9 mins].mp4 - 11.30 MB
|- 64.Learning layers of features by stacking RBMs [17 min].srt - 23.00 kB
|- 64.Learning layers of features by stacking RBMs [17 min].mp4 - 20.10 MB
|- 63.The wake-sleep algorithm [13 min].srt - 17.00 kB
|- 63.The wake-sleep algorithm [13 min].mp4 - 15.70 MB
|- 62.Learning sigmoid belief nets [12 min].srt - 15.00 kB
|- 62.Learning sigmoid belief nets [12 min].mp4 - 13.60 MB
|- 61.Belief Nets [13 min].srt - 17.00 kB
|- 61.Belief Nets [13 min].mp4 - 14.90 MB
|- 60.The ups and downs of back propagation [10 min].srt - 14.00 kB
|- 60.The ups and downs of back propagation [10 min].mp4 - 11.80 MB
|- 6.Types of neural network architectures [7 min].srt - 10.00 kB
|- 6.Types of neural network architectures [7 min].mp4 - 8.80 MB
|- 59.RBMs for collaborative filtering [8 mins].srt - 11.00 kB
|- 59.RBMs for collaborative filtering [8 mins].mp4 - 9.50 MB
|- 58.An example of RBM learning [7 mins].srt - 10.00 kB
|- 58.An example of RBM learning [7 mins].mp4 - 8.70 MB
|- 57.Restricted Boltzmann Machines [11 min].srt - 14.00 kB
|- 57.Restricted Boltzmann Machines [11 min].mp4 - 12.70 MB
|- 55.Boltzmann machine learning [12 min].srt - 16.00 kB
|- 55.Boltzmann machine learning [12 min].mp4 - 14.00 MB
|- 54.How a Boltzmann machine models data [12 min].srt - 16.00 kB
|- 54.How a Boltzmann machine models data [12 min].mp4 - 13.30 MB
|- 53.Using stochastic units to improv search [11 min].srt - 14.00 kB
|- 53.Using stochastic units to improv search [11 min].mp4 - 11.80 MB
|- 52.Hopfield nets with hidden units [10 min].srt - 12.00 kB
|- 52.Hopfield nets with hidden units [10 min].mp4 - 11.30 MB
|- 51.Dealing with spurious minima [11 min].srt - 15.00 kB
|- 51.Dealing with spurious minima [11 min].mp4 - 12.80 MB
|- 50.Hopfield Nets [13 min].srt - 16.00 kB
|- 50.Hopfield Nets [13 min].mp4 - 14.60 MB
|- 5.Three types of learning [8 min].srt - 10.00 kB
|- 5.Three types of learning [8 min].mp4 - 9.00 MB
|- 49.Dropout [9 min].srt - 12.00 kB
|- 49.Dropout [9 min].mp4 - 9.70 MB
|- 48.Making full Bayesian learning practical [7 min].srt - 8.00 kB
|- 48.Making full Bayesian learning practical [7 min].mp4 - 8.10 MB
|- 47.The idea of full Bayesian learning [7 min].srt - 10.00 kB
|- 47.The idea of full Bayesian learning [7 min].mp4 - 8.40 MB
|- 46.Mixtures of Experts [13 min].srt - 17.00 kB
|- 46.Mixtures of Experts [13 min].mp4 - 15.00 MB
|- 45.Why it helps to combine models [13 min].srt - 18.00 kB
|- 45.Why it helps to combine models [13 min].mp4 - 15.10 MB
|- 44.MacKay’s quick and dirty method of setting weight costs [4 min].srt - 4.00 kB
|- 44.MacKay’s quick and dirty method of setting weight costs [4 min].mp4 - 4.40 MB
|- 43.The Bayesian interpretation of weight decay [11 min].srt - 13.00 kB
|- 43.The Bayesian interpretation of weight decay [11 min].mp4 - 12.30 MB
|- 42.Introduction to the full Bayesian approach [12 min].srt - 13.00 kB
|- 42.Introduction to the full Bayesian approach [12 min].mp4 - 12.00 MB
|- 41.Using noise as a regularizer [7 min].srt - 9.00 kB
|- 41.Using noise as a regularizer [7 min].mp4 - 8.50 MB
|- 40.Limiting the size of the weights [6 min].srt - 8.00 kB
|- 40.Limiting the size of the weights [6 min].mp4 - 7.40 MB
|- 4.A simple example of learning [6 min].srt - 7.00 kB
|- 4.A simple example of learning [6 min].mp4 - 6.60 MB
|- 39.Overview of ways to improve generalization [12 min].srt - 16.00 kB
|- 39.Overview of ways to improve generalization [12 min].mp4 - 13.60 MB
|- 38.Echo State Networks [9 min].srt - 12.00 kB
|- 38.Echo State Networks [9 min].mp4 - 11.30 MB
|- 37.Learning to predict the next character using HF [12 mins].srt - 16.00 kB
|- 37.Learning to predict the next character using HF [12 mins].mp4 - 13.90 MB
|- 36.Modeling character strings with multiplicative connections [14 mins].srt - 17.00 kB
|- 36.Modeling character strings with multiplicative connections [14 mins].mp4 - 16.60 MB
|- 35.A brief overview of Hessian Free optimization.srt - 18.00 kB
|- 35.A brief overview of Hessian Free optimization.mp4 - 16.20 MB
|- 34.Long-term Short-term-memory.srt - 12.00 kB
|- 34.Long-term Short-term-memory.mp4 - 10.20 MB
|- 33.Why it is difficult to train an RNN.srt - 10.00 kB
|- 33.Why it is difficult to train an RNN.mp4 - 8.90 MB
|- 32.A toy example of training an RNN.srt - 8.00 kB
|- 32.A toy example of training an RNN.mp4 - 7.20 MB
|- 31.Training RNNs with back propagation.srt - 8.00 kB
|- 31.Training RNNs with back propagation.mp4 - 7.30 MB
|- 3.Some simple models of neurons [8 min].srt - 11.00 kB
|- 3.Some simple models of neurons [8 min].mp4 - 9.30 MB
|- 28.Adaptive learning rates for each connection.srt - 8.00 kB
|- 28.Adaptive learning rates for each connection.mp4 - 6.60 MB
|- 27.The momentum method.srt - 11.00 kB
|- 27.The momentum method.mp4 - 9.70 MB