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neural_nets_hinton
Size 884.52 MB
Files 78
Info Hash: 743C16A18756557A67478A7570BAF24A59F9CDA6
Indexed 2016-09-01 00:00:00
Updated 2026-06-09 19:16:50
📂 File List (78)
🎬
1 - 1 - Why do we need machine learning- [13 min].mp4
15.05 MB
🎬
1 - 2 - What are neural networks- [8 min].mp4
9.76 MB
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1 - 3 - Some simple models of neurons [8 min].mp4
9.26 MB
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1 - 4 - A simple example of learning [6 min].mp4
6.57 MB
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1 - 5 - Three types of learning [8 min].mp4
8.96 MB
🎬
10 - 1 - Why it helps to combine models [13 min].mp4
15.12 MB
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10 - 2 - Mixtures of Experts [13 min].mp4
14.98 MB
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10 - 3 - The idea of full Bayesian learning [7 min].mp4
8.39 MB
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10 - 4 - Making full Bayesian learning practical [7 min].mp4
8.13 MB
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10 - 5 - Dropout [9 min].mp4
9.69 MB
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11 - 1 - Hopfield Nets [13 min].mp4
14.65 MB
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11 - 2 - Dealing with spurious minima [11 min].mp4
12.77 MB
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11 - 3 - Hopfield nets with hidden units [10 min].mp4
11.31 MB
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11 - 4 - Using stochastic units to improv search [11 min].mp4
11.76 MB
🎬
11 - 5 - How a Boltzmann machine models data [12 min].mp4
13.28 MB
🎬
12 - 1 - Boltzmann machine learning [12 min].mp4
14.03 MB
🎬
12 - 2 - OPTIONAL VIDEO- More efficient ways to get the statistics [15 mins].mp4
16.93 MB
🎬
12 - 3 - Restricted Boltzmann Machines [11 min].mp4
12.68 MB
🎬
12 - 4 - An example of RBM learning [7 mins].mp4
8.71 MB
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12 - 5 - RBMs for collaborative filtering [8 mins].mp4
9.53 MB
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13 - 1 - The ups and downs of back propagation [10 min].mp4
11.83 MB
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13 - 2 - Belief Nets [13 min].mp4
14.86 MB
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13 - 3 - Learning sigmoid belief nets [12 min].mp4
13.59 MB
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13 - 4 - The wake-sleep algorithm [13 min].mp4
15.68 MB
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14 - 1 - Learning layers of features by stacking RBMs [17 min].mp4
20.07 MB
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14 - 2 - Discriminative learning for DBNs [9 mins].mp4
11.29 MB
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14 - 3 - What happens during discriminative fine-tuning- [8 mins].mp4
10.17 MB
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14 - 4 - Modeling real-valued data with an RBM [10 mins].mp4
11.2 MB
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14 - 5 - OPTIONAL VIDEO- RBMs are infinite sigmoid belief nets [17 mins].mp4
19.44 MB
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15 - 1 - From PCA to autoencoders [5 mins].mp4
9.68 MB
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15 - 2 - Deep auto encoders [4 mins].mp4
4.92 MB
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15 - 3 - Deep auto encoders for document retrieval [8 mins].mp4
10.25 MB
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15 - 4 - Semantic Hashing [9 mins].mp4
9.99 MB
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15 - 5 - Learning binary codes for image retrieval [9 mins].mp4
11.51 MB
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15 - 6 - Shallow autoencoders for pre-training [7 mins].mp4
8.25 MB
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16 - 1 - OPTIONAL- Learning a joint model of images and captions [10 min].mp4
13.83 MB
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16 - 2 - OPTIONAL- Hierarchical Coordinate Frames [10 mins].mp4
11.16 MB
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16 - 3 - OPTIONAL- Bayesian optimization of hyper-parameters [13 min].mp4
15.8 MB
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16 - 4 - OPTIONAL- The fog of progress [3 min].mp4
2.78 MB
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2 - 1 - Types of neural network architectures [7 min].mp4
8.78 MB
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2 - 2 - Perceptrons- The first generation of neural networks [8 min].mp4
9.39 MB
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2 - 3 - A geometrical view of perceptrons [6 min].mp4
7.32 MB
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2 - 4 - Why the learning works [5 min].mp4
5.9 MB
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2 - 5 - What perceptrons can-'t do [15 min].mp4
16.57 MB
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3 - 1 - Learning the weights of a linear neuron [12 min].mp4
13.52 MB
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3 - 2 - The error surface for a linear neuron [5 min].mp4
5.89 MB
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3 - 3 - Learning the weights of a logistic output neuron [4 min].mp4
4.37 MB
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3 - 4 - The backpropagation algorithm [12 min].mp4
13.35 MB
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3 - 5 - Using the derivatives computed by backpropagation [10 min].mp4
11.15 MB
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4 - 1 - Learning to predict the next word [13 min].mp4
14.28 MB
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4 - 2 - A brief diversion into cognitive science [4 min].mp4
5.31 MB
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4 - 3 - Another diversion- The softmax output function [7 min].mp4
8.03 MB
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4 - 4 - Neuro-probabilistic language models [8 min].mp4
8.93 MB
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4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp4
14.26 MB
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5 - 1 - Why object recognition is difficult [5 min].mp4
5.37 MB
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5 - 2 - Achieving viewpoint invariance [6 min].mp4
6.89 MB
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5 - 3 - Convolutional nets for digit recognition [16 min].mp4
18.46 MB
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5 - 4 - Convolutional nets for object recognition [17min].mp4
23.03 MB
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6 - 1 - Overview of mini-batch gradient descent.mp4
9.6 MB
🎬
6 - 2 - A bag of tricks for mini-batch gradient descent.mp4
14.9 MB
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6 - 3 - The momentum method.mp4
9.74 MB
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6 - 4 - Adaptive learning rates for each connection.mp4
6.63 MB
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6 - 5 - Rmsprop- Divide the gradient by a running average of its recent magnitude.mp4
15.12 MB
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7 - 1 - Modeling sequences- A brief overview.mp4
20.13 MB
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7 - 2 - Training RNNs with back propagation.mp4
7.33 MB
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7 - 3 - A toy example of training an RNN.mp4
7.24 MB
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7 - 4 - Why it is difficult to train an RNN.mp4
8.89 MB
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7 - 5 - Long-term Short-term-memory.mp4
10.23 MB
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8 - 1 - A brief overview of Hessian Free optimization.mp4
16.24 MB
🎬
8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp4
16.56 MB
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8 - 3 - Learning to predict the next character using HF [12 mins].mp4
13.92 MB
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8 - 4 - Echo State Networks [9 min].mp4
11.28 MB
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9 - 1 - Overview of ways to improve generalization [12 min].mp4
13.57 MB
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9 - 2 - Limiting the size of the weights [6 min].mp4
7.36 MB
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9 - 3 - Using noise as a regularizer [7 min].mp4
8.48 MB
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9 - 4 - Introduction to the full Bayesian approach [12 min].mp4
12 MB
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9 - 5 - The Bayesian interpretation of weight decay [11 min].mp4
12.27 MB
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9 - 6 - MacKay-'s quick and dirty method of setting weight costs [4 min].mp4
4.37 MB
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