1634441407_maxresdefault.jpg
34
39:56

(Old) Lecture 0 | Course Logistics

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lecture0.logistics.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ YouTube Source for this AI Video
1634334368_maxresdefault.jpg
98
01:00:55

(Old) Lecture 1 | A Brief History of Deep Learning

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lecture1.introduction.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Contents: • Introduction to deep learning • Course logistics • History and cognitive basis of neural computation. • The perceptron • Multi-layer perceptrons YouTube Source for this AI Video
1634602622_maxresdefault.jpg
19
01:19:52

(Old) Lecture 10 | (3/3) Convolutional Neural Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec10.CNN.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Backpropagation through CNNs • Increasing output map size • Transform invariance • Alexnet, Inception, VGG YouTube Source for this AI Video
1634580208_maxresdefault.jpg
30
01:28:32

(Old) Lecture 11 | (1/3) Recurrent Neural Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec11.recurrent.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Recurrent Neural Networks (RNNs) • Modeling series • Back propagation through time (BPTT) • Bidirectional RNNs YouTube Source for this AI Video
1634644689_maxresdefault.jpg
17
01:19:44

(Old) Lecture 12 | (2/3) Recurrent Neural Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec12.recurrent.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Stability • Exploding/vanishing gradients • Long Short-Term Memory Units (LSTMs) and variants YouTube Source for this AI Video
1634771534_maxresdefault.jpg
12
01:16:00

(Old) Lecture 15 | (3/3) Recurrent Neural Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec14.CTC.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Sequence To Sequence Modeling • Connectionist Temporal Classification (CTC) YouTube Source for this AI Video
1634498041_maxresdefault.jpg
48
01:53:33

(Old) Lecture 16 | Connectionist Temporal Classification

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec14.CTC.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Connectionist Temporal Classification (CTC) YouTube Source for this AI Video
1634689567_maxresdefault.jpg
18
01:14:34

(Old) Lecture 17 | Sequence-to-sequence Models with Attention

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec_16_representations.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Attention Models YouTube Source for this AI Video
1634595169_maxresdefault.jpg
20
01:17:22

(Old) Lecture 18 | Autoencoders and Dimensionality Reduction

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec_16_representations.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • What do networks learn • Autoencoders and dimensionality reduction YouTube Source for this AI Video
1634719509_maxresdefault.jpg
16
01:20:59

(Old) Lecture 19 | Hopfield Networks and Auto-Associators

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec_16_representations.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Hopfield Networks YouTube Source for this AI Video
1634365103_maxresdefault.jpg
66
01:10:06

(Old) Lecture 2 | The Universal Approximation Theorem

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec2.universal.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • The neural net as a universal approximator YouTube Source for this AI Video
1634704672_maxresdefault.jpg
4
01:21:49

(Old) Lecture 20 | (1/2) Boltzmann Machines

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec19.BM.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Restricted Boltzman Machines (RBMs) • Deep Boltzmann Machines (DBMs) YouTube Source for this AI Video
1634805059_maxresdefault.jpg
10
01:19:51

(Old) Lecture 21 | (2/2) Boltzmann Machines

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec19.BM.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Restricted Boltzman Machines (RBMs) • Deep Boltzmann Machines (DBMs) YouTube Source for this AI Video
1634682228_maxresdefault.jpg
16
01:20:55

(Old) Lecture 22 | (1/2) Generative Adversarial Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/bstriner_gans.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Generative Adversarial Networks (GANs) • Non-linear generators YouTube Source for this AI Video
1634945494_maxresdefault.jpg
10
01:21:48

(Old) Lecture 23 | (2/2) Generative Adversarial Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/bstriner_gans_part_2.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Contents: • Generative Adversarial Networks (GANs) YouTube Source for this AI Video
1634978655_maxresdefault.jpg
7
01:18:38

(Old) Lecture 27 (4/4) Deep Reinforcement Learning

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Reinforcement Learning YouTube Source for this AI Video
1634874969_maxresdefault.jpg
14
01:13:26

(Old) Lecture 28 | Deep Q Learning

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 For more information, please visit: http://deeplearning.cs.cmu.edu/ Contents: • Q Learning • Deep Q Learning YouTube Source for this AI Video
1634411187_maxresdefault.jpg
41
01:09:15

(Old) Lecture 3 | Perceptrons and Gradient Descent

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec3.learning.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Training a neural network • Perceptron learning rule • Empirical Risk Minimization • Optimization by gradient descent YouTube Source for this AI Video
1634460466_maxresdefault.jpg
27
01:22:26

(Old) Lecture 4 | The Backpropagation Algorithm

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec4.backprop.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Backpropagation algorithm • Calculus of backpropagation YouTube Source for this AI Video
1634437741_maxresdefault.jpg
44
01:35:09

(Old) Lecture 5 | Convergence in Neural Networks

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lec5.convergence.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Contents: • Convergence in neural networks • Rates of convergence • Loss surfaces • Learning rates, and optimization methods • RMSProp, Adagrad, Momentum YouTube Source for this AI Video
1634547676_maxresdefault.jpg
22
01:20:50

(Old) Lecture 6 | Acceleration, Regularization, and Normalization

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Spring 2019 Slides: http://deeplearning.cs.cmu.edu/slides.spring19/lecture_6_SGD.pdf For more information, please visit: http://deeplearning.cs.cmu.edu/ Content: • Stochastic gradient descent • Optimization • Acceleration • Overfitting and regularization • Tricks of the trade: – Choosing a divergence (loss) function – Batch normalization – Dropout YouTube Source for this AI Video