Deep Learning
4.0
(1)
7 learners
What you'll learn
This course includes
- 26.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 118 lessons • 26.5 hours of video
Deep Learning
118 lessons
• 26.5 hours
Deep Learning
118 lessons
• 26.5 hours
- Deep Learning - Course Introduction06:59
- Deep Learning(CS7015): Lec 1.1 Biological Neuron06:58
- Deep Learning(CS7015): Lec 1.2 From Spring to Winter of AI13:44
- Deep Learning(CS7015): Lec 1.3 The Deep Revival07:57
- Deep Learning(CS7015): Lec 1.4 From Cats to Convolutional Neural Networks03:32
- Deep Learning(CS7015): Lec 1.5 Faster, higher, stronger02:48
- Deep Learning(CS7015): Lec 1.6 The Curious Case of Sequences06:36
- Deep Learning(CS7015): Lec 1.7 Beating humans at their own games (literally)02:03
- Deep Learning(CS7015): Lec 1.8 The Madness (2013-)04:55
- Deep Learning(CS7015): Lec 1.9 (Need for) Sanity04:46
- Deep Learning(CS7015): Lec 2.1 Motivation from Biological Neurons07:32
- Deep Learning(CS7015): Lec 2.2 McCulloch Pitts Neuron, Thresholding Logic13:39
- Deep Learning(CS7015): Lec 2.3 Perceptrons11:00
- Deep Learning(CS7015): Lec 2.4 Error and Error Surfaces04:21
- Deep Learning(CS7015): Lec 2.5 Perceptron Learning Algorithm13:35
- Deep Learning(CS7015): Lec 2.6 Proof of Convergence of Perceptron Learning Algorithm15:38
- Deep Learning(CS7015): Lec 2.7 Linearly Separable Boolean Functions06:18
- Deep Learning(CS7015): Lec 2.8 Representation Power of a Network of Perceptrons13:30
- Deep Learning(CS7015): Lec 3.1 Sigmoid Neuron12:30
- Deep Learning(CS7015): Lec 3.2 A typical Supervised Machine Learning Setup16:02
- Deep Learning(CS7015): Lec 3.3 Learning Parameters: (Infeasible) guess work11:53
- Deep Learning(CS7015): Lec 3.4 Learning Parameters: Gradient Descent31:21
- Deep Learning(CS7015): Lec 3.5 Representation Power of Multilayer Network of Sigmoid Neurons35:35
- Deep Learning(CS7015): Lec 4.1 Feedforward Neural Networks (a.k.a multilayered network of neurons)18:46
- Deep Learning(CS7015): Lec 4.2 Learning Paramters of Feedforward Neural Networks (Intuition)06:57
- Deep Learning(CS7015): Lec 4.3 Output functions and Loss functions26:50
- Deep Learning(CS7015): Lec 4.4 Backpropagation (Intuition)13:52
- Deep Learning(CS7015): Lec 4.5 Backpropagation: Computing Gradients w.r.t. the Output Units17:08
- Deep Learning(CS7015): Lec 4.6 Backpropagation: Computing Gradients w.r.t. Hidden Units21:02
- Deep Learning(CS7015): Lec 4.7 Backpropagation: Computing Gradients w.r.t. Parameters12:45
- Deep Learning(CS7015): Lec 4.8 Backpropagation: Pseudo code05:59
- Deep Learning(CS7015): Lec 4.9 Derivative of the activation function01:53
- Deep Learning(CS7015): Lec 6.6 PCA : Interpretation 304:02
- Deep Learning(CS7015): Lec 4.10 Information content, Entropy & cross entropy44:28
- Deep Learning(CS7015): Lec 5.1 & Lec 5.2 Recap: Learning Parameters: Guess Work, Gradient Descent09:11
- Deep Learning(CS7015): Lec 5.3 Contours Maps11:10
- Deep Learning(CS7015): Lec 5.4 Momentum based Gradient Descent18:45
- Deep Learning(CS7015): Lec 5.5 Nesterov Accelerated Gradient Descent11:59
- Deep Learning(CS7015): Lec 5.6 Stochastic And Mini-Batch Gradient Descent14:12
- Deep Learning(CS7015): Lec 5.7 Tips for Adjusting Learning Rate and Momentum13:21
- Deep Learning(CS7015): Lec 5.8 Line Search09:21
- Deep Learning(CS7015): Lec 5.9 Gradient Descent with Adaptive Learning Rate40:48
- Deep Learning(CS7015): Lec 5.9 (Part-2) Bias Correction in Adam10:12
- Deep Learning(CS7015): Lec 6.1 Eigenvalues and Eigenvectors17:49
- Deep Learning(CS7015): Lec 6.2 Linear Algebra : Basic Definitions11:18
- Deep Learning(CS7015): Lec 6.3 Eigenvalue Decompositon09:45
- Deep Learning(CS7015): Lec 6.4 Principal Component Analysis and its Interpretations25:42
- Deep Learning(CS7015): Lec 6.5 PCA : Interpretation 217:08
- Deep Learning(CS7015): Lec 6.6 (Part-2) PCA : Interpretation 3 (Contd.)02:11
- Deep Learning(CS7015): Lec 6.7 PCA : Practical Example12:12
- Deep Learning(CS7015): Lec 6.8 Singular Value Decomposition26:00
- Deep Learning(CS7015): Lec 7.1 Introduction to Autoncoders52:33
- Deep Learning(CS7015): Lec 7.2 Link between PCA and Autoencoders17:39
- Deep Learning(CS7015): Lec 7.3 Regularization in autoencoders (Motivation)12:27
- Deep Learning(CS7015): Lec 7.4 Denoising Autoencoders26:18
- Deep Learning(CS7015): Lec 7.5 Sparse Autoencoders09:12
- Deep Learning(CS7015): Lec 7.6 Contractive Autoencoders08:39
- Deep Learning(CS7015): Lec 8.1 Bias and Variance10:53
- Deep Learning(CS7015): Lec 8.2 Train error vs Test error11:24
- Deep Learning(CS7015): Lec 8.2 (Part-2) Train error vs Test error (Recap)18:01
- Deep Learning(CS7015): Lec 8.3 True error and Model complexity08:48
- Deep Learning(CS7015): Lec 8.4 L2 regularization24:13
- Deep Learning(CS7015): Lec 8.5 Dataset augmentation06:23
- Deep Learning(CS7015): Lec 8.6 Parameter sharing and tying01:31
- Deep Learning(CS7015): Lec 8.7 Adding Noise to the inputs08:03
- Deep Learning(CS7015): Lec 8.8 Adding Noise to the outputs04:51
- Deep Learning(CS7015): Lec 8.9 Early stopping11:58
- Deep Learning(CS7015): Lec 8.10 Ensemble Methods07:54
- Deep Learning(CS7015): Lec 8.11 Dropout16:37
- Deep Learning(CS7015): Lec 9.1 A quick recap of training deep neural networks06:21
- Deep Learning(CS7015): Lec 9.2 Unsupervised pre-training24:57
- Deep Learning(CS7015): Lec 9.3 Better activation functions28:09
- Deep Learning(CS7015): Lec 9.4 Better initialization strategies26:31
- Deep Learning(CS7015): Lec 9.5 Batch Normalization15:44
- Deep Learning(CS7015): Lec 10.1 One-hot representations of words09:26
- Deep Learning(CS7015): Lec 10.2 Distributed Representations of words12:06
- Deep Learning(CS7015): Lec 10.3 SVD for learning word representations14:41
- Deep Learning(CS7015): Lec 10.3 (Part-2) SVD for learning word representations (Contd.)01:41
- Deep Learning(CS7015): Lec 10.4 Continuous bag of words model36:36
- Deep Learning(CS7015): Lec 10.5 Skip-gram model10:59
- Deep Learning(CS7015): Lec 10.5 (Part-2) Skip-gram model (Contd.)08:35
- Deep Learning(CS7015): Lec 10.6 Contrastive estimation07:05
- Deep Learning(CS7015): Lec 10.7 Hierarchical softmax13:30
- Deep Learning(CS7015): Lec 10.8 GloVe representations07:59
- Deep Learning(CS7015): Lec 10.9 Evaluating word representations08:51
- Deep Learning(CS7015): Lec 10.10 Relation between SVD and Word2Vec04:16
- Deep Learning(CS7015): Lec 11.1 The convolution operation18:29
- Deep Learning(CS7015): Lec 11.2 Relation between input size, output size and filter size12:27
- Deep Learning(CS7015): Lec 11.3 Convolutional Neural Networks16:58
- Deep Learning(CS7015): Lec 11.3 (Part-2) Convolutional Neural Networks (Contd.)17:39
- Deep Learning(CS7015): Lec 11.4 CNNs (success stories on ImageNet)20:49
- Deep Learning(CS7015): Lec 11.4 (Par-2) CNNs (success stories on ImageNet) (Contd.)02:57
- Deep Learning(CS7015): Lec 11.5 Image Classification continued (GoogLeNet and ResNet)22:50
- Deep Learning(CS7015): Lec 12.1 Visualizing patches which maximally activate a neuron06:36
- Deep Learning(CS7015): Lec 12.2 Visualizing filters of a CNN06:31
- Deep Learning(CS7015): Lec 12.3 Occlusion experiments06:21
- Deep Learning(CS7015): Lec 12.4 Finding influence of input pixels using backpropagation06:12
- Deep Learning(CS7015): Lec 12.5 Guided Backpropagation04:52
- Deep Learning(CS7015): Lec 12.6 Optimization over images10:28
- Deep Learning(CS7015): Lec 12.7 Create images from embeddings06:20
- Deep Learning(CS7015): Lec 12.8 Deep Dream11:19
- Deep Learning(CS7015): Lec 12.9 Deep Art05:49
- Deep Learning(CS7015): Lec 12.10 Fooling Deep Convolutional Neural Networks06:43
- Deep Learning(CS7015): Lec 13.1 Sequence Learning Problems08:44
- Deep Learning(CS7015): Lec 13.2 Recurrent Neural Networks09:48
- Deep Learning(CS7015): Lec 13.3 Backpropagation through time14:24
- Deep Learning(CS7015): Lec 13.4 The problem of Exploding and Vanishing Gradients11:04
- Deep Learning(CS7015): Lec 13.5 Some Gory Details06:58
- Deep Learning(CS7015): Lec 14.1 Selective Read, Selective Write, Selective Forget09:18
- Deep Learning(CS7015): Lec 14.2 Long Short Term Memory(LSTM) and Gated Recurrent Units(GRUs)31:20
- Deep Learning(CS7015): Lec 14.3 How LSTMs avoid the problem of vanishing gradients08:11
- Deep Learning(CS7015): Lec 14.3 (Part-2) How LSTMs avoid the problem of vanishing gradients (Contd.)23:53
- Deep Learning(CS7015): Lec 15.1 Introduction to Encoder Decoder Models21:55
- Deep Learning(CS7015): Lec 15.2 Applications of Encoder Decoder models17:34
- Deep Learning(CS7015): Lec 15.3 Attention Mechanism27:38
- Deep Learning(CS7015): Lec 15.3 (Part-2) Attention Mechanism (Contd.)02:38
- Deep Learning(CS7015): Lec 15.4 Attention over images11:31
- Deep Learning(CS7015): Lec 15.5 Hierarchical Attention20:32
