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Introduction to Deep Learning
Course Lectures
001. Welcome to AML Specialization! (2:49)
002. Course Intro (6:02)
003. Linear regression (9:58)
004. Linear classification (10:50)
005. Gradient descent (5:04)
006. Overfitting problem and model validation (6:56)
007. Model regularization (5:20)
008. Stochastic gradient descent (5:40)
009. Gradient descent extensions (9:58)
010. Multilayer perceptron (MLP) (12:36)
011. Chain rule (7:30)
012. Backpropagation (9:01)
013. Efficient MLP implementation (13:08)
014. Other matrix derivatives (5:54)
015. What is TensorFlow (10:54)
016. Our first model in TensorFlow (10:11)
017. What Deep Learning is and is not (8:37)
018. Deep learning as a language (6:59)
019. Motivation for convolutional layers (11:14)
020. Our first CNN architecture (10:59)
021. Training tips and tricks for deep CNNs (14:48)
022. Overview of modern CNN architectures (8:19)
023. Learning new tasks with pre-trained CNNs (5:16)
024. A glimpse of other Computer Vision tasks (8:17)
025. Unsupervised learning what it is and why bother (5:57)
026. Autoencoders 101 (5:34)
027. Autoencoder applications (9:42)
028. Autoencoder applications image generation, data visualization & more (7:19)
029. Natural language processing primer (10:10)
030. Word embeddings (13:21)
031. Generative models 101 (7:31)
032. Generative Adversarial Networks (10:05)
033. Applications of adversarial approach (11:08)
034. Motivation for recurrent layers (7:36)
035. Simple RNN and Backpropagation (8:23)
036. The training of RNNs is not that easy (7:19)
037. Dealing with vanishing and exploding gradients (9:02)
038. Modern RNNs LSTM and GRU (11:30)
039. Practical use cases for RNNs (13:17)
016. Our first model in TensorFlow
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