Filename Size 001.Specialization Promo/001. Welcome to AML specialization!.mp4 13.7 MB 001.Specialization Promo/001. Welcome to AML specialization!.srt 4.7 KB 002.Course intro/002. Course intro.mp4 22.1 MB 002.Course intro/002. Course intro.srt 8.8 KB 003.Linear model as the simplest neural network/003. Linear regression.mp4 35.7 MB 003.Linear model as the simplest neural network/003. Linear regression.srt 13.3 KB 003.Linear model as the simplest neural network/004. Linear classification.mp4 42.7 MB 003.Linear model as the simplest neural network/004. Linear classification.srt 16.4 KB 003.Linear model as the simplest neural network/005. Gradient descent.mp4 19 MB 003.Linear model as the simplest neural network/005. Gradient descent.srt 7.4 KB 004.Regularization in machine learning/006. Overfitting problem and model validation.mp4 26.4 MB 004.Regularization in machine learning/006. Overfitting problem and model validation.srt 9.8 KB 004.Regularization in machine learning/007. Model regularization.mp4 19.9 MB 004.Regularization in machine learning/007. Model regularization.srt 7.4 KB 005.Stochastic methods for optimization/008. Stochastic gradient descent.mp4 21.1 MB 005.Stochastic methods for optimization/008. Stochastic gradient descent.srt 7.8 KB 005.Stochastic methods for optimization/009. Gradient descent extensions.mp4 36.6 MB 005.Stochastic methods for optimization/009. Gradient descent extensions.srt 13.4 KB 006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp4 44.7 MB 006.The simplest neural network MLP/010. Multilayer perceptron (MLP).srt 18.5 KB 006.The simplest neural network MLP/011. Chain rule.mp4 26.6 MB 006.The simplest neural network MLP/011. Chain rule.srt 10 KB 006.The simplest neural network MLP/012. Backpropagation.mp4 31.6 MB 006.The simplest neural network MLP/012. Backpropagation.srt 11.4 KB 007.Matrix derivatives/013. Efficient MLP implementation.mp4 47.1 MB 007.Matrix derivatives/013. Efficient MLP implementation.srt 16.6 KB 007.Matrix derivatives/014. Other matrix derivatives.mp4 21.4 MB 007.Matrix derivatives/014. Other matrix derivatives.srt 8.6 KB 008.TensorFlow framework/015. What is TensorFlow.mp4 39.4 MB 008.TensorFlow framework/015. What is TensorFlow.srt 14.7 KB 008.TensorFlow framework/016. Our first model in TensorFlow.mp4 36.8 MB 008.TensorFlow framework/016. Our first model in TensorFlow.srt 13.8 KB 009.Philosophy of deep learning/017. What Deep Learning is and is not.mp4 29.5 MB 009.Philosophy of deep learning/017. What Deep Learning is and is not.srt 13.9 KB 009.Philosophy of deep learning/018. Deep learning as a language.mp4 24.6 MB 009.Philosophy of deep learning/018. Deep learning as a language.srt 11.9 KB 010.Introduction to CNN/019. Motivation for convolutional layers.mp4 41.4 MB 010.Introduction to CNN/019. Motivation for convolutional layers.srt 16 KB 010.Introduction to CNN/020. Our first CNN architecture.mp4 42.6 MB 010.Introduction to CNN/020. Our first CNN architecture.srt 13.3 KB 011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp4 57.9 MB 011.Modern CNNs/021. Training tips and tricks for deep CNNs.srt 18.2 KB 011.Modern CNNs/022. Overview of modern CNN architectures.mp4 32.2 MB 011.Modern CNNs/022. Overview of modern CNN architectures.srt 9.5 KB 012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp4 19.3 MB 012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.srt 6.8 KB 012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp4 30.7 MB 012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.srt 10.8 KB 013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp4 23.8 MB 013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.srt 9.5 KB 013.Intro to Unsupervised Learning/026. Autoencoders 101.mp4 22.1 MB 013.Intro to Unsupervised Learning/026. Autoencoders 101.srt 8.1 KB 014.More Autoencoders/027. Autoencoder applications.mp4 40.8 MB 014.More Autoencoders/027. Autoencoder applications.srt 14.7 KB 014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp4 28.2 MB 014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.srt 10.6 KB 015.Word Embeddings/029. Natural language processing primer.mp4 36.7 MB 015.Word Embeddings/029. Natural language processing primer.srt 15.3 KB 015.Word Embeddings/030. Word embeddings.mp4 48.3 MB 015.Word Embeddings/030. Word embeddings.srt 20.2 KB 016.Generative Adversarial Networks/031. Generative models 101.mp4 26.7 MB 016.Generative Adversarial Networks/031. Generative models 101.srt 11.2 KB 016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp4 36.2 MB 016.Generative Adversarial Networks/032. Generative Adversarial Networks.srt 15.3 KB 016.Generative Adversarial Networks/033. Applications of adversarial approach.mp4 41.9 MB 016.Generative Adversarial Networks/033. Applications of adversarial approach.srt 15.9 KB 017.Introduction to RNN/034. Motivation for recurrent layers.mp4 30.2 MB 017.Introduction to RNN/034. Motivation for recurrent layers.srt 10.6 KB 017.Introduction to RNN/035. Simple RNN and Backpropagation.mp4 35.1 MB 017.Introduction to RNN/035. Simple RNN and Backpropagation.srt 12.5 KB 018.Modern RNNs/036. The training of RNNs is not that easy.mp4 26.4 MB 018.Modern RNNs/036. The training of RNNs is not that easy.srt 10.4 KB 018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp4 34.9 MB 018.Modern RNNs/037. Dealing with vanishing and exploding gradients.srt 13.7 KB 018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp4 47.7 MB 018.Modern RNNs/038. Modern RNNs LSTM and GRU.srt 17.2 KB 019.Applications of RNNs/039. Practical use cases for RNNs.mp4 56.1 MB 019.Applications of RNNs/039. Practical use cases for RNNs.srt 19.5 KB
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