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011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp4 57.9 MB
019.Applications of RNNs/039. Practical use cases for RNNs.mp4 56.12 MB
015.Word Embeddings/030. Word embeddings.mp4 48.35 MB
018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp4 47.7 MB
007.Matrix derivatives/013. Efficient MLP implementation.mp4 47.09 MB
006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp4 44.68 MB
003.Linear model as the simplest neural network/004. Linear classification.mp4 42.66 MB
010.Introduction to CNN/020. Our first CNN architecture.mp4 42.57 MB
016.Generative Adversarial Networks/033. Applications of adversarial approach.mp4 41.89 MB
010.Introduction to CNN/019. Motivation for convolutional layers.mp4 41.38 MB
014.More Autoencoders/027. Autoencoder applications.mp4 40.85 MB
008.TensorFlow framework/015. What is TensorFlow.mp4 39.44 MB
008.TensorFlow framework/016. Our first model in TensorFlow.mp4 36.8 MB
015.Word Embeddings/029. Natural language processing primer.mp4 36.68 MB
005.Stochastic methods for optimization/009. Gradient descent extensions.mp4 36.57 MB
016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp4 36.16 MB
003.Linear model as the simplest neural network/003. Linear regression.mp4 35.73 MB
017.Introduction to RNN/035. Simple RNN and Backpropagation.mp4 35.07 MB
018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp4 34.86 MB
011.Modern CNNs/022. Overview of modern CNN architectures.mp4 32.24 MB
006.The simplest neural network MLP/012. Backpropagation.mp4 31.63 MB
012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp4 30.74 MB
017.Introduction to RNN/034. Motivation for recurrent layers.mp4 30.15 MB
009.Philosophy of deep learning/017. What Deep Learning is and is not.mp4 29.46 MB
014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp4 28.21 MB
016.Generative Adversarial Networks/031. Generative models 101.mp4 26.68 MB
006.The simplest neural network MLP/011. Chain rule.mp4 26.59 MB
004.Regularization in machine learning/006. Overfitting problem and model validation.mp4 26.42 MB
018.Modern RNNs/036. The training of RNNs is not that easy.mp4 26.39 MB
009.Philosophy of deep learning/018. Deep learning as a language.mp4 24.6 MB
013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp4 23.78 MB
013.Intro to Unsupervised Learning/026. Autoencoders 101.mp4 22.14 MB
002.Course intro/002. Course intro.mp4 22.1 MB
007.Matrix derivatives/014. Other matrix derivatives.mp4 21.42 MB
005.Stochastic methods for optimization/008. Stochastic gradient descent.mp4 21.1 MB
004.Regularization in machine learning/007. Model regularization.mp4 19.85 MB
012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp4 19.28 MB
003.Linear model as the simplest neural network/005. Gradient descent.mp4 18.96 MB
001.Specialization Promo/001. Welcome to AML specialization!.mp4 13.67 MB
015.Word Embeddings/030. Word embeddings.srt 20.23 KB
019.Applications of RNNs/039. Practical use cases for RNNs.srt 19.47 KB
006.The simplest neural network MLP/010. Multilayer perceptron (MLP).srt 18.51 KB
011.Modern CNNs/021. Training tips and tricks for deep CNNs.srt 18.18 KB
018.Modern RNNs/038. Modern RNNs LSTM and GRU.srt 17.21 KB
007.Matrix derivatives/013. Efficient MLP implementation.srt 16.62 KB
003.Linear model as the simplest neural network/004. Linear classification.srt 16.39 KB
010.Introduction to CNN/019. Motivation for convolutional layers.srt 15.97 KB
016.Generative Adversarial Networks/033. Applications of adversarial approach.srt 15.89 KB
016.Generative Adversarial Networks/032. Generative Adversarial Networks.srt 15.34 KB
015.Word Embeddings/029. Natural language processing primer.srt 15.32 KB
014.More Autoencoders/027. Autoencoder applications.srt 14.73 KB
008.TensorFlow framework/015. What is TensorFlow.srt 14.67 KB
009.Philosophy of deep learning/017. What Deep Learning is and is not.srt 13.9 KB
008.TensorFlow framework/016. Our first model in TensorFlow.srt 13.84 KB
018.Modern RNNs/037. Dealing with vanishing and exploding gradients.srt 13.67 KB
005.Stochastic methods for optimization/009. Gradient descent extensions.srt 13.38 KB
003.Linear model as the simplest neural network/003. Linear regression.srt 13.34 KB
010.Introduction to CNN/020. Our first CNN architecture.srt 13.32 KB
017.Introduction to RNN/035. Simple RNN and Backpropagation.srt 12.54 KB
009.Philosophy of deep learning/018. Deep learning as a language.srt 11.89 KB
006.The simplest neural network MLP/012. Backpropagation.srt 11.37 KB
016.Generative Adversarial Networks/031. Generative models 101.srt 11.22 KB
012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.srt 10.79 KB
014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.srt 10.64 KB
017.Introduction to RNN/034. Motivation for recurrent layers.srt 10.56 KB
018.Modern RNNs/036. The training of RNNs is not that easy.srt 10.36 KB
006.The simplest neural network MLP/011. Chain rule.srt 9.97 KB
004.Regularization in machine learning/006. Overfitting problem and model validation.srt 9.79 KB
013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.srt 9.54 KB
011.Modern CNNs/022. Overview of modern CNN architectures.srt 9.52 KB
002.Course intro/002. Course intro.srt 8.78 KB
007.Matrix derivatives/014. Other matrix derivatives.srt 8.57 KB
013.Intro to Unsupervised Learning/026. Autoencoders 101.srt 8.15 KB
005.Stochastic methods for optimization/008. Stochastic gradient descent.srt 7.76 KB
004.Regularization in machine learning/007. Model regularization.srt 7.43 KB
003.Linear model as the simplest neural network/005. Gradient descent.srt 7.41 KB
012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.srt 6.84 KB
001.Specialization Promo/001. Welcome to AML specialization!.srt 4.71 KB
[FTU Forum].url 252 B
[FreeCoursesOnline.Me].url 133 B
[FreeTutorials.Us].url 119 B
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