[FreeCoursesOnline.Me] Coursera - Introduction to Deep Learning

011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp4

019.Applications of RNNs/039. Practical use cases for RNNs.mp4

015.Word Embeddings/030. Word embeddings.mp4

018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp4

007.Matrix derivatives/013. Efficient MLP implementation.mp4

006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp4

003.Linear model as the simplest neural network/004. Linear classification.mp4

010.Introduction to CNN/020. Our first CNN architecture.mp4

016.Generative Adversarial Networks/033. Applications of adversarial approach.mp4

010.Introduction to CNN/019. Motivation for convolutional layers.mp4

014.More Autoencoders/027. Autoencoder applications.mp4

008.TensorFlow framework/015. What is TensorFlow.mp4

008.TensorFlow framework/016. Our first model in TensorFlow.mp4

015.Word Embeddings/029. Natural language processing primer.mp4

005.Stochastic methods for optimization/009. Gradient descent extensions.mp4

016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp4

003.Linear model as the simplest neural network/003. Linear regression.mp4

017.Introduction to RNN/035. Simple RNN and Backpropagation.mp4

018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp4

011.Modern CNNs/022. Overview of modern CNN architectures.mp4

006.The simplest neural network MLP/012. Backpropagation.mp4

012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp4

017.Introduction to RNN/034. Motivation for recurrent layers.mp4

009.Philosophy of deep learning/017. What Deep Learning is and is not.mp4

014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp4

016.Generative Adversarial Networks/031. Generative models 101.mp4

006.The simplest neural network MLP/011. Chain rule.mp4

004.Regularization in machine learning/006. Overfitting problem and model validation.mp4

018.Modern RNNs/036. The training of RNNs is not that easy.mp4

009.Philosophy of deep learning/018. Deep learning as a language.mp4

013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp4

013.Intro to Unsupervised Learning/026. Autoencoders 101.mp4

002.Course intro/002. Course intro.mp4

007.Matrix derivatives/014. Other matrix derivatives.mp4

005.Stochastic methods for optimization/008. Stochastic gradient descent.mp4

004.Regularization in machine learning/007. Model regularization.mp4

012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp4

003.Linear model as the simplest neural network/005. Gradient descent.mp4

001.Specialization Promo/001. Welcome to AML specialization!.mp4

015.Word Embeddings/030. Word embeddings.srt

019.Applications of RNNs/039. Practical use cases for RNNs.srt

006.The simplest neural network MLP/010. Multilayer perceptron (MLP).srt

011.Modern CNNs/021. Training tips and tricks for deep CNNs.srt

018.Modern RNNs/038. Modern RNNs LSTM and GRU.srt

007.Matrix derivatives/013. Efficient MLP implementation.srt

003.Linear model as the simplest neural network/004. Linear classification.srt

010.Introduction to CNN/019. Motivation for convolutional layers.srt

016.Generative Adversarial Networks/033. Applications of adversarial approach.srt

016.Generative Adversarial Networks/032. Generative Adversarial Networks.srt

015.Word Embeddings/029. Natural language processing primer.srt