Introduction to deep learning-Course Introduction
A warm welcome from John Cohn, IBM Fellow Watson IoT
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IBM Digital Badge
Introduction - Romeo Kienzler
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Introduction - Ilja Rasin
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Introduction - Niketan Pansare
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Course Logistics
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Video summary on environment setup
Where to get all the code and slides for download?
Introduction to deep learning-Framework Introduction
Cloud Architectures for AI and DeepLearning
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Hands-on Lab: Sign Up for IBM Cloud Account
Introduction to deep learning-Mathematical Foundations on Neural Networks and DeepLearning
Linear algebra
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Deep feed forward neural networks
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Convolutional Neural Networks
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Recurrent neural networks
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LSTMs
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Auto encoders and representation learning
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Methods for neural network training
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Gradient Descent Updater Strategies
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How to choose the correct activation function
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The bias-variance tradeoff in deep learning
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Introduction to deep learning-Supplementary Materials
Link to Github
DeepLearning Frameworks-Introduction to TensorFlow
Intoduction to TensorFlow
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Neural Network Debugging with TensorBoard
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Automatic Differentiation
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DeepLearning Frameworks-Introduction to Keras
Introduction video
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Keras overview
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Sequential models in keras
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Feed forward networks
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Recurrent neural networks
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Beyond sequential models: the functional API
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Saving and loading models
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DeepLearning Frameworks-Introduction to Apache SystemML
What is SystemML (1/2)
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What is SystemML (2/2)
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DeepLearning Frameworks-Introduction to PyTorch
PyTorch Installation
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PyTorch Packages
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Tensor Creation and Visualization of Higher Dimensional Tensors
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Math Computation and Reshape
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Computation Graph, CUDA
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Linear Model
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DeepLearning Frameworks-Supplementary Materials
Link to files in Github
DeepLearning Applications-Anomaly Detection
Introduction to Anomaly Detection
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How to implement an anomaly detector (1/2)
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How to implement an anomaly detector (2/2)
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How to deploy a real-time anomaly detector
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DeepLearning Applications-Time Series Forecasting
Introduction to Time Series Forecasting
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Stateful vs. Stateless LSTMs
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Batch Size
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Number of Time Steps, Epochs, Training and Validation
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Trainin Set Size
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Input and Output Data Construction
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Designing the LSTM network in Keras
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Anatomy of a LSTM Node
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Number of Parameters
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Training and loading a saved model
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DeepLearning Applications-(Image) Classification
Classifying the MNIST dataset with Convolutional Neural Networks
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Image classification with Imagenet and Resnet50
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DeepLearning Applications-Natural Language Processing (NLP)
Autoencoder - understanding Word2Vec
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Text Classification with Word Embeddings
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Scaling and Deployment-Scaling Neural Networks with Apache SystemML
Run Keras Models in Parallel on Apache Spark using Apache SystemML
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Scaling and Deployment-Scaling Neural Networks with IBM Watson
Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning
Computer Vision with IBM Watson Visual Recognition
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Text Classification with IBM Watson Natural Language Classifier
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Scaling and Deployment-Supplementary Materials
Link to Github