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