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Курс: Applied AI with DeepLearning. Чтобы вернуться, нажмите здесь.
- A warm welcome from John Cohn, IBM Fellow Watson IoT
- IBM Digital Badge
- Introduction - Romeo Kienzler
- Introduction - Ilja Rasin
- Introduction - Niketan Pansare
- Introduction - Tom Hanlon
- Course Logistics
- Video summary on environment setup
- Where to get all the code and slides for download?
- Cloud Architectures for AI and DeepLearning
- IMPORTANT: How to submit your programming assignments
- 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
- Introduction to ApacheSpark
- Link to Github
- 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) ?
- Demo - How to use Apache SystemML on IBM DSX (1/3)
- Demo - How to use Apache SystemML on IBM DSX (2/3)
- Demo - How to use Apache SystemML on IBM DSX (3/3)
- Introduction to DeepLearning4J
- Demo: Running Java in Data Science Experience
- DL4J Neural Network Code Example, Mnist Classifier
- PyTorch Installation
- PyTorch Packages
- Tensor Creation and Visualization of Higher Dimensional Tensors
- Math Computation and Reshape
- Computation Graph, CUDA
- Linear Model
- Link to files in Github
- 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
- Generative Adversarial Networks (GANs) (optional)
- Parallel Neural Network Training
- Run Keras Models in Parallel on Apache Spark using Apache SystemML
- Creating and Scaling a Keras Model in ApacheSpark using DL4J
- Creating and Scaling a Keras Model in ApacheSpark using DL4J (Demo)
- Scale a Keras Model with IBM Watson Machine Learning
- Computer Vision with IBM Watson Visual Recognition
- Text Classification with IBM Watson Natural Language Classifier
- Link to Github