Applied AI with DeepLearning


Applied AI with DeepLearning, offered by IBM, is a comprehensive course that provides invaluable insights into deep learning models used in various disciplines. Students will learn about the fundamentals of linear algebra and neural networks, and gain hands-on experience with popular deep learning frameworks such as Keras, TensorFlow, PyTorch, DeepLearning4J, and Apache SystemML.

The course covers topics like anomaly detection, time series forecasting, image recognition, and natural language processing using real-life examples from IoT, financial market data, literature, and image databases. Additionally, students will learn how to scale artificial intelligence models using Kubernetes, Apache Spark, and GPUs.

  • Gain insights into deep learning models
  • Hands-on experience with popular deep learning frameworks
  • Real-life examples from various disciplines
  • Scaling artificial intelligence models

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Applied AI with DeepLearning
Course Modules

The course modules cover deep learning fundamentals, frameworks like TensorFlow and PyTorch, applications such as anomaly detection and NLP, and scaling and deployment techniques, providing a comprehensive understanding of AI and deep learning.

Introduction to deep learning

The Introduction to deep learning module provides a comprehensive overview of linear algebra, deep feed forward neural networks, convolutional neural networks, recurrent neural networks, LSTMs, and various other essential concepts for building a strong foundation in deep learning.

DeepLearning Frameworks

The DeepLearning Frameworks module delves into popular frameworks like TensorFlow, Keras, PyTorch, and Apache SystemML, providing in-depth knowledge on installation, usage, features, and integration with other technologies. Additionally, this module covers neural network debugging, automatic differentiation, and model saving and loading techniques.

DeepLearning Applications

The DeepLearning Applications module focuses on practical applications such as anomaly detection, time series forecasting, image classification, and natural language processing. Students will learn to implement anomaly detectors, LSTM networks, and work with real-world datasets to gain hands-on experience in these areas.

Scaling and Deployment

The Scaling and Deployment module explores methods for parallel neural network training, scaling deep learning models on IBM Watson Machine Learning, and utilizing IBM Watson Visual Recognition and Natural Language Classifier for computer vision and text classification tasks. Additionally, it covers Apache SystemDS for deep learning neural network parallel training.

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