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Deeply learn from these Deep Learning Books
- Dive into machine learning concepts in general, as well as deep learning in particular
- Understand how deep networks evolved from neural network fundamentals
- Explore the major deep network architectures, including Convolutional and Recurrent
- Learn how to map specific deep networks to the right problem
- Walkthrough the fundamentals of tuning general neural networks and specific deep network architectures
- Use vectorization techniques for different data types with DataVec, DL4J's workflow tool
- Learn how to use DL4J natively on Spark and Hadoop
- Deep learning from first principles
- Setting up your own deep-learning environment
- Image classification and generation
- Deep learning for text and sequences
- Implement advanced techniques in the right way in Python and TensorFlow
- Debug and optimize advanced methods (such as dropout and regularization)
- Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
- Set up a machine learning project focused on deep learning on a complex dataset