This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
Backprop, modular models
Parameter Space Transforms
Energy for inference,
Objective for learning
Latent Variable Models
TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
This course requires working knowledge of Python and statistics.
This course is intended for engineers seeking to enter the world of Machine Learning or optimize their Machine Learning workflow.
After completing this course, participants will be able to:
Machine Learning and Recursive Neural Networks (RNN) basics
NN and RNN
Long short-term memory (LSTM)
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
Threading and Queues
Writing Documentation and Sharing your Model
Customizing Data Readers
Manipulating TensorFlow Model Files
Weekend Deep Learning courses, Evening Deep Learning training, Deep Learning boot camp, Deep Learning instructor-led
, Deep Learning instructor,Weekend Deep Learning training, Evening Deep Learning courses, Deep Learning trainer , Deep Learning private courses, Deep Learning coaching, Deep Learning one on one training
, Deep Learning classes, Deep Learning training courses