This course runs for a duration of 2 days.
The class will run daily from 8:30 am PST to 4:30 pm PST.
Class Location: San Diego - San Diego, CA.
TensorFlow is currently the most practical and accessible open-source machine learning tool for integrating intelligent features into our applications. Our organizations rely on petabytes of aggregated, structured data we’ve collected over the years. Even with good business intelligence practices, competitive position is increasingly defined by the ability to integrate an “intelligence layer” into the applications which ingest and utilize our data.
For software developers and data engineers ready to expand their silo and integrate intelligent services into applications, this TensoFlow course trains up engineering teams to build on domain expertise and design machine intelligence features using the popular TensorFlow library. TensorFlow delivers a myriad of deep learning best practices as part of its default configuration so complex machine learning just works out of the box.
TensorFlow democratizes deep learning for anyone with Python or C capability, but machine learning is not just another programming language. Our workshop allows you to learn from a live teacher, tie machine learning to value-driven use cases, and ask real-time questions in class.
Part 1: Overview of TensorFlow and TensorFlow libraries
Using Python with TensorFlow
Translating meaningful information into geometric spaces
Training infrastructure
Regressors
Keras API
Estimators and experiments
Part 2: Use cases for a machine learning service
Deep learning implementation
AI and macro insight opportunities
Image processing use cases
Predictive use cases
Scoring datasets for machine learning
Estimators
Part 3: Using and applying your model
Defining the model
Training the model
Evaluating the model
Prediction outputs
Part 4: Training your model
Setting up the training cycle
Training data
Adjusting bias
Weights
Part 5: Testing your model
Testing overview
Model values vs. output values
Part 6: Using TensorBoard to visualize model performance
Loss curve
Biases
Examining graphs
Learning rate decay
This course is for individuals with intermediate experience with Python and C. Any experience with TensorFlow is also beneficial.
