Who should attend
Developers, Software Engineers, Data Analysts, Data Scientists, Solution Architects, Systems Engineers and curious cats.
Prerequisites
Experience with an object-oriented programming language, e.g., Python is required (all code demos during the training will be in Python).
Course Objectives
By the end of this Learning Path, you will be able to develop, train, and deploy your models using TensorFlow, Keras, and Google Cloud Machine Learning Engine.
- Gain proficiency in building deep learning projects using TensorFlow without any need to delve into writing models from scratch
- Build a base for TensorFlow by implementing regression
- Solve prediction and image classification deep learning problems with TensorFlow
- Tackle the potential of RNN and LSTM neural networks with TensorFlow to solve time series problems
- Gain hands-on experience designing, training, and deploying your deep learning models with TensorFlow and Keras to handle large volumes of data and complex neural network architectures
- Design and experiment with complex neural network architectures using low-level TensorFlow while also using TensorFlow’s high level APIs and Keras
- Scale out training and prediction using different distributed techniques such as data parallelism using GPUs on your local machine and in the cloud using Google Cloud ML Engine
Course Content
This course is your step-by-step guide to exploring the possibilities in the field of deep learning, making use of Google’s TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data with the help of insightful examples that you can relate to and show how these can be exploited in the real world with complex raw data. You will also learn how to scale and deploy your deep learning models on the cloud using tools and frameworks such as TensorFlow, Keras, and Google Cloud Machine Learning Engine (MLE). This learning path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient deep learning.