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.
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
Outline: Deep Learning Bootcamp with Google’s TensorFlow and Keras: Deep Learning Bootcamp with Google’s TensorFlow and Keras (DSAIT01)
Module 1: Deep Learning Fundamentals
- The role of Training datasets in AI
- Introduction to Neural Networks and Deep Learning
- Essential Deep Learning Theory
- Deep Learning Frameworks and Libraries
Module 2: Deep Learning with Keras, TensorFlow’s High-Level API
- Shallow Neural Network in Keras
- Learning with Artificial Neurons
- TensorFlow Playground – visualizing a Deep Net in Action
Module 3: Introduction to TensorFlow
- TensorFlow Primer (matrix operations, computation graphs and data types)
- Build and Train Deep Nets on TensorFlow
- Handling data in TensorFlow (fitting, hyper-parameter tuning)
Module 4: Deep Learning with TensorFlow
- Elements of TensorFlow Graph
- Constructing and Executing a TensorFlow Graph
- Training a (Simple) Linear model in TensorFlow
Module 5: Working with TensorFlow Graphs
- Elements of TensorFlow Graph
- Constructing and Executing a TensorFlow Graph
- Training a (Simple) Linear model in TensorFlow
Module 6: Dense Neural Networks in TensorFlow
- Review a Dense Net in Keras
- Designing a Dense Net in TensorFlow
- Training a Dense Net in TensorFlow
Module 7: Convolutional Neural Networks in TensorFlow
- Review a ConvNet in Keras
- ConvNets in TensorFlow
- When to use TensorFlow vs Keras or another API
Module 8a: Project: Deep Learning Application for Machine Computer Vision
Module 8b: Project: Deep Learning Application for Natural Language Processing (NLP)
Module 9: Summary and Project Review