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TensorFlow 2.0

  • Course Code: Artificial Intelligence - TensorFlow 2.0
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level TensorFlow skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

TensorFlow is one of the most popular machine learning frameworks in Python. With this course, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what’s new in TensorFlow 2.0 Alpha, the course moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The course will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the course, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques. 

Working in a hands-on learning environment, led by our TensorFlow 2.0 expert instructor, students will learn about and explore: 

  • Train your own models for effective prediction, using high-level Keras API 
  • Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks 
  • Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Use tf.Keras for fast prototyping, building, and training deep learning neural network models 
  • Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files 
  • Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications 
  • Understand image recognition techniques using TensorFlow 
  • Perform neural style transfer for image hybridization using a neural network 
  • Code a recurrent neural network in TensorFlow to perform text-style generation 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introducing TensorFlow 2 
  • Introducing TensorFlow 2 
  • Looking at the modern TensorFlow ecosystem 
  • Installing TensorFlow 
  • Housekeeping and eager operations 
  • Providing useful TensorFlow operations 
  1. Keras, a High-Level API for TensorFlow 2 
  • Keras, a High-Level API for TensorFlow 2 
  • The adoption and advantages of Keras 
  • The features of Keras 
  • The default Keras configuration file 
  • The Keras backend 
  • Keras data types 
  • Keras models 
  1. ANN Technologies Using TensorFlow 2 
  • ANN Technologies Using TensorFlow 2 
  • Presenting data to an ANN 
  • One-hot encoding 
  • Layers 
  • Activation functions 
  • Creating the model 
  • Gradient calculations for gradient descent algorithms 
  • Loss functions 
  1. Supervised Machine Learning Using TensorFlow 2 
  • Supervised Machine Learning Using TensorFlow 2 
  • Supervised learning 
  • Linear regression 
  • Our first linear regression example 
  • The Boston housing dataset 
  • Logistic regression (classification) 
  • k-Nearest Neighbors (KNN) 
  1. Unsupervised Learning Using TensorFlow 2 
  • Unsupervised Learning Using TensorFlow 2 
  • Autoencoders 
  1. Recognizing Images with TensorFlow 2 
  • Recognizing Images with TensorFlow 2 
  • Quick Draw – image classification using TensorFlow 
  • CIFAR 10 image classification using TensorFlow 
  1. Neural Style Transfer Using TensorFlow 2 
  • Neural Style Transfer Using TensorFlow 2 
  • Setting up the imports 
  • Preprocessing the images 
  • Viewing the original images 
  • Using the VGG19 architecture 
  • Creating the model 
  • Calculating the losses 
  • Performing the style transfer 
  • Final displays 
  1. Recurrent Neural Networks Using TensorFlow 2 
  • Recurrent Neural Networks Using TensorFlow 2 
  • Neural network processing modes 
  • Recurrent architectures 
  • An application of RNNs 
  • The code for our RNN example 
  • Building and instantiating our model 
  • Using our model to get predictions 
  1. TensorFlow Estimators and TensorFlow Hub 
  • TensorFlow Estimators and TensorFlow Hub 
  • TensorFlow Estimators 
  • TensorFlow Hub 
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