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Course Skill Level:

Intermediate to Advanced

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT and Linux skills

About this course

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.

Skills acquired & topics covered

  • Training your own models for effective prediction, using high-level Keras API
  • Performing supervised and unsupervised machine learning and learn advanced techniques such as training neural networks
  • Getting acquainted with some new practices introduced in TensorFlow 2.0 Alpha
  • Using tf.Keras for fast prototyping, building, and training deep learning neural network models
  • Converting your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files
  • Using TensorFlow to tackle traditional supervised and unsupervised machine learning applications
  • Image recognition techniques using TensorFlow
  • Performing neural style transfer for image hybridization using a neural network
  • Coding a recurrent neural network in TensorFlow to perform text-style generation

Course breakdown / modules

  • Looking at the modern TensorFlow ecosystem
  • Installing TensorFlow
  • Housekeeping and eager operations
  • Providing useful TensorFlow operations

  • The adoption and advantages of Keras
  • The features of Keras
  • The default Keras configuration file
  • The Keras backend
  • Keras data types
  • Keras models

  • Presenting data to an ANN
  • One-hot encoding
  • Layers
  • Activation functions
  • Creating the model
  • Gradient calculations for gradient descent algorithms
  • Loss functions

  • Supervised learning
  • Linear regression
  • Our first linear regression example
  • The Boston housing dataset
  • Logistic regression (classification)
  • k-Nearest Neighbors (KNN)

  • Autoencoders

  • Quick Draw – image classification using TensorFlow
  • CIFAR 10 image classification using TensorFlow

  • 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

  • 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

  • TensorFlow Estimators
  • TensorFlow Hub