Expert-led training for your team

TensorFlow training course

Gain a comprehensive introduction to TensorFlow - Google's open source software library for Deep Learning.

JBI training course London UK

"There was lots of in depth content on how to maximise the use of the software library for our business and an excellent, helpful trainer to guide us."

JP,  Software Engineer, TensorFlow, April 2021

Public Courses

01/07/24 - 3 days
£2500 +VAT
12/08/24 - 3 days
£2500 +VAT
23/09/24 - 3 days
£2500 +VAT

Customised Courses

* Train a team
* Tailor content
* Flex dates
From £1200 / day
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JBI training course London UK

  • Explore TensorFlow Basics
  • Create and initialise variables and data 
  • Use TensorFlow Mechanics to build graphs and train the model 
  • Gain knowledge about the perceptron learning algorithm and binary classification
  • Support vector machines: kernels and margin classification 
  • Acquire knowledge in feedforward and feedback Artificial Neural Networks
  • Learn Convolutional Neural Networks: explore model architecture and training 

Tensorflow Basics

  •          Creation, Initializing, Saving and Restoring TensorFlow variables
  •          Feeding, Reading and Preloading TensorFlow data
  •          How to use TensorFlow infrastructure to train models at scale
  •          Visualizing and Evaluating models with TensorBoard

TensorFlow Mechanics

  •          Inputs and Placeholders
  •          Build the Graph
    •    Inference
    •    Loss
    •    Training
  •          Train the model
    •    The graph
    •    The session
    •    Train loop
  •          Evaluate the model.
    •    Build the eval graph
    •    Eval output

The perceptron

  •          Activation functions
  •          The perceptron learning algorithm
  •          Binary classification with the perceptron
  •          Document classification with the perceptron
  •          Limitations of the perceptron

Support Vector Machines

  •          Kernels and the kernel trick.
  •          Maximum margin classification and support vectors

Artificial Neural Networks

  •          Nonlinear decision boundaries
  •          Feedforward and feedback artificial neural networks
  •          Multilayer perceptrons
  •          Minimizing the cost function
  •          Forward propagation
  •          Back propagation
  •          Improving the way neural networks learn

Convolutional Neural Networks

  •          Goals
  •          Model architecture
  •          Principles
  •          Code organization
  •          Launching and training the model.
  •          Evaluating a model. 
JBI training course London UK

The course is aimed at delegates with a Mathematical and/or Data Science/ML background.
Good programming knowledge, especially using the Python programming language.
Some experience and familiarity with the Pandas, Numpy and MatPlotLib python libraries for data analysis. 

5 star

4.8 out of 5 average

"There was lots of in depth content on how to maximise the use of the software library for our business and an excellent, helpful trainer to guide us."

JP,  Software Engineer, TensorFlow, April 2021

“JBI  did a great job of customizing their syllabus to suit our business  needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and  the Instructor handled this particularly well - very impressive”

Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022



JBI training course London UK



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Our TensorFlow training course for Deep Learning will give delegates a comprehensive introduction to this Google open-source software library, used by data science professionals for numerical computation using data flow graphs.

Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them.

The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organisation for the purposes of conducting Machine Learning and Deep Neural Networks research. The system is general enough to be applicable in a wide variety of other domains as well.

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