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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

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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. 


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 training course London UK
 
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