Neural Networks Fundamentals using TensorFlow as Example Training Course

Course Code

Neuralnettf

Duration

28 hours (usually 4 days including breaks)

Requirements

Background in physics, mathematics and programming. Involvment in image processing activities.

Overview

This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

Course Outline

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 GraphS
    • 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

From the Perceptron to 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

Testimonials

★★★★★
★★★★★

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