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Neural Networks Fundamentals using TensorFlow as Example Training Course
Course
Online
Description
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Type
Course
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Methodology
Online
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 choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
About this course
Background in physics, mathematics and programming. Involvment in image processing activities.
Reviews
Subjects
- Algorithms
- Technology
- Mechanics
- Networks
Course programme
- 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
- 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
- Activation functions
- The perceptron learning algorithm
- Binary classification with the perceptron
- Document classification with the perceptron
- Limitations of the perceptron
- Kernels and the kernel trick
- Maximum margin classification and support vectors
- 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
- Goals
- Model Architecture
- Principles
- Code Organization
- Launching and Training the Model
- Evaluating a Model
Additional information
Neural Networks Fundamentals using TensorFlow as Example Training Course