Code formation
Neuralnettf
Durée
28 heures (généralement 4 jours pauses comprises)
Pré requis
Background in physics, mathematics and programming. Involvment in image processing activities.
Aperçu
Ce cours vous donnera des connaissances sur les réseaux de neurones et plus généralement sur les algorithmes d’apprentissage automatique, d’apprentissage approfondi (algorithmes et applications).
Cette formation met davantage l'accent sur les principes fondamentaux, mais vous aidera à choisir la technologie TensorFlow : TensorFlow , Caffe , Teano, DeepDrive, Keras , etc. Les exemples sont réalisés dans TensorFlow .
Machine Translated
Plan du cours
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
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