Classroom course

Deep Learning Course with TensorFlow, Keras and PyTorch


classroom course

Deep Learning Course with TensorFlow, Keras and PyTorch


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Price
1.430 € + VAT

Duration
16 hours

Data:
July 04th-05th

Qualification obtained
Certificate

Course code
AGSAI003

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TensorFlow, Keras and PyTorch are among the most used libraries in Research & Development and in the Industry world for the implementation of Neural Networks. This course represents an introductory guide to using these libraries. We will start from the concept of Tensor and carry on with the development and training of a Neural Network. Each library will be described by means of examples and there will be a comparison of the main features and differences, which one to choose depending on the applications, their pros and cons.

Who is it for?

The course is aimed at:

  • programmers
  • graduate students who know programming
Objective
The goal of this course is to provide you with all the tools to decide in autonomy which algorithm and architecture to apply depending on the use case and objectives; you will learn the syntax and the strengths of Tensorflow and PyTorch and you will be able to train from scratch a Neural Network.
Program
Tensorflow/Keras – Pytorch course
  • 1. The fundamentals of Machine Learning
    • 1.1 What is Machine Learning and when to use it
    • 1.2 Types of Machine Learning algorithm
    • 1.2.1 Supervised Learning
    • 1.2.2 Unsupervised Learning
  • 2. Neural Networks e Deep Learning
    • 2.1 Introduction to Neural Networks
    • 2.2 Multi layer perceptron
    • 2.3 Convolutional Neural Networks
    • 2.4 Recurrent neural networks
    • 2.4 Other architectures (Autoencoder, GAN, Transformers)
  • 3. Training Neural Networks
    • 3.1 Vanishing/Exploding Gradients
    • 3.2 Re-use of Pre-trained Layers
    • 3.3 Optimizers
    • 3.4 Overfitting e Regularization
  • 4. Design Neural Networks with Tensorflow 2.0
    • 4.1 Tensorflow Overview
    • 4.2 MLP with Tensorflow
    • 4.3 ConvNet with Tensorflow
    • 4.4 Recurrent Networks with Tensorflow
    • 4.5 Other Architectures
  • 5. Design Neutral Networks with PyTorch
    • 5.1 Pytorch Overview
    • 5.2 MLP with PyTorch
    • 5.3 ConvNet with PyTorch
    • 5.4 Recurrent Networks with Pytorch
    • 5.5 Other Architectures
  • 6. Advanced Topics with Tensorflow and Pytorch
    • 6.1 Memory management
    • 6.2 Training on GPUs
    • 6.3 Training on Google Colab
Classroom and safety rules
To comply with Covid safety regulations, the classroom will be equipped with special filtering systems special air filtering systems and participation will be limited to a maximum of 8 participants with a green pass. The course will be confirmed upon reaching the minimum number of 4 participants.

TensorFlow, Keras and PyTorch are among the most used libraries in Research & Development and in the Industry world for the implementation of Neural Networks. This course represents an introductory guide to using these libraries. We will start from the concept of Tensor and carry on with the development and training of a Neural Network. Each library will be described by means of examples and there will be a comparison of the main features and differences, which one to choose depending on the applications, their pros and cons.

Who is it for?

The course is aimed at:

  • programmers
  • graduate students who know programming
Objective
The goal of this course is to provide you with all the tools to decide in autonomy which algorithm and architecture to apply depending on the use case and objectives; you will learn the syntax and the strengths of Tensorflow and PyTorch and you will be able to train from scratch a Neural Network.
Program
Tensorflow/Keras – Pytorch course
  • 1. The fundamentals of Machine Learning
    • 1.1 What is Machine Learning and when to use it
    • 1.2 Types of Machine Learning algorithm
    • 1.2.1 Supervised Learning
    • 1.2.2 Unsupervised Learning
  • 2. Neural Networks e Deep Learning
    • 2.1 Introduction to Neural Networks
    • 2.2 Multi layer perceptron
    • 2.3 Convolutional Neural Networks
    • 2.4 Recurrent neural networks
    • 2.4 Other architectures (Autoencoder, GAN, Transformers)
  • 3. Training Neural Networks
    • 3.1 Vanishing/Exploding Gradients
    • 3.2 Re-use of Pre-trained Layers
    • 3.3 Optimizers
    • 3.4 Overfitting e Regularization
  • 4. Design Neural Networks with Tensorflow 2.0
    • 4.1 Tensorflow Overview
    • 4.2 MLP with Tensorflow
    • 4.3 ConvNet with Tensorflow
    • 4.4 Recurrent Networks with Tensorflow
    • 4.5 Other Architectures
  • 5. Design Neutral Networks with PyTorch
    • 5.1 Pytorch Overview
    • 5.2 MLP with PyTorch
    • 5.3 ConvNet with PyTorch
    • 5.4 Recurrent Networks with Pytorch
    • 5.5 Other Architectures
  • 6. Advanced Topics with Tensorflow and Pytorch
    • 6.1 Memory management
    • 6.2 Training on GPUs
    • 6.3 Training on Google Colab
Classroom and safety rules
To comply with Covid safety regulations, the classroom will be equipped with special filtering systems special air filtering systems and participation will be limited to a maximum of 8 participants with a green pass. The course will be confirmed upon reaching the minimum number of 4 participants.
REQUEST INFORMATION
Price
1.430 € + VAT

Duration
16 hours

Data:
July 04th-05th

Qualification obtained
Certificate

Course code
AGSAI003

subscribe