Deep Learning

This course covers deep learning, neural networks, and image classification using TensorFlow and optimization techniques.

introduction

Deep Learning

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For who?

We recommend this course if you:

This course is for:

  • Data Scientists and Machine Learning Engineers looking to deepen their understanding of deep learning algorithms.
  • AI Enthusiasts who want to explore the mathematics and architecture behind neural networks and deep learning.
  • Developers aiming to apply deep learning techniques to solve real-world problems like image classification and object detection.
  • Researchers in fields such as computer vision and artificial intelligence seeking to expand their technical knowledge.
  • Students pursuing careers in AI or data science who want to build a strong foundation in deep learning.
  • Technology Consultants interested in advising companies on implementing AI solutions using deep learning.

By the end of the course, participants will be able to apply deep learning techniques, including neural networks and image classification, using TensorFlow and optimization methods.

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features

Advantages and features of the course:

Workshop Overview

Many factors influenced the rise of AI and the launch of the fourth technological revolution. However, one primary invention that accelerated the process was the ability to transform images into information. This breakthrough paved the way for transforming videos, texts, and audio into information, resulting in advancements such as driverless cars, bots, and automation that almost match human abilities. This workshop focuses on the algorithms behind this technological breakthrough, making AI a reality and allowing you to apply deep learning algorithms to solve new, challenging problems.

Learning Outcomes

  • Learn the mathematics behind Deep Learning.
  • Explore the logic of optimization with Gradient Descent.
  • Dissect components of neural networks.
  • Adjust hyperparameters of algorithms to optimize cost functions.
  • Explore the architecture of main deep learning networks.
  • Improve the accuracy of Classification and Estimation.
  • Establish knowledge in:

                   - Image classification

                   - Face recognition, and

                   - Object detection.

Duration 4 days

Day 1:

- Algebra and Calculus

Day 2:

- Gradient Descent

- Perceptron Algorithm

Day 3:

- Feedforward Neural Networks

Day 4:

- Convolutional Neural Networks

What will it be about?

- Comprehensive colored PPT documents.

- Neurons, Hidden layers, Synapsis, ...

- Weights, Scores, ...

- Activation functions: Sigmoid, TanH, ...

- SoftMax rule

- Feed Forward of information

- Backpropagation

- Convolution windows, MaxReLu, ...

- TensorFlow coding applications

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Teacher leading this course

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We believe the solution lies at the intersection of education and technology innovation.
About coach
certificate

It is difficult to obtain.
And it is valued by employers.

We have partnerships with international
professional organizations that specialize in professional training and have unique and up-to-date quality programs for our students.

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