Introduction to Deep Learning Training

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Deep Learning is one of the pieces of the great puzzle called machine learning. Machine Learning is based on artificial neural networks and creates algorithms that mimic the functioning of the human brain. But we want to delve deeper – and here comes Deep Learning. Today, we have access to an immense amount of data – social media, search engines, e-commerce platforms, streaming services like HBO or Netflix – all of these are data mines. And there is a shortage of miners!

• For those starting their journey with Deep Learning, as well as for those who wish to deepen their knowledge of more advanced topics related to Data Science.
• For programmers, data analysts, business analysts, marketers, designers, and anyone for whom machine learning significantly eases their work.

• To acquire basic knowledge about Deep Learning, an advanced branch of machine learning that uses artificial neural networks to create algorithms mimicking the functioning of the human brain.
• To familiarize with various types of deep learning, the possibilities of this technology, hardware platform, and programming environment, including the use of cloud computing.
• To master the basics of TensorFlow, including structure, data types, data operations, Gradient Tape, and Stochastic Gradient Descent (SGD).

• You will gain skills in modeling, building fully connected networks in tf.keras, analyzing model quality, and tuning the model.
• You will master advanced techniques in Deep Learning, including low-level network construction, regularization, using Tensorboard, analyzing model parameters, and TensorFlow callbacks.
• You will learn to save and load models, which is crucial for the practical use of Deep Learning, and create artificial neural networks using tf.keras, including understanding the theory and inspiration behind neurons, layers, and network flexibility.

• Introduction

  • Types of Deep Learning

  • Possibilities of Deep Learning

  • Hardware Platform and Programming Environment

  • Cloud Utilization Possibilities

  • TensorFlow – Basics

  • Structure

  • Data Types

  • Data Operations

  • Gradient Tape

  • SGD

• Artificial Neural Network using tf.keras

  • Theory and Inspiration

  • Neuron

  • Layers

  • Flexibility of ANN

  • Types of ANN Models

• Modeling

  • Building Fully Connected Networks in tf.keras

  • Solving a "Simple" Problem

  • Model Quality Analysis

  • Model Tuning

• Extension

  • Low-Level Network Construction

  • Regularization

  • TensorBoard

  • Model Parameter Analysis

  • TensorFlow Callbacks

  • Saving and Loading Models

40 h/ 5 days

• Certificate of Completion
• Monthly Access to Training Recording (for online format)
• Customization of Training Program to Client Needs