Deep Learning with PyTorch Training provides a comprehensive introduction to the tools and techniques available in PyTorch for developing, training, evaluating, and optimizing deep learning models.

PyTorch is an open-source deep learning framework recognized for its dynamic computation graph and flexible development environment. It supports the creation and improvement of neural network models across research projects and real-world applications.

The course begins with tensor operations and essential mathematical concepts before progressing to automatic differentiation (Autograd), neural network layers, model training, backpropagation, and optimization.

Participants also explore deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, generative models, and Graph Neural Networks (GNNs).

Additional topics include data preprocessing, distributed training, high-performance model development, hyperparameter optimization, model compression, acceleration, transfer learning, and production deployment.

The training aims to provide both theoretical understanding and practical knowledge for participants who want to develop end-to-end deep learning solutions using PyTorch.

For corporate groups, we can organize this training as an on-site private group.

Prerequisites

There are no mandatory prerequisites for attending this training.

Who Should Attend

Individuals interested in Artificial Intelligence and Machine Learning
University students and those pursuing a career in deep learning
Data Scientists and Data Engineers
Software Developers who want to integrate deep learning into their applications
Academics and Researchers
Professionals working in healthcare, finance, automotive, and retail
Technical teams and professionals interested in applying deep learning to business processes

What You Will Learn

Upon completing this training, you will be able to:

Design, train, and optimize deep learning models from scratch using PyTorch.
Understand fundamental tensor operations such as Broadcasting and torch.einsum.
Work with Dynamic Computation Graphs, Autograd, and Memory Management.
Configure neural network layers, Backpropagation, and model training loops.
Develop deep learning models using CNN and RNN architectures.
Explore modern architectures such as Transformers, Generative Models, and GNNs.
Use the PyTorch ecosystem for large-scale data and distributed training scenarios.
Apply Hyperparameter Optimization, Model Compression, and Acceleration techniques.
Understand Transfer Learning and Model Deployment workflows.
Develop scalable deep learning solutions for industry and research applications.

Training Outline

1. Introduction to Deep Learning and PyTorch

Evolution of Deep Learning

Historical background and fundamental concepts of deep learning
Development from Perceptron and Backpropagation to AlexNet, Transformers, and Diffusion models
Key differences between traditional Machine Learning and Deep Learning

PyTorch Ecosystem

Structure and applications of the PyTorch framework
TorchScript
torch-data
torch-metrics
torch-compile
PyTorch 2.x Dynamo
PyTorch community, release lifecycle, and LTS distributions

Mathematical Foundations of Deep Learning

Linear Algebra: Eigenvalues and Singular Value Decomposition (SVD)
Calculus: The Chain Rule
Jacobian and Hessian matrices
Probability and Information Theory
KL Divergence and Cross-Entropy

Tensor Operations

Understanding tensors in PyTorch
Broadcasting
Einstein Summation
Tensor operations using torch.einsum

2. PyTorch Fundamentals and Advanced Mechanisms

This section explores the core building blocks of PyTorch and the mechanisms used to develop deep learning models.

Dynamic Computation Graphs
Automatic differentiation with Autograd
Neural network layers
Modular model development
Memory management
Using PyTorch mechanisms throughout model development and training

3. Data Ecosystem and Preprocessing

This section focuses on preparing datasets for deep learning workflows.

Exploring the PyTorch data ecosystem
Data preprocessing
Preparing datasets for model training
Integrating data preparation into the model development workflow

4. Training Loops and Fundamental Neural Networks

Building neural network architectures
Structuring the model training loop
Forward Propagation
Backpropagation
Optimization processes
Model training and evaluation approaches

5. Convolutional Networks and Image Processing

Understanding Convolutional Neural Networks (CNNs)
The role of CNNs in image processing
Developing convolutional neural networks with PyTorch
Approaches to training models on image datasets

6. Additional Deep Learning Architectures

Other architectures identified in the course overview and learning outcomes include:

Recurrent Neural Networks (RNNs)
Transformer architectures
Generative Models
Graph Neural Networks (GNNs)
Evaluating the application areas of different architectures

7. Model Optimization and Production Workflows

Advanced topics reflected in the stated learning outcomes include:

Hyperparameter Optimization
Transfer Learning
Distributed Training
Large-scale data processing and high-performance model training
Model Compression
Model Acceleration
Model Deployment to production environments
Developing scalable and sustainable deep learning solutions

Why Choose Us

Leading UK-based global training provider since 1995.

Experience Deep Learning with PyTorch in Germany through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

Delivery Method: Online Instructor-Led
Participation Model: Public / Private (In-house)
Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
Worldwide Access: Join our live virtual classrooms from Germany or anywhere else in the world, or arrange a dedicated online training program for your organization.

Experience Deep Learning with PyTorch through face-to-face Classroom Based training in Germany. Training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class for your organization.

Delivery Method: Classroom Based
Participation Model: Public / Private (In-house)
Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange Deep Learning with PyTorch in Germany as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

Delivery Method: Onsite
Participation Model: Private (In-house)
Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
Worldwide Onsite Delivery: Arrange the training in Germany or at another preferred location worldwide. Bilginç IT Academy trainers can travel to your selected location to deliver the dedicated training program.

Deep Learning with PyTorch Training in Germany