Training: Deep Learning with TensorFlow and Neural Networks

Ref. AI-05-06

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Deep Learning with TensorFlow and Neural Networks Training

The Deep Learning with TensorFlow and Neural Networks training course enables participants to take their understanding of advanced artificial intelligence approaches to the next level. It helps them discover the fundamental concepts behind neural networks, deep learning and using a framework like TensorFlow for learning, modeling and implementation purposes.

An advanced training course for a deeper understanding of deep learning concepts

During this course, participants discover the key concepts of deep learning, the role of neuron layers, learning mechanisms, the main use cases and the conditions for implementation. The training provides a structured overview of the subject and prepares participants for further technical skill development on specialized environments and frameworks.

Participant Profiles

IT professionals
Technical project managers
Data professionals
Innovation managers
Anyone with a strong data or machine learning background who wants to understand the fundamentals of deep learning with TensorFlow

Objectives

Understand the principles of neural networks
Discover the fundamentals of deep learning
Identify the main use cases for deep learning
Understand the role of TensorFlow in model implementation
Connect theoretical concepts to practical applications
Prepare for progression toward more technical or hands-on approaches

Prerequisites

A solid understanding of machine learning is recommended
A general background in data and AI is desirable

Course Content

Module 1: Introduction to deep learning and neural networks

Definition of deep learning
Difference between traditional machine learning and deep learning
Understanding the logic of neural networks
Identifying typical use cases

Module 2: Understanding how a neural network works

Inputs
Hidden layers
Outputs
Weights
Learning
Propagation
Understanding simply how a network adjusts its parameters

Module 3: The main applications of deep learning

Computer vision
Natural language processing
Pattern recognition
Forecasting
Complex classification
Identifying the contexts in which deep learning becomes relevant

Module 4: Discovering TensorFlow

TensorFlow’s position in the AI ecosystem
Role of a deep learning framework
Understanding what it enables
Connecting the framework to the modeling and experimentation workflow

Module 5: Limitations, constraints and caution

Data requirements
Training time
Implementation complexity
Interpretability
Computing cost
The need for rigorous project scoping

Module 6: Preparing for further skill development

When to move toward deep learning
How to choose a good use case
What skills to mobilize
How to progress toward more technical environments

Documentation

Support de cours numérique inclus

Lab / Exercises

This course includes conceptual understanding workshops, case studies and analytical exercises to identify how deep learning and neural networks work, their use cases and key points of attention.

Complementary Courses

Eligible Funding

ITTA is a partner of a continuing education fund dedicated to temporary workers. This fund can subsidize your training, provided that you are subject to the “Service Provision” collective labor agreement (CCT) and meet certain conditions, including having worked at least 88 hours in the past 12 months.

Additional Information

TensorFlow, the reference framework for deep learning

TensorFlow, developed by Google, is one of the most widely used deep learning frameworks in the world. It offers a complete ecosystem for designing, training and deploying neural networks, from the simplest models to the most complex architectures. Its maturity and community make it a reliable choice for enterprise projects.

Mastering TensorFlow opens up considerable career opportunities in a market where demand for deep learning skills far exceeds supply.

Understanding neural networks

Artificial neural networks draw inspiration from the human brain to process information. Each neuron receives inputs, applies weights and produces an output. Stacking layers of neurons enables the processing of increasingly complex problems, from simple classifiers to the most advanced language models.

Network architectures vary according to the application. Convolutional networks excel at image processing, recurrent networks at sequence processing and transformers at natural language processing. Understanding these architectures helps choose the right approach for each problem.

Developing with TensorFlow and Keras

Keras, integrated into TensorFlow, provides a high-level API that simplifies the construction of neural networks. Defining a model, configuring training and evaluating results can be done in just a few lines of code. This accessibility allows developers to focus on model architecture rather than implementation details.

TensorFlow also offers advanced features for experienced users, such as eager mode for debugging, distribution strategies for distributed training and TensorFlow Serving for production deployment.

Practical applications of deep learning

Deep learning has applications across many domains. Image classification, object detection, speech recognition, automatic translation, text generation and recommendation are common enterprise use cases.

Pre-trained models and transfer learning deliver strong results even with limited datasets. This approach significantly reduces the cost and development time of deep learning solutions.

From prototype to production deployment

Moving from prototype to production is a major challenge in deep learning. TensorFlow provides tools for every stage: TensorFlow Lite for mobile deployment, TensorFlow.js for the browser and TensorFlow Serving for production APIs. MLOps pipelines with TFX automate the entire lifecycle.

ITTA offers this advanced technical training in Geneva and Lausanne for developers and engineers in French-speaking Switzerland who want to master deep learning with TensorFlow and build production-quality AI solutions.

AI-augmented data analysis in the Swiss context

Switzerland is a country where data culture is particularly well developed, especially in the finance, healthcare and manufacturing sectors. Artificial intelligence makes it possible to extract even more value from this data by making it accessible to a wider audience within organizations. Augmented analytics tools democratize access to data insights and enable every team member to contribute to informed decision-making.

Swiss requirements for data quality, analytical rigor and privacy protection create a demanding but beneficial framework for AI deployment. Organizations that train their teams in AI-driven data exploitation gain a lasting competitive advantage. The skills acquired are cross-functional and applicable regardless of the industry or company size.

Is Python knowledge required to learn TensorFlow?

Yes, Python is TensorFlow’s primary language. A command of Python basics and scientific libraries such as NumPy is recommended. Participants without Python experience can take a preliminary Python training course.

TensorFlow or PyTorch: which one to choose?

Both frameworks are excellent. TensorFlow excels in production deployment and its ecosystem of tools. PyTorch is often preferred for research and rapid prototyping. The TensorFlow training provides foundations that are transferable to PyTorch.

Is a GPU required for this training?

Training exercises use cloud environments such as Google Colab, which provide free access to GPUs. No specific hardware is required from participants.

Is deep learning suitable for every problem?

No, deep learning excels with unstructured data and complex problems but can be outperformed by simpler approaches on structured tabular data. The training teaches when to use deep learning and when to prefer alternatives.

What career opportunities does TensorFlow proficiency offer?

Deep learning and TensorFlow skills are in high demand across the technology, finance, healthcare and manufacturing sectors. ML Engineer, Data Scientist and AI Developer positions are among the most sought-after roles on the Swiss job market.