Deep Learning Training in Queretaro

This instructor-led, live training in Queretaro (online or onsite) targets intermediate-level developers, data scientists, and AI practitioners seeking to leverage TensorFlow Lite for Edge AI applications.

By the conclusion of this training, participants will be able to:

• Comprehend the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This live, instructor-led training in Queretaro (delivered online or onsite) is tailored for advanced professionals seeking to refine their knowledge of computer vision and utilize TensorFlow’s capabilities to build sophisticated vision models via Google Colab.

Upon completion, participants will be equipped to:

• Construct and train convolutional neural networks (CNNs) with TensorFlow.
• Utilize Google Colab to facilitate scalable and efficient cloud-based model development.
• Apply image preprocessing techniques tailored for computer vision tasks.
• Deploy computer vision models for practical, real-world use cases.
• Employ transfer learning strategies to boost the performance of CNN models.
• Visualize and analyze the outcomes of image classification models.

This instructor-led, live training in Queretaro (online or onsite) is designed for intermediate-level data scientists and developers aiming to understand and implement deep learning techniques using the Google Colab environment.

Upon completing this training, participants will be capable of:

• Setting up and navigating Google Colab for deep learning initiatives.
• Grasping the core concepts of neural networks.
• Developing deep learning models using TensorFlow.
• Training and assessing deep learning models.
• Leveraging advanced TensorFlow features for deep learning.

This instructor-led, live training in Queretaro (online or in-person) is designed for advanced professionals who want to specialize in cutting-edge deep learning techniques for NLU.

By the end of this training, participants will be able to:

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

This instructor-led, live training in Queretaro (online or onsite) targets advanced-level professionals who wish to explore cutting-edge XAI techniques for deep learning models, focusing on the creation of interpretable AI systems.

By the end of this training, participants will be able to:

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This instructor-led live training in Queretaro (online or onsite) is aimed at intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters to improve model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led, live training in Queretaro (online or onsite) is designed for advanced professionals who want to use AI techniques to revolutionize drug discovery and development processes.

By the end of this training, participants will be able to:

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

This instructor-led, live training in Queretaro (online or in-person) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

This instructor-led, live training session in Queretaro (online or onsite) targets beginner to intermediate-level developers who wish to utilize Large Language Models for a range of natural language tasks.

By the end of this training, participants will be able to:

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training (available online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

During this instructor-led, live training in Queretaro, participants will learn the most relevant and cutting-edge machine learning techniques in Python by building a series of demo applications that involve image, music, text, and financial data.

By the end of this training, participants will be able to:

• Implement machine learning algorithms and techniques for solving complex problems.
• Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Push Python algorithms to their maximum potential.
• Use libraries and packages such as NumPy and Theano.

This instructor-led, live training in Queretaro (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

This instructor-led, live training in Queretaro (online or on-site) is targeted at software developers, data analysts, and technical professionals who aim to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

This instructor-led live training in Queretaro (online or onsite) is designed for data scientists interested in using TensorFlow to analyze potential fraud data.

By the conclusion of this training, participants will be able to:

• Develop a fraud detection model using Python and TensorFlow.
• Build linear regression models to predict fraud.
• Create an end-to-end AI application for analyzing fraud data.

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

• Build a deep learning model
• Automate data labeling
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Queretaro (delivered online or on-site) targets developers and data scientists who intend to utilize TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and more.

By the end of this program, participants will be capable of:

• Installing and configuring TensorFlow 2.x.
• Understanding the benefits of TensorFlow 2.x over previous iterations.
• Constructing deep learning models.
• Implementing an advanced image classifier.
• Deploying deep learning models to the cloud, mobile, and IoT devices.