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Suzhou, Jinghope Plaza

11/F, 11/F, Tower 2, 88 Hua Chi Street, SIP, Suzhou, China, 215021

Situated right in the core of the Suzhou industry zone, The Regus Centre is on the 11th floor of Jinghope Plaza. Jinghope Plaza is a mixed use complex comprising a high-end shopping mall, 5 star hotel and international Grade A office building. It is one of the most prestigious landmarks in the financial center of Suzhou. Advanced facilities and central location attract many Fortune 500 companies such as Nokia, Philips and 3M. Jinghope Plaza has easy access to excellent facilities and amenities in Suzhou. Metro Line 1 Times Square station is just in the basement of the complex. The high-speed train station which provides easy transportation to Shanghai and Nanjing is only a 5 minutes drive from the centre.

Jinhe Plaza

11th Floor, No. 2, 88 Huachi Street, Industrial Park, Suzhou, China, 215021

The center of Jinhe Plaza is located at the core of Suzhou Industrial Zone, on the east bank of Jinji Lake. Jinhe Plaza is a comprehensive building, including high-end shopping malls, five-star hotels, international Grade A office buildings, and Regus is located on the eleventh floor of the building. As the most famous landmark in Suzhou Financial Center, with its advanced facilities and central location, it has attracted many Fortune 500 companies such as Nokia, Philips and 3M. Jinhe Plaza provides easy access to all major core areas of Suzhou. Metro Line 1 Times Square Station is located on the basement of the building, while the High Speed ​​Rail Station to Shanghai and Nanjing is a 5-minute drive away.

Subcategories (5)

Stable Diffusion

Stable Diffusion
TensorFlow

TensorFlow
Neural Networks

Neural Networks
TensorFlow Lite

TensorFlow Lite
AlphaFold

AlphaFold

Explore Our Courses

Edge AI with TensorFlow Lite

 14 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

  • Understand 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.
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Computer Vision with Google Colab and TensorFlow

 21 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

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

  • Build and train convolutional neural networks (CNNs) using TensorFlow.
  • Leverage Google Colab for scalable and efficient cloud-based model development.
  • Implement image preprocessing techniques for computer vision tasks.
  • Deploy computer vision models for real-world applications.
  • Use transfer learning to enhance the performance of CNN models.
  • Visualize and interpret the results of image classification models.
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Deep Learning with TensorFlow in Google Colab

 14 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

  • Set up and navigate Google Colab for deep learning projects.
  • Understand the fundamentals of neural networks.
  • Implement deep learning models using TensorFlow.
  • Train and evaluate deep learning models.
  • Utilize advanced features of TensorFlow for deep learning.
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Deep Learning for NLU: Beyond NLP Models

 21 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at advanced-level professionals who wish 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.
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Explainability in Deep Learning: Demystifying Black-Box Models

 21 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building 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.
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Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

 21 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at intermediate to advanced-level 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 for better model performance and generalization.
  • Integrate Stable Diffusion with other deep learning frameworks and tools
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AI-Driven Drug Discovery and Development

 21 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at advanced-level professionals who wish to leverage 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.
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AlphaFold: AI-Driven Protein Structure Prediction and Interpretation

 7 Hours

This instructor-led, live training in SuZhou (online or onsite) 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.
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Introduction to Large Language Models (LLMs)

 14 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various 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.
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Introduction to Stable Diffusion for Text-to-Image Generation

 21 Hours

This instructor-led, live training in (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.
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Advanced Machine Learning with Python

 21 Hours

In this instructor-led, live training in SuZhou, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving 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.
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Applied AI from Scratch in Python

 28 Hours
This practical training in SuZhou helps programmers build AI models from scratch using Python. You will master supervised learning, neural networks, and unsupervised techniques using scikit-learn and Apache Spark. It focuses on hands-on Jupyter exercises for real-world problem solving.
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Deep Reinforcement Learning with Python

 21 Hours
This instructor-led training in SuZhou covers the theoretical foundations and practical implementation of Deep Reinforcement Learning using Python. Participants will build and train DRL agents with TensorFlow or PyTorch, applying key algorithms like DQN and PPO to solve complex real-world problems.
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Artificial Intelligence (AI) Overview

 7 Hours
An introductory training in SuZhou exploring AI fundamentals, from intelligent agents to machine learning. It equips executives and architects to assess emerging AI trends, integrate practical solutions, and accelerate business agility through automated strategies.
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Artificial Intelligence (AI) in Automotive

 14 Hours
Explore how Machine Learning and Deep Learning transform the automotive sector. This SuZhou course covers core concepts from simple automation to autonomous decision-making, including neural networks and practical TensorFlow examples for real-world applications.
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Artificial Neural Networks, Machine Learning, Deep Thinking

 21 Hours
This three-day program in SuZhou covers the theory and practice of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will explore network architectures, learning mechanisms, and mathematical foundations, moving from basic perceptrons to advanced deep learning techniques.
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Pattern Recognition

 21 Hours

This instructor-led, live training in SuZhou (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.
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Fundamentals of Deep Learning

 8 Hours

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

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Fraud Detection with Python and TensorFlow

 14 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.

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

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

 14 Hours

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
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Deep Learning with TensorFlow 2

 21 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.

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

  • Install and configure TensorFlow 2.x.
  • Understand the benefits of TensorFlow 2.x over previous versions.
  • Build deep learning models.
  • Implement an advanced image classifier.
  • Deploy a deep learning model to the cloud, mobile and IoT devices.
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Understanding Deep Neural Networks

 35 Hours
This 35-hour course in SuZhou covers deep neural network fundamentals, including CNNs, RNNs, and generative models like GANs. Participants gain hands-on experience with Theano and TensorFlow, learning to build, train, and deploy production-grade deep learning models for real-world applications.
Read more...

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Testimonials (5)

Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.

Matthew Gay - Tarsus Pharmaceuticals

Course - Artificial Intelligence (AI) Overview

The training was organized and well-planned out, and I come out of it with systematized knowledge and a good look at topics we looked at

Magdalena - Samsung Electronics Polska Sp. z o.o.

Course - Deep Learning with TensorFlow 2

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts

Course - Artificial Neural Networks, Machine Learning, Deep Thinking

That it was applying real company data. Trainer had a very good approach by making trainees participate and compete

Jimena Esquivel - Zaklad Uslugowy Hakoman Andrzej Cybulski

Course - Applied AI from Scratch in Python

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall

Course - Python for Advanced Machine Learning

Upcoming Courses

Fundamentals of Deep Learning

2026-10-19 09:30
8 hours
Suzhou, Jinghope Plaza

Computer Vision with Google Colab and TensorFlow

2026-11-02 09:30
21 hours
Jinhe Plaza

Artificial Intelligence (AI) Overview

2026-11-16 09:30
7 hours
Suzhou, Jinghope Plaza

Explainability in Deep Learning: Demystifying Black-Box Models

2026-11-30 09:30
21 hours
Jinhe Plaza

Fraud Detection with Python and TensorFlow

2026-12-14 09:30
14 hours
Suzhou, Jinghope Plaza

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