
Online or onsite, instructor-led live TensorFlow training courses demonstrate through interactive discussion and hands-on practice how to use the TensorFlow system to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
TensorFlow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live TensorFlow trainings in the UK can be carried out locally on customer premises or in NobleProg corporate training centres.
NobleProg -- Your Local Training Provider
Testimonials
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
I started with close to zero knowledge, and by the end I was able to build and train my own networks.
Huawei Technologies Duesseldorf GmbH
Course: TensorFlow for Image Recognition
Very updated approach or api (tensorflow, kera, tflearn) to do machine learning
Paul Lee
Course: TensorFlow for Image Recognition
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course: TensorFlow Extended (TFX)
Very knowledgeable
Usama Adam - TWPI
Course: Natural Language Processing with TensorFlow
The way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course: Natural Language Processing with TensorFlow
Organisation, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course: Natural Language Processing with TensorFlow
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
TensorFlow Course Outlines
- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers
- Developers
- Data scientists
- Part lecture, part discussion, exercises and heavy hands-on practice
- Design and code DL for NLP using Python libraries.
- Create Python code that reads a substantially huge collection of pictures and generates keywords.
- Create Python Code that generates captions from the detected keywords.
- 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.
- By the end of this training, participants will be able to:
- Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
- Use OpenShift to simplify the work of initializing a Kubernetes cluster.
- Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
- Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
- Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
- 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.
- Build and train machine learning models with TensorFlow.js.
- Run existing machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning using custom data.
- Train, export and serve various TensorFlow models.
- Test and deploy algorithms using a single architecture and set of APIs.
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
- have a good understanding on deep neural networks(DNN), CNN and RNN
- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
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