Deep Learning with TensorFlow
TensorFlow for Image Recognition
This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition
Audience
This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition
After completing this course, delegates will be able to:
- 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
Here is what you will get with this course:
Machine Learning and Recursive Neural Networks (RNN) basics
- NN and RNN
- Backpropagation
- Long short-term memory (LSTM)
TensorFlow Basics
- Creation, Initializing, Saving, and Restoring TensorFlow variables
- Feeding, Reading and Preloading TensorFlow Data
- How to use TensorFlow infrastructure to train models at scale
- Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics
- Tutorial Files
- Prepare the Data
- Download
- Inputs and Placeholders
- Build the Graph
- Inference
- Loss/Accuracy
- Training
- Train the Model
- Graph
- Session
- Train Loop (Epochs)
- Evaluate the Model
- Build the Eval Graph
- Eval Output
Advanced Usage
- Threading and Queues
- Distributed TensorFlow
- Writing Documentation and Sharing your Model
- Customizing Data Readers
- Using GPU's
- Manipulating TensorFlow Model Files
TensorFlow Serving
- Introduction
- Basic Serving Tutorial
- Advanced Serving Tutorial
- Serving Inception Model Tutorial
Convolutional Neural Networks (CNN)
- Overview
- Main Goals
- Highlights of the Tutorial
- Model Architecture
- Code Organization
- CIFAR-10 Model
- Model Inputs
- Model Prediction
- Model Training
- Launching and Training the Model
- Evaluating a Model
- Training a Model Using Multiple GPU Cards
- Placing Variables and Operations on Devices
- Launching and Training the Model on Multiple GPU cards
Deep Learning for MNIST
- Setup
- Load MNIST Data
- Start TensorFlow Interactive Session
- Build a Softmax Regression Model
- Placeholders
- Variables
- Predicted Class and Cost Function
- Train the Model
- Evaluate the Model
- Build a Multilayer Convolutional Network
- Weight Initialization
- Convolution and Pooling
- First Convolutional Layer
- Second Convolutional Layer
- Densely Connected Layer
- Readout Layer
- Train and Evaluate the Model
Requirements for live participation:
- Customary computer or laptop (64-bit), headset, webcam
- Stable internet connection
- NVIDIA GPUs
- At least 2 GB of RAM
Who this course is for:
- Anyone who wants to pass the TensorFlow Developer exam so they can join Google's Certificate Network and display their certificate and badges on their resume, GitHub, and social media platforms including LinkedIn, making it easy to share their level of TensorFlow expertise with the world
- Students, developers, and data scientists who want to demonstrate practical machine learning skills through the building and training of models using TensorFlow
- Anyone looking to expand their knowledge when it comes to AI, Machine Learning and Deep Learning
- Anyone looking to master building ML models with the latest version of TensorFlow
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Duration 28 Hours Price per participant 3.800 EUR | |
Language/Documentation English Germany | |
Participants
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Contact
If you are interested in a company-specific custom development and would like to find out more, please feel free to get in touch with us.
Give us a call on: +49 (0) 176 310 693 62
or send an email to: info@inovaitec.com
Alternatively, You can fill out our contact form here. We look forward to hearing from you.