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Course Outline
Foundations of Machine Learning and Recursive Neural Networks (RNN)
- Neural Networks (NN) and RNNs
- Backpropagation
- Long Short-Term Memory (LSTM)
TensorFlow Fundamentals
- Creating, initializing, saving, and restoring TensorFlow variables
- Feeding, reading, and preloading data in TensorFlow
- Leveraging TensorFlow infrastructure for large-scale model training
- Visualizing and evaluating models using TensorBoard
TensorFlow Mechanics 101
- Tutorial Files
- Data Preparation
- Downloading Data
- Inputs and Placeholders
- Graph Construction
- Inference
- Loss Calculation
- Training Process
- Model Training
- The Graph
- The Session
- Training Loop
- Model Evaluation
- Constructing the Evaluation Graph
- Evaluation Outputs
Advanced Applications
- Threading and Queues
- Distributed TensorFlow
- Documentation and Model Sharing
- Custom Data Readers
- Utilizing GPUs¹
- Manipulating TensorFlow Model Files
TensorFlow Serving
- Introduction
- Basic Serving Tutorial
- Advanced Serving Tutorial
- Serving the Inception Model Tutorial
Convolutional Neural Networks
- Overview
- Objectives
- Tutorial Highlights
- Model Architecture
- Code Organization
- CIFAR-10 Model
- Model Inputs
- Model Predictions
- Model Training
- Launching and Training the Model
- Evaluating the Model
- Training a Model Using Multiple GPU Cards¹
- Assigning Variables and Operations to Devices
- Launching and Training the Model on Multiple GPU Cards
Deep Learning for MNIST
- Setup
- Loading MNIST Data
- Initiating TensorFlow InteractiveSession
- Constructing a Softmax Regression Model
- Placeholders
- Variables
- Predicted Class and Cost Function
- Training the Model
- Evaluating the Model
- Building a Multilayer Convolutional Network
- Weight Initialization
- Convolution and Pooling
- First Convolutional Layer
- Second Convolutional Layer
- Densely Connected Layer
- Readout Layer
- Training and Evaluating the Model
Image Recognition
- Inception-v3
- C++
- Java
¹ Topics related to the use of GPUs are not available as a part of a remote course. They can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.
Requirements
- Python
28 Hours
Testimonials (1)
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
