This course combines engaging instructor-led sessions, valuable demonstrations, and hands-on labs designed to help you:

• Understand and implement neural networks using TensorFlow, a powerful deep learning framework.
• Load, preprocess, and manage various types of data including tabular, image, text, and audio data.
• Build and optimize regression and classification models, learning key metrics and techniques for tuning performance.
• Utilize TensorFlow tools like TensorBoard for model visualization and TensorFlow Hub for leveraging pre-trained networks.
• Apply regularization techniques and tune hyperparameters to improve model generalization.
• Explore advanced deep learning strategies like transfer learning, feature extraction, and using pre-trained networks to accelerate model development.

This course is designed for technical professionals who are ready to integrate deep learning into their data projects. It's ideal for data scientists, developers, machine learning engineers, and analysts who want to extend their skill set into deep learning. It's also suited for business analysts and other technical stakeholders involved in data-driven decision-making who need to understand the potential of deep learning in solving complex business challenges. Python experience is required.

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We'll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.

1. Introduction to Machine Learning with TensorFlow

• Implementing Artificial Neural Networks in TensorFlow
• The TensorFlow Library
• Introduction to Tensors
• Tensor Addition
• Reshaping
• Tensor Multiplication
• Optimization
• Activation functions

2. Loading and Processing Data

• Understand how to create an effective deep learning environment.
• Exploring Data Types
• Data Preprocessing
• Processing Tabular Data
• Processing Image Data
• Image Augmentation
• Text Processing
• Audio Processing

3. TensorFlow Development

• TensorBoard
• TensorFlow Hub

4. Regression and Classification Models

• Introduction
• Sequential Models
• Model Fitting
• Classification Models

5. Classification Models

• Introduction
• Binary Classification
• Metrics for Classifiers
• Multi-Class Classification
• Multi-Label Classification

6. Regularization and Hyperparameter Tuning

• Regularization Techniques
• Hyperparameter Tuning

7. Pre-Trained Networks

• ImageNet
• Transfer Learning
• Fine-Tuning
• TensorFlow Hub
• Feature Extraction

Bonus Content: Exploring Generative Models in Deep Learning

Bonus: Recurrent Neural Networks

• Sequential Data
• Recurrent Neural Networks
• Natural Language Processing
• Backpropagation Through Time (BPTT)

Bonus: Generative Models

• Text Generation
• Generative Adversarial Networks
• Deep Convolutional Generative Adversarial Networks (DCGANs)