- Duration: 3 days
- Skill-level: Foundation-level Deep Learning with Python skills for Intermediate skilled team members. This is not a basic class.
- Targeted Audience: This course is geared for Python experienced developers, analysts or others who want to build understand through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable.
- Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required.
- Delivery Format: This course is available for onsite private classroom presentation.
- Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals.
Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, François Cholet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this edition includes new lessons, cutting-edge innovations, and coverage of the very latest deep learning tools. You’ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
Working in a hands-on learning environment, led by our Deep Learning with Python expert instructor, students will learn about and explore:
- You’ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models.
- By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below
- Deep learning from first principles
- Image-classification, imagine segmentation, and object detection
- Deep learning for natural language processing
- Timeseries forecasting
- Neural style transfer, text generation, and image generation
Audience & Pre-Requisites
This course is geared for attendees with Deep Learning with Python who wish to explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
Pre-Requisites: Students should have
- Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.
- Readers need intermediate Python skills.
- No previous experience with Keras, TensorFlow, or machine learning is required.
- Good foundational mathematics or logic skills
- Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
Course Agenda / Topics
- What is deep learning?
- Artificial intelligence, machine learning, and deep learning
- Before deep learning: a brief history of machine learning
- Why deep learning? Why now?
- The mathematical building blocks of neural networks
- A first look at a neural network
- Data representations for neural networks
- The gears of neural networks: tensor operations
- The engine of neural networks: gradient-based optimization
- Looking back at our first example
- Introduction to Keras and TensorFlow
- What’s TensorFlow?
- What’s Keras?
- Keras and TensorFlow: a brief history
- Setting up a deep-learning workspace
- First steps with TensorFlow
- Anatomy of a neural network: understanding core Keras APIs
- Getting started with neural networks: classification and regression
- Classifying movie reviews: a binary classification example
- Classifying newswires: a multiclass classification example
- Predicting house prices: a regression example
- Fundamentals of machine learning
- Generalization: the goal of machine learning
- Evaluating machine-learning models
- Improving model fit
- Improving generalization
- The universal workflow of machine learning
- Define the task
- Develop a model
- Deploy your model