In this 2-days bootcamp, you will learn the fundamentals of Deep Learning primarily through a series of hands on exercises guided by the instructor. Students will learn about the foundational underpinnings of machine learning and deep learning as well as how to put that knowledge to the test with practical exercises. Identify and frame problems that can be solved by deep learning
Choose the right techniques to the problems
Understand key deep learning concepts and how deep learning models work
Build deep neural nets for classification on images as well as structured data using the Keras.
Build convolutional neural networks for image classification and segmentation using the Keras.
Build deep learning pipeline in prodution and learn AI engineer careers
The course takes unique project focused approach to teach you deep learning by building deep learning models. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code, and building neural networks.
The course will balance learning theory, working on projects, and hearing from guest speakers who are industry practitioners in the field.
Students who take this course will be able to:
- Aug 20th Tue 9am-4:00pm PT
- Aug 21st Wed 9am-4:00pm PT
- Practical walkthroughs that present solutions to actual, real-world deep learning problems and challenges
- Hands-on tutorials (with lots of code) that not only show you the algorithms behind deep learning but their implementations as well
- A no-nonsense teaching style that is guaranteed to cut through all the cruft and help you master deep learning
- Breakfast, lunch, coffee breaks, and happy hour
Check the content tab for full course outlines.
WHO SHOULD LEARN:
Developers, data scientists, students who want to get started on building deep learning projects or applications
Overall score: 4.7 / 5
Score: 5 (68%); 4 (32%)
Day 1 (9am -4:00pm)
Module 1: Machine Learning & Neural Network Fundamentals
- Deep learning applications, key concepts and terminology
- Neural network architecture and theory
- Code lab environment setup
- Code lab 1: build first deep learning model to classify handwritten digits
Module 2: Improving Deep Neural Networks
- Identify tasks that are well and ill suited to deep learning
- Underfitting and overfitting, Regularization tactics
- Feature engineering
- Optimizers and training parameters
- Code Lab 2: Explore the impact different neural network architectures
- Code Lab 3: Explore the impact of different optimizers, dropout, and regularization tactics
Module 3: Data Cleaning and Preprocessing
- Define and identify common problems with training data
- Describe and apply tactics for improving data quality and improve network performance
- Evaluate the appropriateness of applying said tactics.
- Code Lab 4: Clean up a messy dataset
- Code Lab 5: Improve a dataset with synthetic data and augmentation
Module 4: Convolutional neural networks for classification and segmentation
- Identify tasks for which CNNs are well suited
- Compare and contrast classification with segmentation
- Build CNNs from scratch using Keras and evaluate their performance
- Code Lab 6: Build your first CNN to classify images
- Code Lab 7: Build CNN model to identify items within an image
Day 2 (9am -4:00pm)
Module 5: Continue with CNN 2
- Transfer learning and identify examples of situations where it might help
- Import well known CNN architectures and leverage transfer learning using Keras
- Code Lab 8: Identify flower species project
- Code Lab 9: Import pre-trained CNN’s and apply Transfer Learning have classify a different dataset
Module 6: CNN and Transfer Learning
- Improving image datasets with data augmentation.
- Define transfer learning, and identify its strengths and weaknesses.
- Discuss freezing layers and mitigating overfitting with transfer learning.
- Exercise: Implement transfer learning with the Cifar100 dataset.
Module 7: Deep Learning in Production
- Deep learning in enterprise application
- Deep Learning produciton lifecycle
- Tools/frameworks in production
Module 8: AI/Deep Learning Engineers
- knowledges and skills are required for AI Engineer
- Career path for AI Engineer
12:00-1:00pm: Lunch break
Tyler and Jack
Tyler is an educator, technologist, programmer, and all around curious human. He holds a bachelor’s degree in computer science. He is now focused on technology education, outreach, and policy.
Director of AI at Mastercard. He is leading worldwide teams to build Data Engineering and AI shared services and capabilities for the company. Jack has more than 10 years experiences at Big Data and AI, across telcom and finance industries, ]Jack is a Big Data & AI technical evangelist and active speaker