COURSE OBJECTIVES:
In this course 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.
The course takes project focused approach to teach you deep learning by building deep learning models with Tensorflow 2.x. 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 and working on projects. Students who take this course will be able to:
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 and regression
Build CNN for computer vision and object detection with Tensorflow
Applying neural networks for NLP using TensorFlow
Build solid knowledge and skills for Tensorflow developer certificate, Link
COURSE SCHEDULE:
- Session 1: Jan 12, 10am~12pm PST (US Pacific, GMT-8)
- Session 2: Jan 14, 10am~12pm PST
- Session 3: Jan 19, 10am~12pm PST
- Session 4: Jan 21, 10am~12pm PST
- Session 5: Jan 26, 10am~12pm PST
- Session 6: Jan 28, 10am~12pm PST
- Session 7: Feb 2, 10am~12pm PST
- Session 8: Feb 4, 10am~12pm PST
COURSE INCLUDE:
- 16 hours/ 8 sessions
- 10 lectures / 16 hands-on code labs
- Live Sessions with Zoom, Real time interaction
- Slack support in and after class
- Capstone project, Github portfolio
- One-year access to course materials
COURSE CONTENT:
Check the Syllabus tab for full course content.
WHO SHOULD LEARN:
Developers, data scientists, students who want to get started on building deep learning projects or applications.
PREREQUISITE:
Familiarity with Python, or willingness to learn it quickly
Basic familiarity with statistics and probability theory is a plus, but not required
Comfort with the command line a plus
DIFFICULTY LEVEL
Beginner~Intermediate
Est. time spend per week: 4 hours live class (required) + 4 hours homework (required) + 4 hours projects (bonus, optional).
FREE TRIAL
Full refund upon request before the first session ends (Jan 4th, 2021 12:00pm PST). 5% transaction fee is not refundable.
SESSION REPLAY
If miss the live sessions, you can watch recorded sessions any time, along with interactive learning tools, slides, course notes
BENEFITS
Certificate of completing course
Scholarship is available, contact us for applications
PREVIOUS COHORTS:
Cohort 6: Aug 24, 2020 ~ Sep 16, 2020
Cohort 5: Apr 28, 2019 ~ May 21, 2020
Cohort 4: Jan 7, 2019 ~ Jan 30, 2020
Cohort 3: Oct 15, 2019 ~ Nov 7, 2019
Cohort 2: Aug 20, 2019 ~ Aug 21, 2019
Cohort 1: May 14, 2019 ~ Jun 6, 2019
Module 1: Deep Learning & Neural Network Fundamentals
- Deep learning applications, key concepts and terminology
- Neural network architecture and theory
- Code lab 1: Building and training a neural network.
- Code lab 2: Exploring different network architectures
Module 2: Deep Neural Networks in Detail
- Identify tasks that are well and ill suited to deep learning
- Underfitting and overfitting, Regularization tactics
- Feature engineering, Optimizers and training parameters
- Code Lab 3: Exploring common activation functions
- Code lab 4: Exploring common loss functions
- Code lab 5: Exploring common optimizers
- Code lab 6: Underfitting, overfitting, 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 7: Clean up a messy dataset
- Code Lab 8: 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 9: Build a CNN from scratch.
- Code lab 10: Import and use well known pre-trained CNN architectures.
Module 5: Convolutional Neural Networks and Transfer Learning
- Transfer learning and identify examples of situations where it might help
- Import well known CNN architectures and leverage transfer learning using Keras
- Code Lab 11: Transfer learning with the Cifar100 dataset
- Code Lab 12: A complete CNN pipeline with transfer learning
Module 6: Object Localization and Image Segmentation
- Define object localization and image segmentation and what they’re used for
- Code lab 13: implement object localization
- Code lab 14: implement image segmentation
Module 7: RNN and NLP
- Define RNN and what they’re used for
- Build natural language processing systems using TensorFlow
- Use word embeddings, LSTM in you models
- Code lab 15: implement a text classification using Tensorflow
Module 8: ML Life Cycle and Tools For Production Systems
- Define the product life cycle for ML products.
- Save and restore models with Tensorflow after training.
- Use callbacks to save checkpoints during training, create more detailed logs, and other custom behavior
- Create custom metrics for more informative training output.
Tyler Bettilyon
Tyler is an educator, technologist, programmer, and all around curious human. He holds a bachelor’s degree in computer science and completed his MBA by counter example in San Francisco’s startup scene. Looking out from inside the Bay Area Bubble he realized that the world is not prepared for the future that technology is bringing. He is now focused on technology education, outreach, and policy
Jan 12, 10:00AM PST(18:00 GMT) | Tue,Thu