In this repository, I recorded everything that experienced when I participated in the Google Developers Machine Learning Bootcamp 2021 Korea. From 21.08.12.Thu
Google-ML-Bootcamp-2021 is program that provides machine learning education to developer so that they can grow as machine learning engineer
Google-ML-Bootcamp-2021 consistents of four parts
| Part | Goal | Start | End | etc |
|---|---|---|---|---|
| Part1. Coursera Deep Learning Specialization Courses | Deep Learning Specialization Certification |
21.08.08.Sun | # | # |
| Part2. Machine Learning Certifications | TensorFlow certification |
# | # | # |
| Part3. Machine Learning Project | Kaggle Tabular playground, competition |
# | # | # |
| Part4. Machine Learning Networking | Tech Talk, Career, Growth, Mentoring |
# | # | # |
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. -by Coursera-
- Course by Coursera - Deep Learning Specialization Course link
- Lecturer - Andrew Ng
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
- familiar with the significant technological trends driving the rise of deep learning
- build, train, and apply fully connected deep neural networks
- Implement efficient (vectorized) neural network
- identify key parameters in a neural network's architecture
- apply deep learning to your own applications
- Leaned basics of NN(Neural Networks)
- Numpy Programming(Basic) code
- Programming Assignment: Logistic_Regression_with_a_Neural_Network_mindset code
- Programming Assignment: Building_your_Deep_Neural_Network_Step_by_Step.ipynb code
- Programming Assignment: Deep Neural Network - Application code
- Programming Assignment: Planar Data Classification with One Hidden Layer code
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
- learn best practices to train and develop test sets
- analyze bias/variance for building deep learning applications
- be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization,
- gradient checking
- implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence;
- implement a neural network in TensorFlow.
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
- diagnose errors in a machine learning system
- prioritize strategies for reducing errors
- understand complex ML settings, such as mismatched training/test sets,
- comparing to and/or surpassing human-level performance
- apply end-to-end learning, transfer learning, and multi-task learning
This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.
In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.
- build a convolutional neural network, including recent variations such as residual networks
- apply convolutional networks to visual detection and recognition tasks
- use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.
In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
- build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs
- apply RNNs to Character-level Language Modeling
- gain experience with natural language processing and Word Embeddings
- use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.