Toggle navigation sidebar
Toggle in-page Table of Contents
Eduardo Avelar
E-learning ML/Data resources
ML/Data/Dev Books
Coursera courses
GCP Courses/Videos Analysis
GCP Skills Boost Updates
GCP ML/Data labs
Pages/Posts/Lectures
Platforms/Tools
Courses/Videos
Dev-ML Tools
Check for duplicated questions
Create html page with ML questions
VS Code ext to paste one line
Qwiklabs Quizzes to JSON
GCP Courses Builder
GCP Videos to Images
My Dev notes
apt install dev-essentials
Dev commands
Dev Troubleshooting Notes
Install Kubeflow Pipelines for Local Deployment
Kubeflow Pipelines v1 sdk v2 examples
My VS Code theme
ML Notes
ML Good practice
Nvidia ML
Google Cloud Skills Boost Courses
Google Cloud Big Data and Machine Learning Fundamentals
How Google Does Machine Learning
Launching into Machine Learning
TensorFlow on Google Cloud
Feature Engineering
Machine Learning in the Enterprise
Vertex Vizier Hyperparameter Tuning
Introduction
Vertex AI Vizier hyperparameter tuning
Lab intro: Vertex Vizier Hyperparameter Tuning
Prediction and Model Monitoring using Vertex AI
Introduction
Predictions using Vertex AI
Model management using Vertex AI
Lab intro: Vertex AI Model Monitoring
Vertex AI Pipelines
Introduction
Prediction using Vertex AI pipelines
Lab intro: Vertex AI Pipelines
Best Practices for Machine Learning Development
Introduction
Best practices for model deployment and serving
Best practices for model monitoring
Vertex AI pipeline best practices
Best practices for artifact organization
Series Summary
Series summary
Working dir
intro_to_vertex_pipelines.ipynb
Production Machine Learning Systems
Introduction to Advanced Machine Learning on Google Cloud
Advanced Machine Learning on Google Cloud
Welcome
Architecting Production ML Systems
Architecting ML systems
Data extraction, analysis, and preparation
Model training, evaluation, and validation
Trained model, prediction service, and performance monitoring
Training design decisions
Serving design decisions
Using Vertex AI
Lab introduction: Structured data prediction
Designing Adaptable ML Systems
Introduction
Adapting to data
Changing distributions
Lab: Adapting to data
Right and wrong decisions
System failure
Concept drift
Actions to mitigate concept drift
TensorFlow data validation
Components of TensorFlow data validation
Lab Introduction: Introduction to TensorFlow Data Validation
Lab Introduction: Advanced Visualizations with TensorFlow Data Validation
Mitigating training-serving skew through design
Lab Introduction: Serving ML Predictions in Batch and Real Time
Diagnosing a production model
Designing High-performance ML Systems
Training
Why distributed training is needed
Distributed training architectures
TensorFlow distributed training strategies
Mirrored strategy
Multi-worker mirrored strategy
TPU strategy
Parameter server strategy
Lab Introduction: Distributed Training with Keras
Lab Introduction: Distributed Training using GPUs on Cloud AI Platform
Training on large datasets with tf.data API
Lab Introduction: TPU-speed Data Pipelines
Building Hybrid ML Systems
Kubeflow
Optimizing TensorFlow for mobile
Summary
Course summary
Working dir
keras.ipynb
serving_ml_prediction.ipynb
tfdv_basic_spending.ipynb
babyweight/train_deploy.ipynb
Computer Vision Fundamentals with Google Cloud
Introduction to Computer Vision and Pre-built ML Models for Image Classification
What Is Computer Vision
Different Type of Computer Vision Problems
Computer Vision Use Cases
Vision API - Pre-built ML Models
Vertex AI and AutoML Vision on Vertex AI
What is Vertex AI and why does a unified platform matter?
Introduction to AutoML Vision on Vertex AI
How does Vertex AI help with the ML workflow, part 1 ?
How does Vertex AI help with the ML workflow, part 2 ?
Which vision product is right for you?
Lab Introduction - Identifying Damaged Car Parts with Vertex AI for AutoML Vision users
Custom Training with Linear, Neural Network and Deep Neural Network models
Introduction
Introduction to Linear Models
Reading the Data
Implementing Linear Models for Image Classification
Neural Networks and Deep Neural Networks for Image Classification
Deep Neural Networks with Dropout and Batch Normalization
Convolutional Neural Networks
Introduction
Convolutional Neural Networks
Understanding Convolutions
CNN Model Parameters
Working with Pooling Layers
Implementing CNNs on Vertex AI with pre-built TF container using Vertex Workbench
Dealing with Image Data
Introduction
Preprocessing the Image Data
Model Parameters and the Data Scarcity Problem
Data Augmentation
Transfer Learning
Summary
Summary
Natural Language Processing on Google Cloud
Course introduction
Course Introduction
NLP on Google Cloud
Introduction
What is NLP?
NLP history
NLP architecture
NLP APIs
NLP solutions
Summary
NLP with Vertex AI
Introduction
NLP options
Vertex AI
NLP with AutoML
NLP with custom training
NLP end-to-end workflow
Summary
Text representation
Introduction
Tokenization
One-hot encoding and bag-of-words
Word embeddings
Word2vec
Transfer learning and reusable embeddings
Summary
Advanced NLP models
Introduction
Encoder-decoder architecture
Attention mechanism
Transformer
BERT
Large language models
Lab introduction: Text Translation using Encoder-decoder Architecture
Summary
Recommendation Systems on Google Cloud
Recommendation Systems Overview
Types of Recommendation Systems
Content-Based or Collaborative
Recommendation System Pitfalls
Content-Based Recommendation Systems
Content-Based Recommendation Systems
Similarity Measures
Building a User Vector
Making Recommendations Using a User Vector
Making Recommendations for Many Users
Using Neural Networks for Content-Based Recommendation Systems
Machine Learning Operations (MLOps): Getting Started
Employing Machine Learning Operations
Machine learning (ML) practitioners’ pain points
The concept of devOps in ML
ML lifecycle
Automating the ML process
Vertex AI and MLOps on Vertex AI
What is vertex ai and why does a unified platform matter?
Introduction to mlops on vertex ai
How does vertex ai help with the mlops workflow, part 2?
Official GCP codes
Create machine learning models in BigQuery ML
GCP notes/resources
GCP notes
Machine Learning Cert guide
Notebooks To Do
Vertex AI MLOps
Official TF codes
Structured Data
Time series forecasting
Distributed training
Multi-worker training with Keras
Tensorflow Notebooks
TF Libraries & extensions
tfdv_basic.ipynb
Preprocessing Layers and tools
Scikit Learn anf TF
TensorFlow Developer Certificate
Feature Engineering Tools
DeepLearning.AI Courses
TensorFlow Dev Cert - original version
C1W1 Assignment: Housing Prices
C1W2: Implementing Callbacks in TensorFlow using the MNIST Dataset
C1W3: Improve MNIST with Convolutions
C1W4: Handling Complex Images - Happy or Sad Dataset
C2W1: Using CNN’s with the Cats vs Dogs Dataset
C2W2: Tackle Overfitting with Data Augmentation
C2W3: Transfer Learning
C2W4: Multi-class Classification
C3W1: Explore the BBC News archive
C3W2: Diving deeper into the BBC News archive
C3W3: Exploring Overfitting in NLP
C3W4: Predicting the next word
C4W1: Working with time series
C4W2: Predicting time series
C4W3: Using RNNs to predict time series
C4W4: Using real world data
TensorFlow Dev Cert - updated
C1W1 Assignment: Housing Prices
C1W2: Implementing Callbacks in TensorFlow using the MNIST Dataset
C1W3: Improve MNIST with Convolutions
C1W4: Handling Complex Images - Happy or Sad Dataset
C2W1: Using CNN’s with the Cats vs Dogs Dataset
C2W2: Tackle Overfitting with Data Augmentation
C2W3: Transfer Learning
C2W4: Multi-class Classification
C3W1: Explore the BBC News archive
C3W2: Diving deeper into the BBC News archive
C3W3: Exploring Overfitting in NLP
C3W4: Predicting the next word
C4W1: Working with time series
C4W2: Predicting time series
C4W3: Using RNNs to predict time series
C4W4: Using real world data
Transformer
