AI Adventures

Channel: Google Cloud Tech

Videos (62)

1 — What is Machine Learning?
2 — AI Adventures: art, science, and tools of machine learning (Google I/O '18)
3 — Introduction to Tensorflow Cloud
4 — What is AutoML Translation?
5 — How to build an ML pipeline with TFX
6 — How to automatically transcribe your video or audio into text
7 — Cloud AI Data labeling service
8 — Build sound classification model with no code
9 — Manage a production ML pipeline with TFX
10 — What is Federated Learning?
11 — AI Platform Optimizer
12 — What is the Translation API?
13 — Persistent Disk for productive data science
14 — Using the What-If Tool for explainability
15 — Introduction to JAX
16 — Setting up AI Platform Pipelines
17 — Intro to Explanations for AI Platform
18 — Tensor Processing Units: History and hardware
19 — AI Platform Training with built-in algorithms
20 — Diving into the TPU v2 and v3
21 — How to Upgrade Colab with More Compute
22 — Using AutoML Natural Language for custom text classification
23 — Getting started with Natural Language Processing: Bag of words
24 — Kubeflow: Machine Learning on Kubernetes
25 — PyTorch on GCP
26 — Understanding image models and predictions using an Activation Atlas
27 — AutoML Tables
28 — TensorFlow Privacy
29 — BigQuery ML: Machine Learning with Standard SQL
30 — Jupyter on the web with Colab
31 — Visualizing Convolutional Neural Networks using Lucid
32 — Training models with custom containers on Cloud AI Platform
33 — TensorFlow Eager Mode
34 — Visualize your Data with Facets
35 — Scaling up Keras with Estimators
36 — Learning Scikit-Learn
37 — AutoML Vision - Part 1
38 — Using TensorFlow Hub for more productive machine learning
39 — Deep Learning VM Images
40 — How to Import a Keras model into TensorFlow.js
41 — Serving Scikit-learn Models at Scale
42 — Getting Started with Keras
43 — Getting Started with TensorFlow.js
44 — AutoML Vision - Part 2
45 — How to Make a Data Science Project with Kaggle
46 — TensorFlow Object Detection on iOS
47 — BigQuery and Open Datasets
48 — Introduction to Kaggle Kernels
49 — Quick Draw: the biggest doodle dataset
50 — Print Statements in TensorFlow
51 — Which Python Package Manager Should You Use?
52 — Distributed Training in the Cloud
53 — Jupyter Tips and Tricks
54 — Wrangling Data with Pandas
55 — Serverless Predictions at Scale
56 — Natural Language Generation at Google Research
57 — Machine Learning Meets Fashion
58 — Visualizing Your Model Using TensorBoard
59 — Estimators Revisited: Deep Neural Networks
60 — Plain and Simple Estimators
61 — Big Data for Training Models in the Cloud
62 — The 7 steps of machine learning