1.
Quick Introduction
1.1.
Supervised Learning
1.2.
Unsupervised Learning
2.
Basic Linear Algebra needed
2.1.
Eigenvectors
2.2.
References
3.
ML Models
3.1.
Word2Vec
3.2.
GloVe
4.
Deep Learning
4.1.
Neural Network
4.2.
Gradient descent
4.3.
Back Propagation
4.3.1.
Calculus
4.4.
Activation Function
4.5.
Convolutional Neural Networks
4.6.
Recurrent Neural Networks
4.6.1.
MNIST
5.
Tensorflow
5.1.
TensorFlow.js
6.
PyTorch
7.
Transformers
7.1.
BERT
7.2.
GPT
7.3.
T5
7.4.
GitHub Copilot
7.5.
References
8.
Salesforce Einstein
8.1.
Machine Learning
8.2.
Natural Language Processing
8.3.
Computer Vision
9.
Google Cloud Platform
10.
Processing Units
10.1.
CPU
10.2.
GPU
10.3.
TPU
11.
ML Pipelines
11.1.
ML ops
11.2.
TensorFlow Serving
11.3.
TensorFlow Extended
11.3.1.
Apache Airflow
11.3.2.
Apache Beam
11.3.3.
Kubeflow
11.4.
AutoML
11.5.
Kubernetes
12.
Speedup
12.1.
JAX
12.2.
Closures and Decorators
12.3.
References
13.
OpenAI
13.1.
API
13.2.
Chat
13.3.
Summarize
13.4.
TLDR
13.5.
Translate
13.6.
Codex
14.
Inspirations
15.
Datasets
15.1.
Boston Housing
16.
Building ML for Industries
16.1.
Lost-Found Item Management
17.
Hardware
17.1.
Raspberry Pi
18.
Conversational-AI
18.1.
Chatbots
18.2.
Einstein Bots
18.3.
DeepPavlov
18.4.
Dialogflow
18.5.
Rasa
19.
Transformers
19.1.
GPT
19.2.
Building GPT
20.
Tools
20.1.
Infrastructure as code
Light (default)
Rust
Coal
Navy
Ayu
Machine Learning for Everyone!
12. Speedup
In this chapter we will see the ways to speed up Machine Learning