00 - 00 - Agenda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agenda/Curriculum\n",
    "\n",
    "    \n",
    "| just some  | really good  | books!!! |\n",
    "|:-:|:-:|:-:|\n",
    "| 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| 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| 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 |\n",
    "\n",
    "### 00) Introductions:\n",
    "- 🙋‍♂️ About myself \n",
    "- About this course/workshop - quick demo & tools overview\n",
    "    - 🎨 Whiteboard drawings\n",
    "    - 📝 Jupyter Notebooks\n",
    "    - 👨🏻‍💻 Terminal commands (pip, jupyter -> !cmd, pyenv & conda)\n",
    "    - 💻 GitHub repos (for class, TFJS -> 🎥 [**pose demo**](https://storage.googleapis.com/tfjs-models/demos/posenet/camera.html) 🕺, books repos, TF/Keras demos)\n",
    "    - 🕸 Websites (TF, TF-hub)\n",
    "    - 📚 Books:\n",
    "        - \"Deep Learning with Python\" by [François Chollet](https://github.com/fchollet/deep-learning-with-python-notebooks)\n",
    "        - \"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems\" by [Aurélien Géron](https://github.com/ageron/handson-ml2)\n",
    "        - \"Hands-On Neural Networks with TensorFlow 2.0\" by [Paolo Galeone](https://github.com/PacktPublishing/Hands-On-Neural-Networks-with-TensorFlow-2.0)\n",
    "- (plot) What is the difference between Statistics / Machine Learning / Deep Learning / Artificial Intelligence?\n",
    "@matvelloso. Shoes size example. Information reduction.\n",
    "- (plot) Compute + Algorithm + IO\n",
    "- (plot) Why now, AI? Chronological retrospective.\n",
    "- (plot) Hardware advances: SIMD, Tensor Cores, TPU, FPGA, Quantum Computing\n",
    "- (plot) HW, compilers, TensorFlow and Keras -> computational graph, memory allocation\n",
    "\n",
    "\n",
    "### 0) Don't be scared of Linear Regressions - it does not \"byte\"!.. Basic Terminology:\n",
    "- Linear regression Notebook\n",
    "- (plot) 🐵🧠 What is neuron? What is activation function?\n",
    "\n",
    "### 1)  👀 Computer Vision:\n",
    "\n",
    "- ✍🏻 Handwritten digits (MNIST) recognized with fully connected neural network\n",
    "- 📸 (plot) One-hot encoding\n",
    "- 👁 Information theory and representation: MNIST Principal Component Analysis\n",
    "- 🙈 (plot) Fully connected vs. convolutional neural network\n",
    "- 📷 (plot + Notebook) Convolutions, pooling, dropouts\n",
    "- 🛒 (plot) Transfer learning and different topologies\n",
    "- 🎨 Style transfer\n",
    "- 🧐 (Convolutional) Neural Network attention - ML explainability\n",
    "\n",
    "### 2) Text Analytics - Natural Language Processing (NLP):\n",
    "- 🤬 Toxicity demo\n",
    "- 📝 (plot) How to represent text as numbers? Text vectorization: one-hot encoding, tokenization, word embeddings\n",
    "- 🙊 IMDB movies review dataset prediction with hot-encoding in Keras\n",
    "- 🤯 Word embeddings and [Embedding Projector](http://projector.tensorflow.org/)\n",
    "- 🗒 Embedding vs hot-encoding and Fully Connected Neural Network for IMDB\n",
    "- 📒 Can LSTM guess the author?\n",
    "\n",
    "### 3) Can Robot juggle? Reinforcement Learning:\n",
    "- (plot) 🎭 Actors and environment\n",
    "\n",
    "### 4) Operationalization, aka \"10 ways to put your slapdash code into production...\"\n",
    "- (plot) Data - Training - Deployment aka MLOps or CI/CD for Data Scientists\n",
    "\n",
    "### 5) Summary\n",
    "- Quick recap what we learned so far"
   ]
  },
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