chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"# Conclusions"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## Key concepts in review"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Various approaches to AI"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### What makes deep learning special within the field of machine learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### How to think about deep learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Key enabling technologies"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### The universal machine-learning workflow"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Key network architectures"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Densely connected networks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow import keras\n",
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"from tensorflow.keras\u00a0import\u00a0layers\n",
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"inputs = keras.Input(shape=(num_input_features,))\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(inputs)\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(x)\n",
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"outputs = layers.Dense(1,\u00a0activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\u00a0loss=\"binary_crossentropy\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(num_input_features,))\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(inputs)\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(x)\n",
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"outputs = layers.Dense(num_classes,\u00a0activation=\"softmax\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\u00a0loss=\"categorical_crossentropy\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(num_input_features,))\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(inputs)\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(x)\n",
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"outputs = layers.Dense(num_classes,\u00a0activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\u00a0loss=\"binary_crossentropy\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(num_input_features,))\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(inputs)\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(x)\n",
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"outputs layers.Dense(num_values)(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\u00a0loss=\"mse\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Convnets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(height,\u00a0width,\u00a0channels))\n",
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"x = layers.SeparableConv2D(32,\u00a03,\u00a0activation=\"relu\")(inputs)\n",
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"x = layers.SeparableConv2D(64,\u00a03,\u00a0activation=\"relu\")(x)\n",
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"x = layers.MaxPooling2D(2)(x)\n",
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"x = layers.SeparableConv2D(64,\u00a03,\u00a0activation=\"relu\")(x)\n",
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"x = layers.SeparableConv2D(128,\u00a03,\u00a0activation=\"relu\")(x)\n",
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"x = layers.MaxPooling2D(2)(x)\n",
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"x = layers.SeparableConv2D(64,\u00a03,\u00a0activation=\"relu\")(x)\n",
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"x = layers.SeparableConv2D(128,\u00a03,\u00a0activation=\"relu\")(x)\n",
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"x = layers.GlobalAveragePooling2D()(x)\n",
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"x = layers.Dense(32,\u00a0activation=\"relu\")(x)\n",
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"outputs = layers.Dense(num_classes,\u00a0activation=\"softmax\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\u00a0loss=\"categorical_crossentropy\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### RNNs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(num_timesteps,\u00a0num_features))\n",
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"x = layers.LSTM(32)(inputs)\n",
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"outputs = layers.Dense(num_classes,\u00a0activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\u00a0loss=\"binary_crossentropy\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(num_timesteps,\u00a0num_features))\n",
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"x = layers.LSTM(32,\u00a0return_sequences=True)(inputs)\n",
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"x = layers.LSTM(32,\u00a0return_sequences=True)(x)\n",
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"x = layers.LSTM(32)(x)\n",
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"outputs = layers.Dense(num_classes,\u00a0activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\",\u00a0loss=\"binary_crossentropy\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Transformers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"encoder_inputs = keras.Input(shape=(sequence_length,), dtype=\"int64\")\n",
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"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(encoder_inputs)\n",
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"encoder_outputs = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\n",
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"decoder_inputs = keras.Input(shape=(None,), dtype=\"int64\")\n",
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"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(decoder_inputs)\n",
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"x = TransformerDecoder(embed_dim, dense_dim, num_heads)(x, encoder_outputs)\n",
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"decoder_outputs = layers.Dense(vocab_size, activation=\"softmax\")(x)\n",
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"transformer = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
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"transformer.compile(optimizer=\"rmsprop\", loss=\"categorical_crossentropy\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"inputs = keras.Input(shape=(sequence_length,), dtype=\"int64\")\n",
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"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(inputs)\n",
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"x = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\n",
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"x = layers.GlobalMaxPooling1D()(x)\n",
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"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
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"model = keras.Model(inputs, outputs)\n",
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"model.compile(optimizer=\"rmsprop\", loss=\"binary_crossentropy\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### The space of possibilities"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## The limitations of deep learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### The risk of anthropomorphizing machine-learning models"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Automatons vs. intelligent agents"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Local generalization vs. extreme generalization"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### The purpose of intelligence"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Climbing the spectrum of generalization"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## Setting the course toward greater generality in AI"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### On the importance of setting the right objective: The shortcut rule"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### A new target"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## Implementing intelligence: The missing ingredients"
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]
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||||
},
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||||
{
|
||||
"cell_type": "markdown",
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||||
"metadata": {
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"colab_type": "text"
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},
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"source": [
|
||||
"### Intelligence as sensitivity to abstract analogies"
|
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]
|
||||
},
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||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### The two poles of abstraction"
|
||||
]
|
||||
},
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||||
{
|
||||
"cell_type": "markdown",
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||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
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"source": [
|
||||
"#### Value-centric analogy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"#### Program-centric analogy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"#### Cognition as a combination of both kinds of abstraction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### The missing half of the picture"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"## The future of deep learning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### Models as programs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### Blending together deep learning and program synthesis"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"#### Integrating deep-learning modules and algorithmic modules into hybrid systems"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"#### Using deep learning to guide program search"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### Lifelong learning and modular subroutine reuse"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### The long-term vision"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"## Staying up to date in a fast-moving field"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### Practice on real-world problems using Kaggle"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### Read about the latest developments on arXiv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"### Explore the Keras ecosystem"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"## Final words"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"collapsed_sections": [],
|
||||
"name": "chapter14_conclusions.i",
|
||||
"private_outputs": false,
|
||||
"provenance": [],
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Reference in New Issue
Block a user