umz21/wyk/2012_RNN.ipynb

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   "source": [
    "## Uczenie maszynowe UMZ 2019/2020\n",
    "### 2 czerwca 2020\n",
    "# 12. Rekurencyjne sieci neuronowe"
   ]
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   "source": [
    "## RNN – _Recurrent Neural Network_\n",
    "\n",
    "## LSTM – _Long Short Term Memory_"
   ]
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   "source": [
    "### Rekurencyjna sieć neuronowa – schemat\n",
    "\n",
    "<img style=\"margin: auto\" width=\"20%\" src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-rolled.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   },
   "source": [
    "### Rekurencyjna sieć neuronowa – schemat\n",
    "\n",
    "<img style=\"margin: auto\" width=\"80%\" src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Zależności długodystansowe (_long-distance dependencies_) w sieciach rekurencyjnych\n",
    "\n",
    "<img style=\"margin: auto\" width=\"60%\" src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-longtermdependencies.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### RNN – typy sekwencji\n",
    "\n",
    "<img style=\"margin: auto\" width=\"80%\" src=\"http://karpathy.github.io/assets/rnn/diags.jpeg\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Prosta sieć RNN – schemat\n",
    "\n",
    "<img src=\"rnn.png\" style=\"margin: auto;\" width=\"100%\" />"
   ]
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   "source": [
    "### LSTM – schemat\n",
    "\n",
    "<img src=\"lstm.jpg\" style=\"margin: auto;\" width=\"80%\" />\n",
    "\n",
    "* Rekurencyjne sieci neuronowe znajduja zastosowanie w przetwarzaniu sekwencji, np. szeregów czasowych i tekstów.\n",
    "* LSTM są rozwinięciem RNN, umożliwiają „zapamiętywanie” i „zapominanie”."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "### Co potrafią generować rekurencyjne sieci neuronowe?\n",
    "\n",
    "http://karpathy.github.io/2015/05/21/rnn-effectiveness/"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### Generowanie tekstu za pomocą LSTM – przykład\n",
    "\n",
    "https://github.com/keras-team/keras/blob/master/examples/lstm_text_generation.py"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### Przewidywanie ciągów czasowych za pomocą LSTM – przykład\n",
    "\n",
    "https://machinelearningmastery.com/time-series-forecasting-long-short-term-memory-network-python/"
   ]
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  {
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   "metadata": {
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   },
   "source": [
    "## GRU – _Gated Recurrent Unit_\n",
    "\n",
    "* Rodzaj rekurencyjnej sieci neuronowej wprwadzony w 2014 roku\n",
    "* Ma prostszą budowę niż LSTM (2 bramki zamiast 3).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### GRU – schemat\n",
    "\n",
    "<img src=\"gru.png\" style=\"margin: auto;\" width=\"50%\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### GRU vs LSTM\n",
    "* LSTM – 3 bramki: wejścia (_input_), wyjścia (_output_) i zapomnienia (_forget_); GRU – 2 bramki: resetu (_reset_) i aktualizacji (_update_). Bramka resetu pełni podwójną funkcję: zastępuje bramki wyjścia i zapomnienia.\n",
    "* GRU i LSTM mają podobną skuteczność, ale GRU dzięki prostszej budowie bywa bardziej wydajna.\n",
    "* LSTM sprawdza się lepiej w przetwarzaniu tekstu, ponieważ lepiej zapamiętuje zależności długosystansowe."
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