fix typo in IntroKeras.ipynb

lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb

@@ -1,6 +1,7 @@
 {
   "cells": [
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "En2vX4FuwHlu"
@@ -16,6 +17,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "8cACQoFMwHl3"
@@ -65,6 +67,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "6tp2xGV7wHl4"
@@ -83,6 +86,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "A10prCPowHl7"
@@ -185,6 +189,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
@@ -206,6 +211,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "SjPlpf2-wHl8"
@@ -251,6 +257,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
@@ -292,6 +299,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
@@ -313,6 +321,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
@@ -320,7 +329,7 @@
         "* `x` and `y` specify training data, features and labels respectively\n",
         "* If we want validation to be performed on each epoch, we can specify `validation_data` parameter, which would be a tuple of features and labels\n",
         "* `epochs` specified the number of epochs\n",
-        "* If we want training to happen in minibatches, we can speficu `batch_size` parameter. You can also pre-batch the data manually before passing it to `x`/`y`/`validation_data`, in which case you do not need `batch_size`"
+        "* If we want training to happen in minibatches, we can specify `batch_size` parameter. You can also pre-batch the data manually before passing it to `x`/`y`/`validation_data`, in which case you do not need `batch_size`"
       ]
     },
     {
@@ -370,6 +379,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "s4_Atvn5K4K9"
@@ -423,6 +433,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "dvAiaj_JndyP"
@@ -508,6 +519,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
@@ -578,6 +590,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
@@ -634,6 +647,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {},
       "source": [
@@ -647,6 +661,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "BmHNhUU8bqEX"
@@ -669,6 +684,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "gZ-kWx84bMDH"
@@ -683,6 +699,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "yX6hqiafwHl9"