Clean notebooks

01. Data.ipynb

@@ -27,7 +27,7 @@
    "metadata": {},
    "source": [
     "# 🗃️ Get some data\n",
-    "In Julia, it's pretty easy to dowload a file from the web using the `download` (https://docs.julialang.org/en/v1/stdlib/Downloads/) function. But also, you can use your favorite command line commad to download files by easily switching from Julia via the `;` key. Let's try both.\n",
+    "In Julia, it's pretty easy to download a file from the web using the `download` (https://docs.julialang.org/en/v1/stdlib/Downloads/) function. But also, you can use your favorite command line command to download files by easily switching from Julia via the `;` key. Let's try both.\n",
     "\n",
     "Note: `download` depends on external tools such as curl, wget or fetch. So you must have one of these."
    ]
@@ -1752,7 +1752,7 @@
    "source": [
     "# Finally...\n",
     "After finishing this notebook, you should be able to:\n",
-    "- [ ] dowload a data file from the web given a url\n",
+    "- [ ] download a data file from the web given a url\n",
     "- [ ] load data from a file from a text file via DelimitedFiles or CSV\n",
     "- [ ] write your data to a text file or csv file\n",
     "- [ ] load data from file types xlsx, jld, npz, mat, rda\n",

02. Linear Algebra.ipynb

@@ -2278,7 +2278,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will remove the first image from the dictionary. The goal is to find the solution of the linear system `Ax=b` where `A` is the dictionary of all images. In face recognition problem we really want to minimize the norm differece `norm(Ax-b)` but the `\\` actually solves a least squares problem when the matrix at hand is not invertible."
+    "We will remove the first image from the dictionary. The goal is to find the solution of the linear system `Ax=b` where `A` is the dictionary of all images. In face recognition problem we really want to minimize the norm difference `norm(Ax-b)` but the `\\` actually solves a least squares problem when the matrix at hand is not invertible."
    ]
   },
   {
@@ -2497,7 +2497,7 @@
     "# Finally...\n",
     "After finishing this notebook, you should be able to:\n",
     "- [ ] reshape and vectorize a matrix\n",
-    "- [ ] apply basic linear algebra operations such as transpose, matrix-matrix product, and solve a linear systerm\n",
+    "- [ ] apply basic linear algebra operations such as transpose, matrix-matrix product, and solve a linear system\n",
     "- [ ] call a linear algebra factorization on your matrix\n",
     "- [ ] use SVD to created a compressed version of an image\n",
     "- [ ] solve the face recognition problem via a least square approach\n",

06. Classification.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "## Classification\n",
-    "Put simply, classification is the task of predicting a label for a given observation. For example: you are given certain physical descriptions of an animal, and your taks is to classify them as either a dog or a cat. Here, we will classify iris flowers.\n",
+    "Put simply, classification is the task of predicting a label for a given observation. For example: you are given certain physical descriptions of an animal, and your task is to classify them as either a dog or a cat. Here, we will classify iris flowers.\n",
     "\n",
     "As we will see later, we will use different classifiers and at the end of this notebook, we will compare them. We will define our accuracy function right now to get it out of the way. We will use a simple accuracy function that returns the ratio of the number of correctly classified observations to the total number of predictions."
    ]

10. Neural Nets.ipynb

@@ -109,7 +109,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, we will transofrm the gray scale values to Float32 types. Here, using Float32 will speedup the neural network substantially withough compromising the quality of the solution."
+    "First, we will transform the gray scale values to Float32 types. Here, using Float32 will speedup the neural network substantially without compromising the quality of the solution."
    ]
   },
   {
@@ -799,7 +799,7 @@
     "- [ ] create a neural network with Flux.jl\n",
     "- [ ] creating an accuracy function and loss function to be passed to train the neural network\n",
     "- [ ] train the neural network\n",
-    "- [ ] describe a few tips that can help make your nerual network faster or more accurate (such as using Float32 as opposed to Float32)"
+    "- [ ] describe a few tips that can help make your neural network faster or more accurate (such as using Float32 as opposed to Float32)"
    ]
   },
   {

11. from other languages.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "## Using other languages\n",
-    "Often, I hear that the biggest challenge of moving from another language to Julia is giving up all the codes you have written in other languages or your favorite packages from other languages. **This notebook is not about data science, but it's about your next data science project** (if you're working on a data science project in Julia and you want to use functionality from other langages). Here, we will specifically cover Python, R, and C."
+    "Often, I hear that the biggest challenge of moving from another language to Julia is giving up all the codes you have written in other languages or your favorite packages from other languages. **This notebook is not about data science, but it's about your next data science project** (if you're working on a data science project in Julia and you want to use functionality from other languages). Here, we will specifically cover Python, R, and C."
    ]
   },
   {