diff --git a/linear-classification/.gitignore b/linear-classification/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..1269488f7fb1f4b56a8c0e5eb48cecbfadfa9219
--- /dev/null
+++ b/linear-classification/.gitignore
@@ -0,0 +1 @@
+data
diff --git a/linear-classification/.ipynb_checkpoints/LinearClassification-checkpoint.ipynb b/linear-classification/.ipynb_checkpoints/LinearClassification-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2bf43933e72fb603f3320854ba3bc641a0d3acae
--- /dev/null
+++ b/linear-classification/.ipynb_checkpoints/LinearClassification-checkpoint.ipynb
@@ -0,0 +1,863 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linear classification is about performing image classification using a function that maps the main features of a data set into a linear function. An example is shown in the image below.\n",
+    "\n",
+    "The performance of this classification method is poor, but it helps to understand some of the main concepts. A good image classification algorithm uses a non-linear classification model."
+   ]
+  },
+  {
+   "attachments": {
+    "example-1.jpeg": {
+     "image/jpeg": 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+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![example-1.jpeg](attachment:example-1.jpeg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ensures that the random numbers will always be the same:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load dataset batches:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "def unpickle(file):\n",
+    "    with open(file, 'rb') as fo:\n",
+    "        dict = pickle.load(fo, encoding='bytes')\n",
+    "    return dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function to reshape and plot an image from the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def reshape_plot_cifar_image(image, label):\n",
+    "    reshaped_image = np.transpose(np.reshape(image, (3, 32, 32)), (1, 2, 0))\n",
+    "    plt.imshow(reshaped_image)\n",
+    "    plt.title(label)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load a batch from the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batches_meta = unpickle('data/batches.meta')\n",
+    "images_per_batch = batches_meta[b'num_cases_per_batch']\n",
+    "label_names = batches_meta[b'label_names']\n",
+    "data_1 = unpickle('data/data_batch_1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load an image to test the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+       "z\n",
+       "\" id=\"DejaVuSans-101\"/>\n",
+       "    </defs>\n",
+       "    <g transform=\"translate(101.791875 16.318125)scale(0.12 -0.12)\">\n",
+       "     <use xlink:href=\"#DejaVuSans-97\"/>\n",
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+       "     <use x=\"383.642578\" xlink:href=\"#DejaVuSans-98\"/>\n",
+       "     <use x=\"447.119141\" xlink:href=\"#DejaVuSans-105\"/>\n",
+       "     <use x=\"474.902344\" xlink:href=\"#DejaVuSans-108\"/>\n",
+       "     <use x=\"502.685547\" xlink:href=\"#DejaVuSans-101\"/>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p4d9b8dde9a\">\n",
+       "   <rect height=\"217.44\" width=\"217.44\" x=\"26.925\" y=\"22.318125\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_index = 5\n",
+    "\n",
+    "test_image = data_1[b'data'][test_index]\n",
+    "test_label = label_names[data_1[b'labels'][test_index]]\n",
+    "\n",
+    "reshape_plot_cifar_image(test_image, test_label.decode('UTF-8'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train a linear model with only one image and random weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function to predict the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(W, x, b):\n",
+    "    return np.dot(W, x) + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Create initial random weight and bias:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "# The weights that the model will learn to best fit the linear classification.\n",
+    "weigths = np.random.rand(10, 3072)\n",
+    "# The probability that an image belongs to a class. Helps to generalize the model.\n",
+    "bias = np.random.rand(10)\n",
+    "image = data_1[b'data'][0]\n",
+    "scores = predict(weigths, image, bias)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "High Loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def high_loss():\n",
+    "    for d in data_1[b'data']:\n",
+    "        return d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate High Loss of all data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 59  43  50 ... 140  84  72]\n"
+     ]
+    }
+   ],
+   "source": [
+    "error = high_loss()\n",
+    "print(error)"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/linear-classification/.ipynb_checkpoints/cifar-10 Linear classifier-checkpoint.ipynb b/linear-classification/.ipynb_checkpoints/cifar-10 Linear classifier-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..635b852d85dbb4140370c6dc92abfee2aabdfbd3
--- /dev/null
+++ b/linear-classification/.ipynb_checkpoints/cifar-10 Linear classifier-checkpoint.ipynb	
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linear classifier, mostly stolen from https://mlxai.github.io/2017/01/06/vectorized-implementation-of-svm-loss-and-gradient-update.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the cifar-10 dataset\n",
+    "import numpy as np\n",
+    "import cPickle\n",
+    "\n",
+    "dataset= cPickle.load(open(\"cifar-10-batches-py\\\\data_batch_2\",'rb'))\n",
+    "X = np.array(dataset['data'].reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(\"uint8\"))\n",
+    "Y = np.array(dataset['labels'])\n",
+    "labels = cPickle.load(open(\"cifar-10-batches-py\\\\batches.meta\",'rb'))['label_names']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xcd6acc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Show a few random images....\n",
+    "from matplotlib import pyplot as plt\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=10,figsize=(12,3),subplot_kw={'xticks': [], 'yticks': []})\n",
+    "for ax in axes.flat:\n",
+    "    index=np.random.randint(10000)\n",
+    "    ax.imshow(X[index], interpolation='nearest')\n",
+    "    ax.set_title(labels[Y[index]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize(X):\n",
+    "    mean_image = np.mean(X, axis=0)\n",
+    "    nX=X.astype(\"float\")-mean_image\n",
+    "    return nX\n",
+    "\n",
+    "def svm_loss_vectorized(W,X,Y,reg,delta):\n",
+    "    \"\"\"\n",
+    "    Inputs have dimension D, there are C classes, and we operate on minibatches\n",
+    "    of N examples.\n",
+    "\n",
+    "    Inputs:\n",
+    "        - W: A numpy array of shape (D, C) containing weights and bias.\n",
+    "        - X:  numpy array of shape (N, D) containing a minibatch of data, already normalized and bias applied.\n",
+    "        - Y: A numpy array of shape (N,) containing training labels; y[i] = c means\n",
+    "            that X[i] has label c, where 0 <= c < C.\n",
+    "        - reg: (float) regularization strength\n",
+    "        - delta: (float) margin in svm\n",
+    "\n",
+    "    Returns a tuple of:\n",
+    "        - loss as single float\n",
+    "        - gradient with respect to weights W; an array of same shape as W\n",
+    "    \"\"\"\n",
+    "    loss = 0.0\n",
+    "    dW = np.zeros(W.shape) # initialize the gradient as zero\n",
+    "    num_train = X.shape[0]\n",
+    "    score=X.dot(W) #shape[10,n]\n",
+    "    yi_scores = score[np.arange(score.shape[0]),Y] #shape[n]\n",
+    "#    print \"X shape:\" + str(X.shape)\n",
+    "#    print \"score shape:\" + str(score.shape)\n",
+    "    margins = np.maximum(0, score - np.matrix(yi_scores).T + delta) #shape:[10,n]\n",
+    "    margins[np.arange(num_train),Y] = 0\n",
+    "#    print \"Size of margins:\" + str(margins.shape)\n",
+    "    loss = np.mean(np.sum(margins, axis=1))\n",
+    "#    print \"Data loss:\"+str(loss)\n",
+    "    loss += 0.5 * reg * np.sum(W * W)\n",
+    "#    print \"Total loss:\" +str(loss)\n",
+    "\n",
+    "    binary = margins\n",
+    "    binary[margins > 0] = 1\n",
+    "    row_sum = np.sum(binary, axis=1)\n",
+    "#    print \"Size of row_sum:\" + str(row_sum.shape)\n",
+    "    binary[ np.arange(num_train),Y] = -row_sum.T\n",
+    "#    print \"binary Shape:\" + str(binary.shape)\n",
+    "    dW = np.dot(X.T, binary)\n",
+    "#    print \"dW shape:\" + str(dW.shape)\n",
+    "\n",
+    "    # Average\n",
+    "    dW /= num_train\n",
+    "    # Regularize\n",
+    "#    dW += reg*W\n",
+    "    return loss, dW\n",
+    "\n",
+    "def show_W(W,labels):\n",
+    "    W=(W-np.amin(W))/(np.amax(W)-np.amin(W))*255\n",
+    "    fig, axes = plt.subplots(nrows=1, ncols=10,figsize=(12,3),subplot_kw={'xticks': [], 'yticks': []})\n",
+    "    for idx, ax  in enumerate(axes.flat):\n",
+    "        ax.imshow(W.T[idx][:-1].reshape(32, 32,3 ), interpolation='nearest')\n",
+    "        ax.set_title(labels[idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize hyperparameters...\n",
+    "W=np.random.uniform(low=-0.1, high=0.1,size=10*3073).reshape(3073,10)\n",
+    "delta=2 # margin in SVM loss\n",
+    "reg=1e-2 # hyperparameter for regularization loss (lambda) \n",
+    "training_set =100 # number of images the loss is calculated for before gradient descent applied\n",
+    "learning_rate=1e-5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration:99999 Loss:7.19996527709\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xef667b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xe9d9e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#train the classifier\n",
+    "losses=[]\n",
+    "for i in range(0,100000):\n",
+    "    idx=np.random.randint(10000,size=training_set)\n",
+    "    X_train=np.hstack([normalize(X[idx]).reshape(training_set,3072),np.ones((training_set,1))]) #shape:[n,3073]\n",
+    "    Y_train=Y[idx] #shape:[n]\n",
+    "    loss,dW=svm_loss_vectorized(W,X_train,Y_train,reg,delta)\n",
+    "    W += -1 * learning_rate * dW\n",
+    "    losses.append(loss)\n",
+    "print \"Iteration:\"+str(i)+\" Loss:\"+str(loss)\n",
+    "plt.plot(losses)\n",
+    "plt.show()\n",
+    "show_W(W,labels)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "58\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Test trained classifier\n",
+    "test_set=100\n",
+    "idx=np.random.randint(10000,size=test_set)\n",
+    "X_train=np.hstack([normalize(X[idx]).reshape(test_set,3072),np.ones((test_set,1))]) #shape:[n,3073]\n",
+    "Y_train=Y[idx] #shape:[n]\n",
+    "score=X_train.dot(W)\n",
+    "classified_image=np.argmax(score,axis=1)\n",
+    "result = np.sum(classified_image==Y_train)\n",
+    "print result\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/linear-classification/.vscode/.ropeproject/config.py b/linear-classification/.vscode/.ropeproject/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..dee2d1ae9a6be9cf0248130b6c6b9e2668052079
--- /dev/null
+++ b/linear-classification/.vscode/.ropeproject/config.py
@@ -0,0 +1,114 @@
+# The default ``config.py``
+# flake8: noqa
+
+
+def set_prefs(prefs):
+    """This function is called before opening the project"""
+
+    # Specify which files and folders to ignore in the project.
+    # Changes to ignored resources are not added to the history and
+    # VCSs.  Also they are not returned in `Project.get_files()`.
+    # Note that ``?`` and ``*`` match all characters but slashes.
+    # '*.pyc': matches 'test.pyc' and 'pkg/test.pyc'
+    # 'mod*.pyc': matches 'test/mod1.pyc' but not 'mod/1.pyc'
+    # '.svn': matches 'pkg/.svn' and all of its children
+    # 'build/*.o': matches 'build/lib.o' but not 'build/sub/lib.o'
+    # 'build//*.o': matches 'build/lib.o' and 'build/sub/lib.o'
+    prefs['ignored_resources'] = ['*.pyc', '*~', '.ropeproject',
+                                  '.hg', '.svn', '_svn', '.git', '.tox']
+
+    # Specifies which files should be considered python files.  It is
+    # useful when you have scripts inside your project.  Only files
+    # ending with ``.py`` are considered to be python files by
+    # default.
+    # prefs['python_files'] = ['*.py']
+
+    # Custom source folders:  By default rope searches the project
+    # for finding source folders (folders that should be searched
+    # for finding modules).  You can add paths to that list.  Note
+    # that rope guesses project source folders correctly most of the
+    # time; use this if you have any problems.
+    # The folders should be relative to project root and use '/' for
+    # separating folders regardless of the platform rope is running on.
+    # 'src/my_source_folder' for instance.
+    # prefs.add('source_folders', 'src')
+
+    # You can extend python path for looking up modules
+    # prefs.add('python_path', '~/python/')
+
+    # Should rope save object information or not.
+    prefs['save_objectdb'] = True
+    prefs['compress_objectdb'] = False
+
+    # If `True`, rope analyzes each module when it is being saved.
+    prefs['automatic_soa'] = True
+    # The depth of calls to follow in static object analysis
+    prefs['soa_followed_calls'] = 0
+
+    # If `False` when running modules or unit tests "dynamic object
+    # analysis" is turned off.  This makes them much faster.
+    prefs['perform_doa'] = True
+
+    # Rope can check the validity of its object DB when running.
+    prefs['validate_objectdb'] = True
+
+    # How many undos to hold?
+    prefs['max_history_items'] = 32
+
+    # Shows whether to save history across sessions.
+    prefs['save_history'] = True
+    prefs['compress_history'] = False
+
+    # Set the number spaces used for indenting.  According to
+    # :PEP:`8`, it is best to use 4 spaces.  Since most of rope's
+    # unit-tests use 4 spaces it is more reliable, too.
+    prefs['indent_size'] = 4
+
+    # Builtin and c-extension modules that are allowed to be imported
+    # and inspected by rope.
+    prefs['extension_modules'] = []
+
+    # Add all standard c-extensions to extension_modules list.
+    prefs['import_dynload_stdmods'] = True
+
+    # If `True` modules with syntax errors are considered to be empty.
+    # The default value is `False`; When `False` syntax errors raise
+    # `rope.base.exceptions.ModuleSyntaxError` exception.
+    prefs['ignore_syntax_errors'] = False
+
+    # If `True`, rope ignores unresolvable imports.  Otherwise, they
+    # appear in the importing namespace.
+    prefs['ignore_bad_imports'] = False
+
+    # If `True`, rope will insert new module imports as
+    # `from <package> import <module>` by default.
+    prefs['prefer_module_from_imports'] = False
+
+    # If `True`, rope will transform a comma list of imports into
+    # multiple separate import statements when organizing
+    # imports.
+    prefs['split_imports'] = False
+
+    # If `True`, rope will remove all top-level import statements and
+    # reinsert them at the top of the module when making changes.
+    prefs['pull_imports_to_top'] = True
+
+    # If `True`, rope will sort imports alphabetically by module name instead
+    # of alphabetically by import statement, with from imports after normal
+    # imports.
+    prefs['sort_imports_alphabetically'] = False
+
+    # Location of implementation of
+    # rope.base.oi.type_hinting.interfaces.ITypeHintingFactory In general
+    # case, you don't have to change this value, unless you're an rope expert.
+    # Change this value to inject you own implementations of interfaces
+    # listed in module rope.base.oi.type_hinting.providers.interfaces
+    # For example, you can add you own providers for Django Models, or disable
+    # the search type-hinting in a class hierarchy, etc.
+    prefs['type_hinting_factory'] = (
+        'rope.base.oi.type_hinting.factory.default_type_hinting_factory')
+
+
+def project_opened(project):
+    """This function is called after opening the project"""
+    # Do whatever you like here!
diff --git a/linear-classification/.vscode/.ropeproject/objectdb b/linear-classification/.vscode/.ropeproject/objectdb
new file mode 100644
index 0000000000000000000000000000000000000000..0a47446c0ad231c193bdd44ff327ba2ab28bf3d8
Binary files /dev/null and b/linear-classification/.vscode/.ropeproject/objectdb differ
diff --git a/linear-classification/.vscode/settings.json b/linear-classification/.vscode/settings.json
new file mode 100644
index 0000000000000000000000000000000000000000..63d9a00b68325e36708886b6a87d68be4c38606a
--- /dev/null
+++ b/linear-classification/.vscode/settings.json
@@ -0,0 +1,4 @@
+{
+    "python.pythonPath": "/home/marmot/miniconda3/envs/dnn/bin/python",
+    "python.formatting.provider": "black"
+}
\ No newline at end of file
diff --git a/linear-classification/LinearClassification.bkp.py b/linear-classification/LinearClassification.bkp.py
new file mode 100644
index 0000000000000000000000000000000000000000..68d9f4c83ea1fbfd747c285bb989447ab61ff4f2
--- /dev/null
+++ b/linear-classification/LinearClassification.bkp.py
@@ -0,0 +1,68 @@
+import numpy as np
+import pickle
+import matplotlib.pyplot as plt
+
+np.random.seed(1)
+
+
+def unpickle(file):
+    with open(file, "rb") as fo:
+        data = pickle.load(fo, encoding="bytes")
+    return data
+
+
+def predict(weights, x, bias):
+    return np.dot(weigths, x) + bias
+
+
+def reshape_plot_cifar_image(image, label):
+    reshaped_image = np.transpose(np.reshape(image, (3, 32, 32)), (1, 2, 0))
+    plt.imshow(reshaped_image)
+    plt.title(label)
+    plt.show()
+
+
+def hinge_loss(images, weights, bias):
+    sum_loss = 0
+    for x, y in zip(images[b"data"], images[b"labels"]):
+        scores = predict(weigths, x, bias)
+        margins = np.maximum(0, scores - scores[y] + 1)
+        margins[y] = 0
+        sum_loss += np.sum(margins)
+    return sum_loss / images_per_batch
+
+
+def softmax(x):
+    return np.exp(x) / np.sum(np.exp(x))
+
+
+def log_likelihood(x_train, y_train, weigths, b):
+    loss_sum = 0
+    for x, y in zip(x_train, y_train):
+        x_normalized = x / np.linalg.norm(x)
+        scores = predict(weigths, x_normalized, bias)
+        scores_softmax = softmax(scores)
+        loss_sum += -np.log(scores_softmax[y])
+    return loss_sum / images_per_batch
+
+
+batches_meta = unpickle("data/batches.meta")
+images_per_batch = batches_meta[b"num_cases_per_batch"]
+label_names = batches_meta[b"label_names"]
+data_1 = unpickle("data/data_batch_1")
+
+# test_index = 5
+# test_image = data_1[b"data"][test_index]
+# test_label = label_names[data_1[b"labels"][test_index]]
+
+# reshape_plot_cifar_image(test_image, test_label.decode("UTF-8"))
+
+# The weights that the model will learn to best fit the linear classification.
+weigths = np.random.rand(10, 3072)
+# The probability that an image belongs to a class. Helps to generalize the model.
+bias = np.random.rand(10)
+# total_loss = hinge_loss(data_1, weigths, bias)
+# print(total_loss)
+
+loss = log_likelihood(data_1[b"data"], data_1[b"labels"], weigths, bias)
+print(loss)
diff --git a/linear-classification/LinearClassification.ipynb b/linear-classification/LinearClassification.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2bf43933e72fb603f3320854ba3bc641a0d3acae
--- /dev/null
+++ b/linear-classification/LinearClassification.ipynb
@@ -0,0 +1,863 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linear classification is about performing image classification using a function that maps the main features of a data set into a linear function. An example is shown in the image below.\n",
+    "\n",
+    "The performance of this classification method is poor, but it helps to understand some of the main concepts. A good image classification algorithm uses a non-linear classification model."
+   ]
+  },
+  {
+   "attachments": {
+    "example-1.jpeg": {
+     "image/jpeg": 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+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![example-1.jpeg](attachment:example-1.jpeg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ensures that the random numbers will always be the same:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load dataset batches:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "def unpickle(file):\n",
+    "    with open(file, 'rb') as fo:\n",
+    "        dict = pickle.load(fo, encoding='bytes')\n",
+    "    return dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function to reshape and plot an image from the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def reshape_plot_cifar_image(image, label):\n",
+    "    reshaped_image = np.transpose(np.reshape(image, (3, 32, 32)), (1, 2, 0))\n",
+    "    plt.imshow(reshaped_image)\n",
+    "    plt.title(label)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load a batch from the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batches_meta = unpickle('data/batches.meta')\n",
+    "images_per_batch = batches_meta[b'num_cases_per_batch']\n",
+    "label_names = batches_meta[b'label_names']\n",
+    "data_1 = unpickle('data/data_batch_1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load an image to test the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+       "    </defs>\n",
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+       "     <use xlink:href=\"#DejaVuSans-97\"/>\n",
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+       "    </g>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p4d9b8dde9a\">\n",
+       "   <rect height=\"217.44\" width=\"217.44\" x=\"26.925\" y=\"22.318125\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_index = 5\n",
+    "\n",
+    "test_image = data_1[b'data'][test_index]\n",
+    "test_label = label_names[data_1[b'labels'][test_index]]\n",
+    "\n",
+    "reshape_plot_cifar_image(test_image, test_label.decode('UTF-8'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train a linear model with only one image and random weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function to predict the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(W, x, b):\n",
+    "    return np.dot(W, x) + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Create initial random weight and bias:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0)\n",
+    "# The weights that the model will learn to best fit the linear classification.\n",
+    "weigths = np.random.rand(10, 3072)\n",
+    "# The probability that an image belongs to a class. Helps to generalize the model.\n",
+    "bias = np.random.rand(10)\n",
+    "image = data_1[b'data'][0]\n",
+    "scores = predict(weigths, image, bias)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "High Loss:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def high_loss():\n",
+    "    for d in data_1[b'data']:\n",
+    "        return d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate High Loss of all data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 59  43  50 ... 140  84  72]\n"
+     ]
+    }
+   ],
+   "source": [
+    "error = high_loss()\n",
+    "print(error)"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/linear-classification/LinearClassification.py b/linear-classification/LinearClassification.py
new file mode 100644
index 0000000000000000000000000000000000000000..234e8f256c60f475602c220015051f157c2a930b
--- /dev/null
+++ b/linear-classification/LinearClassification.py
@@ -0,0 +1,68 @@
+import numpy as np
+import pickle
+import matplotlib.pyplot as plt
+
+np.random.seed(1)
+
+
+def unpickle(file):
+    with open(file, "rb") as fo:
+        dict = pickle.load(fo, encoding="bytes")
+    return dict
+
+
+def predict(weights, x, bias):
+    return np.dot(weigths, x) + bias
+
+
+def reshape_plot_cifar_image(image, label):
+    reshaped_image = np.transpose(np.reshape(image, (3, 32, 32)), (1, 2, 0))
+    plt.imshow(reshaped_image)
+    plt.title(label)
+    plt.show()
+
+
+def high_loss(images, weights, bias):
+    sum_loss = 0
+    for x, y in zip(images[b"data"], images[b"labels"]):
+        scores = predict(weigths, x, bias)
+        margins = np.maximum(0, scores - scores[y] + 1)
+        margins[y] = 0
+        sum_loss += np.sum(margins)
+    return sum_loss / images_per_batch
+
+
+def softmax(x):
+    return np.exp(x) / np.sum(np.exp(x))
+
+
+def loss_log_likelihood(x):
+    return -np.log(x)
+
+
+batches_meta = unpickle("data/batches.meta")
+images_per_batch = batches_meta[b"num_cases_per_batch"]
+label_names = batches_meta[b"label_names"]
+data_1 = unpickle("data/data_batch_1")
+
+# test_index = 5
+# test_image = data_1[b"data"][test_index]
+# test_label = label_names[data_1[b"labels"][test_index]]
+
+# reshape_plot_cifar_image(test_image, test_label.decode("UTF-8"))
+
+# The weights that the model will learn to best fit the linear classification.
+weigths = np.random.rand(10, 3072)
+# The probability that an image belongs to a class. Helps to generalize the model.
+bias = np.random.rand(10)
+# total_loss = high_loss(data_1, weigths, bias)
+# print(total_loss)
+
+loss_sum = 0
+for x, y in zip(data_1[b"data"], data_1[b"labels"]):
+    x_normalized = x / np.linalg.norm(x)
+    scores = predict(weigths, x_normalized, bias)
+    scores_softmax = softmax(scores)
+    loss_sum += loss_log_likelihood(scores_softmax[y])
+
+print(loss_sum / images_per_batch)
diff --git a/linear-classification/LinearClassificationHingeLoss.py b/linear-classification/LinearClassificationHingeLoss.py
new file mode 100644
index 0000000000000000000000000000000000000000..2314b6a9c630dbd36f5ef81cd59df947a1e5fa17
--- /dev/null
+++ b/linear-classification/LinearClassificationHingeLoss.py
@@ -0,0 +1,61 @@
+import numpy as np
+import pickle
+import matplotlib.pyplot as plt
+
+# np.random.seed(1)
+
+
+def unpickle(file):
+    with open(file, "rb") as fo:
+        data = pickle.load(fo, encoding="bytes")
+    return data
+
+
+def predict(weights, x, bias):
+    return np.dot(weights, x) + bias
+
+
+def show_image(image, label):
+    reshaped_image = np.transpose(np.reshape(image, (3, 32, 32)), (1, 2, 0))
+    plt.imshow(reshaped_image)
+    plt.title(label)
+    plt.show()
+
+
+def hinge_loss(images, weights, bias):
+    """SVM loss function."""
+    sum_loss = 0
+    for x, y in zip(images[b"data"], images[b"labels"]):
+        scores = predict(weights, x, bias)
+        margins = np.maximum(0, scores - scores[y] + 1)
+        margins[y] = 0
+        sum_loss += np.sum(margins)
+    return sum_loss / images_per_batch
+
+
+def gradient_descent(weights):
+    h = 0.0001
+    gradients = ((weigths + h) - weigths) / h
+    return gradients
+
+
+batches_meta = unpickle("data/batches.meta")
+images_per_batch = batches_meta[b"num_cases_per_batch"]
+label_names = batches_meta[b"label_names"]
+data_1 = unpickle("data/data_batch_1")
+learning_rate = 0.001
+
+# test_index = 5
+# test_image = data_1[b"data"][test_index]
+# test_label = label_names[data_1[b"labels"][test_index]]
+# show_image(test_image, test_label.decode("UTF-8"))
+
+# for x in range(10):
+# The weights that the model will learn to best fit the linear classification.
+weigths = np.random.rand(10, 3072)
+# The probability that an image belongs to a class. Helps to generalize the model.
+bias = np.random.rand(10)
+total_loss = hinge_loss(data_1, weigths, bias)
+gradients = gradient_descent(weigths)
+print(weigths)
+print(gradients)
diff --git a/linear-classification/README.md b/linear-classification/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6d7eec6e6181ba137e67a0e3f5575f63e9cf9c1f
--- /dev/null
+++ b/linear-classification/README.md
@@ -0,0 +1,3 @@
+# Tutorials
+
+- https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/linear_classification.html
\ No newline at end of file
diff --git a/linear-classification/get-cifar10-dataset.sh b/linear-classification/get-cifar10-dataset.sh
new file mode 100755
index 0000000000000000000000000000000000000000..4d17e5511f3da4a794fdd4e605529c25509d6da9
--- /dev/null
+++ b/linear-classification/get-cifar10-dataset.sh
@@ -0,0 +1,4 @@
+wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
+tar -xvzf cifar-10-python.tar.gz
+mv cifar-10-batches-py data
+rm cifar-10-python.tar.gz
\ No newline at end of file