From 58820202379e7e95b852032feb00da56c30d6a35 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Wed, 16 Aug 2017 21:56:02 +0530 Subject: [PATCH 01/71] Add files via upload --- mnist_knn.ipynb | 293 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 293 insertions(+) create mode 100644 mnist_knn.ipynb diff --git a/mnist_knn.ipynb b/mnist_knn.ipynb new file mode 100644 index 0000000..671c65a --- /dev/null +++ b/mnist_knn.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First lets import matplotlib(for ploting the images), MNIST dataset, and KNN" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as pt\n", + "from sklearn import datasets, metrics\n", + "from sklearn.neighbors import NearestNeighbors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading MNIST digit dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mnist= datasets.load_digits()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dataset = list(zip(mnist.images, mnist.target))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Making the dataset flat for feeding into the machine learning model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n_samples = len(mnist.images)\n", + "images = mnist.images.reshape((n_samples,-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now building the classifier. Here the number of neighbors is set to 1" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "classifier = NearestNeighbors(n_neighbors=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fitting the model with MNIST dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_jobs=1, n_neighbors=1, p=2, radius=1.0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.fit(images[:n_samples//2],mnist.target[:n_samples//2])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now making predictions with test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "distances, indices = classifier.kneighbors(images[n_samples//2:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets calculate the accuracy of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy = 99.9877641824 %\n" + ] + } + ], + "source": [ + "acc = metrics.accuracy_score(mnist.target[n_samples//2:],indices)\n", + "print(\"accuracy = \",100-acc,\"%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now print first eight actual images and predictions of test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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YCXwDkKRTgMXAw3Wcy3GmKsXrpEj6mmgF6rJP0sckbQSuBT4xSbYVqfc1PF/Sk5K+LWlB\nlfKJou73WNIi6vR7E3Fj7hbCp+gKwhLJ24Bvm9ngBJzLcZwmYmbXm9lRwKeAK/O2pwrfB44wsxOA\nhwiDvVbkQur0e/U44T7CWvM+4ABgHmFKYh4wGPMPjyHAHjPbRxiqH0v4NPCpCKfdKV4nRdLXRCsw\nVvtWE+7zTCaj2mhmO6N/AbgJeOsk2QZjew0vpE6/V48TXgMcTZgPPpQwFXEfsCjm3x9PeC8wAzgo\ntjsL2AUMAI/VY4zjTGHWAEdLWiypm+FrolUY1T5JR6eSZxHu+Uwm9dg4N5U8B3i6lewDkHQMMIt6\n/V6ddwWXA+uBF4BXCE9IvA7sBl4mvFk7CCPjoXjsIcyZrMrjTqYffkz2kbpONgKfiXn/CJwT42+L\n18Rewm5j61rMvq8A64C1wI+B41rwNbwm2vhEtPGYVrIvpq8CvlRvn75iznEcJ0d8xZzjOE6OuBN2\nHMfJEXfCjuM4OeJO2HEcJ0fcCTuO4+SIO2HHcZwccSfsOI6TI+6EHcdxcuT/ARO5vMLCamWQAAAA\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = dataset[n_samples//2:]\n", + "for index, (image, label) in enumerate(data[:8]):\n", + " fig = pt.figure()\n", + " a = fig.add_subplot(4,2,1)\n", + " imgplot = pt.imshow(image)\n", + " a.set_title(\"actual :\"+str(label))\n", + " pt.colorbar(ticks = [0.1,0.3,0.5,0.7],orientation = 'horizontal')\n", + " a = fig.add_subplot(4,2,2)\n", + " current = indices[index][0]\n", + " \n", + " imgplot = pt.imshow(dataset[current][0])\n", + " imgplot.set_clim(0.0,0.7)\n", + " a.set_title(\"prediction :\"+str(dataset[current][1]))\n", + " pt.colorbar(ticks = [0.1,0.3,0.5,0.7], orientation = 'horizontal')\n", + " pt.show()" + ] + } + ], + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 14a7f88b061d6ec635056a2528d2738db426ebe1 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Tue, 31 Jul 2018 19:54:59 +0530 Subject: [PATCH 02/71] Create tensorflow.md --- tensorflow.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 tensorflow.md diff --git a/tensorflow.md b/tensorflow.md new file mode 100644 index 0000000..b0539fa --- /dev/null +++ b/tensorflow.md @@ -0,0 +1,12 @@ +# Welcome to Tensorflow Study Jam ! +

+ Keralaai tf study jam +

+ +## Day 1 + +1. Intro to machine learning +2. Reducing Loss +3. Training and Testing sets +4. Logistic Regression +5. First Steps with Tensorflow From 3fc91c97c9a258f16bc7203c0c318d3ec950284d Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Tue, 31 Jul 2018 19:57:19 +0530 Subject: [PATCH 03/71] Update tensorflow.md --- tensorflow.md | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index b0539fa..820f7ee 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -4,7 +4,6 @@

## Day 1 - 1. Intro to machine learning 2. Reducing Loss 3. Training and Testing sets From 057bf808d88d44067be9d8002663d11135942d6b Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 11:23:16 +0530 Subject: [PATCH 04/71] Update tensorflow.md --- tensorflow.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index 820f7ee..5ef4739 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -3,9 +3,15 @@ Keralaai tf study jam

-## Day 1 +# Day 1 1. Intro to machine learning 2. Reducing Loss 3. Training and Testing sets 4. Logistic Regression 5. First Steps with Tensorflow + +## Intro to machine learning +content goes here +## Reducing Loss +content goes here +## Training and Testing Sets From 83912432d6ddebead3c4f603ae422e5256052fbd Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 12:06:54 +0530 Subject: [PATCH 05/71] Update tensorflow.md --- tensorflow.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/tensorflow.md b/tensorflow.md index 5ef4739..c55d479 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -15,3 +15,17 @@ content goes here ## Reducing Loss content goes here ## Training and Testing Sets +
+ +- **training set** — a subset to train a model. +- **test set** — a subset to test the trained model. + +

+ train-test divison +

+ +- **Validation set** - A subset of the data set—disjunct from the training set—that you use to adjust hyperparameters. + +

+ train-test divison +

From e5d5e58e5d5021724b956a589f9ed1a8637f5245 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 12:17:19 +0530 Subject: [PATCH 06/71] Update tensorflow.md --- tensorflow.md | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index c55d479..b3760e3 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -17,15 +17,24 @@ content goes here ## Training and Testing Sets
-- **training set** — a subset to train a model. -- **test set** — a subset to test the trained model. +1. Our goal is to create a machine learning model that generalizes well to new data. Our test set serves as a proxy for new data. + +2. We train the model using a Training set and the test set act as a proxy for new data! +

train-test divison

-- **Validation set** - A subset of the data set—disjunct from the training set—that you use to adjust hyperparameters. +- **training set** — a subset to train a model. +- **test set** — a subset to test the trained model. + +3.

train-test divison

+ + +- **Validation set** - A subset of the data set—disjunct from the training set—that you use to adjust hyperparameters. + From 1acb16a50acaba642b05ffc130d5cc9b2efb0cf4 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 12:21:35 +0530 Subject: [PATCH 07/71] Update tensorflow.md --- tensorflow.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index b3760e3..f96f93d 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -23,18 +23,19 @@ content goes here

- train-test divison + train-test divison

- **training set** — a subset to train a model. - **test set** — a subset to test the trained model. -3. +3. Dividing the data set into two sets is a good idea, but not a panacea. You can greatly reduce your chances of overfitting by partitioning the data set into the three subsets shown in the following figure:

- train-test divison + train-test divison

+4. Use the validation set to evaluate results from the training set. Then, use the test set to double-check your evaluation after the model has "passed" the validation set. - **Validation set** - A subset of the data set—disjunct from the training set—that you use to adjust hyperparameters. From a4883bcdf45bfa43d6cb481351c157e6e348d58a Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 12:26:25 +0530 Subject: [PATCH 08/71] Update tensorflow.md --- tensorflow.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index f96f93d..94d6e7d 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -17,7 +17,7 @@ content goes here ## Training and Testing Sets
-1. Our goal is to create a machine learning model that generalizes well to new data. Our test set serves as a proxy for new data. +1. Our goal is to create a machine learning model that generalizes well to new data. 2. We train the model using a Training set and the test set act as a proxy for new data! @@ -29,6 +29,8 @@ content goes here - **training set** — a subset to train a model. - **test set** — a subset to test the trained model. +### Wait, but there is a problem... + 3. Dividing the data set into two sets is a good idea, but not a panacea. You can greatly reduce your chances of overfitting by partitioning the data set into the three subsets shown in the following figure:

From 6e89f1fd18c6434d89120b3080ff6a693cc68778 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 12:34:36 +0530 Subject: [PATCH 09/71] Update tensorflow.md --- tensorflow.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index 94d6e7d..bf0b90c 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -17,9 +17,14 @@ content goes here ## Training and Testing Sets
-1. Our goal is to create a machine learning model that generalizes well to new data. +

+ train-test divison +

+ + +### 1. Our goal is to create a machine learning model that generalizes well to new data. -2. We train the model using a Training set and the test set act as a proxy for new data! +### 2. We train the model using a Training set and the test set act as a proxy for new data!

@@ -31,13 +36,13 @@ content goes here ### Wait, but there is a problem... -3. Dividing the data set into two sets is a good idea, but not a panacea. You can greatly reduce your chances of overfitting by partitioning the data set into the three subsets shown in the following figure: +### 3. You can greatly reduce your chances of overfitting by partitioning the data set into the three subsets shown in the following figure:

train-test divison

-4. Use the validation set to evaluate results from the training set. Then, use the test set to double-check your evaluation after the model has "passed" the validation set. +### 4. Use the validation set to evaluate results from the training set. Then, use the test set to double-check your evaluation after the model has "passed" the validation set. - **Validation set** - A subset of the data set—disjunct from the training set—that you use to adjust hyperparameters. From a8910f2664bd9e1b8041810519019fd6477eff50 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 12:47:27 +0530 Subject: [PATCH 10/71] Update tensorflow.md --- tensorflow.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index bf0b90c..33bdb4b 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -34,15 +34,5 @@ content goes here - **training set** — a subset to train a model. - **test set** — a subset to test the trained model. -### Wait, but there is a problem... -### 3. You can greatly reduce your chances of overfitting by partitioning the data set into the three subsets shown in the following figure: - -

- train-test divison -

- -### 4. Use the validation set to evaluate results from the training set. Then, use the test set to double-check your evaluation after the model has "passed" the validation set. - -- **Validation set** - A subset of the data set—disjunct from the training set—that you use to adjust hyperparameters. From 950a0005f0ac0166e672ffc4a5f0dcbeff5ba7cf Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 12:48:14 +0530 Subject: [PATCH 11/71] Update tensorflow.md --- tensorflow.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index 33bdb4b..9a5197a 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -21,10 +21,9 @@ content goes here train-test divison

+1. Our goal is to create a machine learning model that generalizes well to new data. -### 1. Our goal is to create a machine learning model that generalizes well to new data. - -### 2. We train the model using a Training set and the test set act as a proxy for new data! +2. We train the model using a Training set and the test set act as a proxy for new data!

From a5c2b45369e8a30420ea9eb6a58b51970e135aad Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 17:04:16 +0530 Subject: [PATCH 12/71] Update tensorflow.md --- tensorflow.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/tensorflow.md b/tensorflow.md index 9a5197a..116f2b7 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -33,5 +33,17 @@ content goes here - **training set** — a subset to train a model. - **test set** — a subset to test the trained model. +## Logistic Regression + +1. Many problems require a probability estimate as output. +2. Logistic regression is an extremely efficient mechanism for calculating probabilities. +3. a sigmoid function, defined as follows, produces output that always falls between 0 and 1. + +

+ train-test divison +

+ + + From 5bd420821510cbbef6519243fe713f1bb9475689 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 17:31:03 +0530 Subject: [PATCH 13/71] Update tensorflow.md --- tensorflow.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index 116f2b7..43127e4 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -40,7 +40,13 @@ content goes here 3. a sigmoid function, defined as follows, produces output that always falls between 0 and 1.

- train-test divison + sigmoid function +

+ +This is the plot of sigmoid function! + +

+ sigmoid graph

From 7236c45b2c2a0bfb0076caa8dd1dbccb8a777f3e Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 17:47:23 +0530 Subject: [PATCH 14/71] Update tensorflow.md --- tensorflow.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index 43127e4..e6b9ca7 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -43,7 +43,12 @@ content goes here sigmoid function

-This is the plot of sigmoid function! +Where, + +``` +y = w1x1 + w2x2 + ... wNxN +``` +and p is the predicted output.

sigmoid graph @@ -51,5 +56,3 @@ This is the plot of sigmoid function! - - From 9798c0808f6c0f3086c76be8be505d6efbb2c546 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 20:59:50 +0530 Subject: [PATCH 15/71] Add files via upload --- logloss.png | Bin 0 -> 15317 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 logloss.png diff --git a/logloss.png b/logloss.png new file mode 100644 index 0000000000000000000000000000000000000000..87b7966cfba421a499756d0582a03e04e603dee0 GIT binary patch literal 15317 zcmZXabyQqEx9|s-B7-}F6_-JZ6?d0X+#QO$ySuwnplES-cPmn$xVyW4^m*R<-n-T} zf6U5B_D*(o_Q^@|OPGS3I5HwWA^-qDmXr|r0sufPyr+fWVc-9+l?LGf09*}oVPOSH zVPO&ldm9sTOJe{)Ehxzi=Bu(i&RM3zQ%_-Q+^O}!shnc7V?c_SD71)>D20d+rh*`v zB?MY57)XN!gi#a_P-Lcta0{6_dwc1uU)8jkPj~Qknan0)^)Sf!05B!v;^Dz9f+C?{ zi#ISWAKcqK90>71we^LG^@WW6=ph<-w6vfGD0p^zb5X|yObt9W&jy#jS-o{)yW8Zz zGXY2lpgr8hU4uyOu>t#-m9|n805#=6Mj!@Whu;d?n1~>fi0wV>?VlbRf(3^UIF^^3 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+**Loss function for Logistic regression is Log Loss** - +

+ sigmoid graph +

From ca7c21482ef1b06219a11b294342a6a65d5c3faf Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 23:17:08 +0530 Subject: [PATCH 17/71] Update tensorflow.md --- tensorflow.md | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index 9eddf5b..36e69ac 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -11,7 +11,20 @@ 5. First Steps with Tensorflow ## Intro to machine learning -content goes here + +Machine learning is the art of making sense of data ! + +1. It do things normal code can't do and it helps to reduce the time you spend for coding. + +2. In machine learning we don't need to tell the algorithm what to do, we only need to show them some examples. + +### Types of machine learning algorithms: +- Supervised Learning +- Unsupervised Learning +- Reinforcement Learning + + + ## Reducing Loss content goes here ## Training and Testing Sets @@ -50,10 +63,14 @@ y = w1x1 + w2x2 + ... wNxN ``` and p is the predicted output. + +

sigmoid graph

+ + **Loss function for Logistic regression is Log Loss**

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z8;u#H=z||LljJ>Acq|er9VV#)C2t%DB*+fnf^tXh@*5M%pSVmWm()v9&4OZhW}FHx zVq3qLsK+#3ur<%Eta(JA3k_o6fLfZu)`h;afwWnw-sx$hB zxlE}{d0N0`Ps0MfQn(IIKB#Rimsd)b zjV@|#^O*?4F;$~h*-1?)uN9TcI0rn>f~aULl0~8Odl;@n_6FT`6EAiB>&MOi>m|E7!zopQ;V}99@CUBp^&5Is Iy3X Date: Thu, 2 Aug 2018 23:29:54 +0530 Subject: [PATCH 19/71] Update tensorflow.md --- tensorflow.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index 36e69ac..6711a7b 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -20,10 +20,22 @@ Machine learning is the art of making sense of data ! ### Types of machine learning algorithms: - Supervised Learning + +

+ supervised learning +

+ - Unsupervised Learning -- Reinforcement Learning +

+ Un-Supervised Learning +

+ +- Reinforcement Learning +

+ Reinforcement Learning +

## Reducing Loss content goes here From 01d751dc2703a5c058a29a27ae9139f617cfd09a Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 23:49:21 +0530 Subject: [PATCH 20/71] Update tensorflow.md --- tensorflow.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tensorflow.md b/tensorflow.md index 6711a7b..66d9854 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -37,6 +37,21 @@ Machine learning is the art of making sense of data ! Reinforcement Learning

+In supervised learning we create “models”, a model is basically a function that takes in simple inputs and produces useful predictions. Here we have features and labels in the dataset. + +**Features:** + +A feature is an input variable—the x variable in simple linear regression. A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features. + +Features of house price predicting ML model can be, + +- Total number of rooms. +- Age of house +- Locality of the house + +**Labels:** +A label is the thing we're predicting. It can be the price of a product, class probability in a classification. + ## Reducing Loss content goes here ## Training and Testing Sets From 820639bc3213a49a1e3b0a127a916634ed8bcfdf Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 23:56:00 +0530 Subject: [PATCH 21/71] Update tensorflow.md --- tensorflow.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/tensorflow.md b/tensorflow.md index 66d9854..b5f3d64 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -50,8 +50,23 @@ Features of house price predicting ML model can be, - Locality of the house **Labels:** + A label is the thing we're predicting. It can be the price of a product, class probability in a classification. +**Regression Vs Classification** + +A regression model is used to predict continuous values. + +For example, T + +- The probability of Captain America Dying in Avengers 4. +- Price of houses in california. + +A classification model predicts discrete values. It can make predictions that answer questions like, + +- Is this an image of a cat or dog or wade wilson. +- Pedicting whether a movie belongs to DC or Marvel(based on the dark screen may be) + ## Reducing Loss content goes here ## Training and Testing Sets From cba52c6e4da9d76332d5c31ccb71897468b7ca53 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Thu, 2 Aug 2018 23:57:01 +0530 Subject: [PATCH 22/71] Update tensorflow.md --- tensorflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index b5f3d64..584a4db 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -64,7 +64,7 @@ For example, T A classification model predicts discrete values. 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z!vNjRQUfe{-K7e4((XDc-ebdnN6pg$=Yt8gZ=}rZ{e8)dgx3UdKmot>Nn%r&c_!eu z-=m_bSdIuT7|^;wXXRVxuM@`MX5X@%C%7cg4;E)gRa;S1ohf^$q8M}77Ij(TtEg;b z#OP#W4ZlP2{CoBVcK&nL7%&}t=0G8qIHXvx!>3s5m^`1t@wB=Y_dZWC7nskW?>5Nk x&G7$#d_T4$o^3Shd7SgVu{$>K90d9R3`OSDe<{d2`{(~EQeyI=zl9Bh{y!8n4VwS} literal 0 HcmV?d00001 From 7837ba401af667d3f0d1a2552961641db04b912c Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Fri, 3 Aug 2018 00:53:29 +0530 Subject: [PATCH 24/71] Update tensorflow.md --- tensorflow.md | 65 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 64 insertions(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index 584a4db..a6933a1 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -67,8 +67,71 @@ A classification model predicts discrete values. It can make predictions that an - Is this an image of a cat or dog or Wade wilson. - Pedicting whether a movie belongs to DC or Marvel(based on the dark screen may be) +**Linear Regression** + +Linear regression is a method for finding the straight line or hyperplane that best fits a set of points. + +The line equation is, + +``` +y = mx + b + +``` + +In machine learning we use this convention instead, + +``` +y' = b + w1x1 +``` +Where, + +- y' is the label we are predicting. +- b is the bias. +- w1 is the weight of feature 1. Weight is the same concept as the "slope" + in the traditional equation of a line. +- x1 is a feature (a known input). + +To predict, just substitute the x1 values to the trained model. + +A sophisticated model can use more than one features. + +``` +y' = b + w1x1 + w2x2 + w3x3 + .... + wNxN +``` +1. **Training** a model simply means learning (determining) good values for all the weights and the bias from labeled examples. + +2. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called **empirical risk minimization**. + +3. Loss is the penalty for a bad prediction. If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater. + +4. The goal of training a model is to find a set of weights and biases that have low loss, on average, across all examples. + +First we have to find the loss. + +**L2 Loss/square loss** is a popular loss function. It is the given as + +``` += the square of the difference between the label and the prediction += (observation - prediction(x))2 += (y - y')2 +``` +**Mean square error (MSE)** is the average squared loss per example over the whole dataset. + +

+ MSE +

+ ## Reducing Loss -content goes here +Reducing the loss is similar to the **"Hot and cold game"** kids play! + +A Machine Learning model is trained by starting with an initial guess for the weights and bias and iteratively adjusting those guesses until learning the weights and bias with the lowest possible loss. + +**Gradient Descent** + +

+ Gradient Descent +

+ ## Training and Testing Sets
From 97cc65c74c157ad935ddf8ce457d975eb074c29b Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Fri, 3 Aug 2018 01:00:34 +0530 Subject: [PATCH 25/71] Update tensorflow.md --- tensorflow.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index a6933a1..70a30f4 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -126,12 +126,19 @@ Reducing the loss is similar to the **"Hot and cold game"** kids play! A Machine Learning model is trained by starting with an initial guess for the weights and bias and iteratively adjusting those guesses until learning the weights and bias with the lowest possible loss. -**Gradient Descent** +### Gradient Descent

- Gradient Descent + Gradient Descent

+### Learning Rate + +

+ Gradient Descent +

+ + ## Training and Testing Sets
@@ -171,7 +178,7 @@ and p is the predicted output.

- sigmoid graph + sigmoid graph

From 477fd1e2efa8eb49f9572854317be51e776ace49 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Fri, 3 Aug 2018 18:25:08 +0530 Subject: [PATCH 26/71] Update tensorflow.md --- tensorflow.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow.md b/tensorflow.md index 70a30f4..4bbe3c1 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -188,3 +188,7 @@ and p is the predicted output.

sigmoid graph

+ +## First Steps with Tensorflow + +Lets start ! From e7e9eec24e64e7b5ddd6a495c761e9037c9abf4e Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Fri, 3 Aug 2018 18:31:47 +0530 Subject: [PATCH 27/71] Update tensorflow.md --- tensorflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow.md b/tensorflow.md index 4bbe3c1..c236b71 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -57,7 +57,7 @@ A label is the thing we're predicting. It can be the price of a product, class p A regression model is used to predict continuous values. -For example, T +For example, - The probability of Captain America Dying in Avengers 4. - Price of houses in california. From d9c13f485cb7cb3662fca19884f6749be1fce293 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sat, 4 Aug 2018 00:33:48 +0530 Subject: [PATCH 28/71] Add files via upload --- regression.csv | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 regression.csv diff --git a/regression.csv b/regression.csv new file mode 100644 index 0000000..45d7bf7 --- /dev/null +++ b/regression.csv @@ -0,0 +1,64 @@ +X,Y +108,392.5 +19,46.2 +13,15.7 +124,422.2 +40,119.4 +57,170.9 +23,56.9 +14,77.5 +45,214.0 +10,65.3 +5,20.9 +48,248.1 +11,23.5 +23,39.6 +7,48.8 +2,6.6 +24,134.9 +6,50.9 +3,4.4 +23,113.0 +6,14.8 +9,48.7 +9,52.1 +3,13.2 +29,103.9 +7,77.5 +4,11.8 +20,98.1 +7,27.9 +4,38.1 +0,0.0 +25,69.2 +6,14.6 +5,40.3 +22,161.5 +11,57.2 +61,217.6 +12,58.1 +4,12.6 +16,59.6 +13,89.9 +60,202.4 +41,181.3 +37,152.8 +55,162.8 +41,73.4 +11,21.3 +27,92.6 +8,76.1 +3,39.9 +17,142.1 +13,93.0 +13,31.9 +15,32.1 +8,55.6 +29,133.3 +30,194.5 +24,137.9 +9,87.4 +31,209.8 +14,95.5 +53,244.6 +26,187.5 From b63447bcb7401b2e1b8a9af96088903251504d94 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sat, 4 Aug 2018 09:21:08 +0530 Subject: [PATCH 29/71] Update tensorflow.md --- tensorflow.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/tensorflow.md b/tensorflow.md index c236b71..c18a880 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -14,9 +14,9 @@ Machine learning is the art of making sense of data ! -1. It do things normal code can't do and it helps to reduce the time you spend for coding. +1. It does things normal code can't do and it helps to reduce the time you spend for coding. -2. In machine learning we don't need to tell the algorithm what to do, we only need to show them some examples. +2. In machine learning we don't need to tell the algorithm what to do, we only need to show it some examples. ### Types of machine learning algorithms: - Supervised Learning @@ -162,6 +162,15 @@ A Machine Learning model is trained by starting with an initial guess for the we 1. Many problems require a probability estimate as output. 2. Logistic regression is an extremely efficient mechanism for calculating probabilities. + +``` +P(thenga|day) = 0.05 +coconut falling on head = +0.05*365 +~= 18 +``` + + 3. a sigmoid function, defined as follows, produces output that always falls between 0 and 1.

+ Over and Under fitting +

+ * Undeffitting occurs,when the model is not able to obtain a sufficiently low error value * Overfitting occurs, when the gap between the training and test error is too large From 743ce86aa06e6d0abfb6722642f0566bde5b5f91 Mon Sep 17 00:00:00 2001 From: akshaydevml <42264146+akshaydevml@users.noreply.github.com> Date: Fri, 10 Aug 2018 14:20:36 +0530 Subject: [PATCH 37/71] Update regularisation.md --- regularisation.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/regularisation.md b/regularisation.md index 882a4b3..39302d2 100644 --- a/regularisation.md +++ b/regularisation.md @@ -25,6 +25,10 @@ ### Regularisation +

+ Regularisation +

+ * To alleviate the problem of underfitting and overfitting,we implement Regularisation * Last lecture, we talked about features,and how each feature contributes differently to the loss function * Sometimes, we have to express preference for one feature over the other From 1e98b215ba87fda7aa2b11d7e140ff7eb292f5f4 Mon Sep 17 00:00:00 2001 From: akshaydevml <42264146+akshaydevml@users.noreply.github.com> Date: Fri, 10 Aug 2018 19:07:46 +0530 Subject: [PATCH 38/71] added validation set and dropout --- regularisation.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/regularisation.md b/regularisation.md index 39302d2..8e83e60 100644 --- a/regularisation.md +++ b/regularisation.md @@ -34,3 +34,16 @@ * Sometimes, we have to express preference for one feature over the other * For example, we might express a preference for linear features over non-linear features * There are many ways of expressing preferences, together they are called "Regularisation" +* There are many type of Regularisation,L2 regularisation,Dropout regularisation +* In dropout regularisation, we take out random nodes for every iteration. +* The intuition behind dropout regularisation is that, we can't rely on one feature,so we have to spread out the weights + + +### Validation Set + +* Till now,we talked about the test set and the training set +* However,there is one more set,called the Validation set +* Earlier,we talked about test set,which we use to test how well the model generalises to new examples +* But,it is also important that we don't use the test set to fine tune our Hyperparameters +* Therefore,we need a new set,this is the validation set.It is always constructed from the training set,by splitting it into two +* We use the validation set to estimate the generalisation error and update our hyperparameters accordingly From 8241e851366a9d1db109773f9b7a2ee94076e037 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Fri, 10 Aug 2018 21:15:28 +0530 Subject: [PATCH 39/71] Create pytorch.md --- pytorch.md | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 pytorch.md diff --git a/pytorch.md b/pytorch.md new file mode 100644 index 0000000..f11477a --- /dev/null +++ b/pytorch.md @@ -0,0 +1,33 @@ +# Intro to PyTorch and Deep Learning! + +## Traditional programming and Machine learning + +Machine learning is making sense of data. + +![](https://scontent.fcok1-1.fna.fbcdn.net/v/t1.0-9/38647232_1065262550318293_8878660628852506624_o.jpg?_nc_cat=0&oh=23aa585957059eed98c0526dbce4075d&oe=5BFFE65D) + +## Machine learning and Deep learning + +In traditional machine learning, the learning process is supervised and the programmer has to be very, very specific when telling the computer what types of things it should be looking for when deciding if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction and the computer's success rate depends entirely upon the programmer's ability to accurately define a feature set for "dog." + +Computer programs that use deep learning go through much the same process. Each algorithm in the hierarchy applies a nonlinear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep. + +![](https://qph.fs.quoracdn.net/main-qimg-6c1dc5666bd31bf16120d332957b4059) + +## Neural Networks + +Neural networks are universal function optimisers. + +### Feed forward neural networks + +

+ feed forward +

+ +### Recurring neural networks + + +

+ recurring +

+ From 8d3d748fd70ca2b0dd2dba58ff5631eae71d4d2a Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Fri, 10 Aug 2018 21:33:10 +0530 Subject: [PATCH 40/71] Update pytorch.md --- pytorch.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/pytorch.md b/pytorch.md index f11477a..2786153 100644 --- a/pytorch.md +++ b/pytorch.md @@ -31,3 +31,10 @@ Neural networks are universal function optimisers. recurring

+### Convolutional Neural Networks + +

+ convnet +

+ + From f1b66fe095288600fa6c33baf105b47957258365 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sat, 11 Aug 2018 00:32:25 +0530 Subject: [PATCH 41/71] Update pytorch.md --- pytorch.md | 49 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/pytorch.md b/pytorch.md index 2786153..206f991 100644 --- a/pytorch.md +++ b/pytorch.md @@ -37,4 +37,53 @@ Neural networks are universal function optimisers. convnet

+## Intro to PyTorch + +Its a scientific computation package in python which helps to use the power of GPUs and its a deep learning research platform. + +### Tensors + +Tensors are similar to numpy ndarrays but it can be also be used on GPUs for fast computations. + +```python +import torch +``` + +Lets construct an empty matrix + +```python +x = torch.empty(5, 3) +``` +output.. +``` +tensor([[-5.0254e+18, 4.5609e-41, -5.0254e+18], + [ 4.5609e-41, 1.6395e-43, 1.3873e-43], + [ 1.4574e-43, 4.4842e-44, 1.4293e-43], + [ 1.4714e-43, 1.5134e-43, 1.4153e-43], + [ 4.4842e-44, 1.5554e-43, 1.5975e-43]]) +``` + +other tensor operators are ```torch.rand()```, ```torch.zeros()```, ```torch.tensor()``` ..etc. + +### Operations + +Addition, + +```python +print(torch.add(x, y)) +``` + +Converting a torch tensor to numpy and vice versa is simple in PyTorch. + +```python +a = torch.ones(5) +a = a.numpy() +``` +In reverse, + +```python +import numpy as np +a = np.ones(5) +b = torch.from_numpy(a) +``` From 08b02e9795c0721c88c98ada9bd2c1953983ddc0 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sat, 11 Aug 2018 00:55:51 +0530 Subject: [PATCH 42/71] Update pytorch.md --- pytorch.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/pytorch.md b/pytorch.md index 206f991..a0b0feb 100644 --- a/pytorch.md +++ b/pytorch.md @@ -86,4 +86,21 @@ import numpy as np a = np.ones(5) b = torch.from_numpy(a) ``` +### Autograd, Tensor and Function. + +Autograd is automatic differentiation by calling ```.backward()``` + +```torch.Tensor``` is the central class of the package. If you set its attribute .requires_grad as True, it starts to track all operations on it. When you finish your computation you can call .backward() and have all the gradients computed automatically. + +```autograd.Function``` implements forward and backward definitions of an autograd operation. + +### Training a neural network + +- Define the neural network that has some learnable parameters (or weights) +- terate over a dataset of inputs +- Process input through the network +- Compute the loss (how far is the output from being correct) +- Propagate gradients back into the network’s parameters +- Update the weights of the network, typically using a simple update rule: +``` weight = weight - learning_rate * gradient``` From 133de263cbd1a9bbc19a6c0562a7795bf8aacb09 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sun, 12 Aug 2018 09:49:35 +0530 Subject: [PATCH 43/71] Add files via upload --- IMG-20180812-WA0001.jpg | Bin 0 -> 245968 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 IMG-20180812-WA0001.jpg diff --git a/IMG-20180812-WA0001.jpg 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create mode 100644 NeuralNetworks.md diff --git a/NeuralNetworks.md b/NeuralNetworks.md new file mode 100644 index 0000000..3f61d67 --- /dev/null +++ b/NeuralNetworks.md @@ -0,0 +1,13 @@ +##Neural Networks + +-Last class we discussed Supervised Learning,where we fed data to the model and got predictions +-In Supervised Learning,we used a specific algorithm,to process the data +-In the domain of deep learning,we primarily use Neural Network +-In,Neural Networks,we define a task and the network tries to "learn",the function,on it's own +-A neural network is comprised of many "layers" +-There are three types of layers,input,output and hidden +-The hidden layers are layers,which come in between the input and output +-They are called,"hidden',because,what goes in them,is unknown to us,and we have no role in setting their state +-The dimensionality of these hidden layers represent,the "width" of the model +-Each layer in the Neural Network,consists of "nodes" or also called "units" +- From 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From 84305a89f9ae1e4172c372d2eefcaf6f00efd1b8 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sat, 4 Aug 2018 10:03:53 +0530 Subject: [PATCH 30/71] Add files via upload --- regression.py | 104 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 104 insertions(+) create mode 100644 regression.py diff --git a/regression.py b/regression.py new file mode 100644 index 0000000..1f89bac --- /dev/null +++ b/regression.py @@ -0,0 +1,104 @@ +import pandas as pd +import numpy as np +import tensorflow as tf +from tensorflow.data import Dataset +from sklearn import metrics +import math +#sheet = pd.ExcelFile("regression.xls") +#print(sheet.sheet_names) +#df = sheet.parse("sheet1") +#df.to_csv('regression.csv', index=False) +insurance_dataframe = pd.read_csv('regression.csv', sep=',') +insurance_dataframe = insurance_dataframe.reindex(np.random.permutation(insurance_dataframe.index)) +print(insurance_dataframe.describe()) + +#my_feature = insurance_dataframe[["X"]] +#print(my_feature) +#feature_columns = [tf.feature_column.numeric_column("X")] +#targets = insurance_dataframe["Y"] +#my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) +#my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0) + +#linear_regressor = tf.estimator.LinearRegressor(feature_columns=feature_columns, optimizer=my_optimizer) + +def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None): + features = {key:np.array(value) for key, value in dict(features).items()} + #print(features) + ds = Dataset.from_tensor_slices((features, targets)) + ds = ds.batch(batch_size).repeat(num_epochs) + if shuffle: + ds = ds.shuffle(buffer_size=10000) + + features, labels = ds.make_one_shot_iterator().get_next() + return features, labels + +#k = my_input_function(my_feature, targets) +#print(k) +#_ = linear_regressor.train(input_fn = lambda:my_input_fn(my_feature, targets),steps=100) + +#prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False) +#predictions = linear_regressor.predict(input_fn=prediction_input_fn) +#predictions = np.array([item['predictions'][0] for item in predictions]) +#print(predictions) +#mean_squared_error = metrics.mean_squared_error(predictions, targets) +#root_mean_squared_error = math.sqrt(mean_squared_error) +#print("Mean Squared Error (on training data): %0.3f" % mean_squared_error) +#print("Root Mean Squared Error (on training data): %0.3f" % root_mean_squared_error) + +def train_model(learning_rate, steps, batch_size, input_feature="X"): + + + periods = 10 + steps_per_period = steps / periods + + my_feature = input_feature + my_feature_data = insurance_dataframe[[my_feature]] + #print(my_feature_data) + my_label = "Y" + targets = insurance_dataframe[my_label] + + # Create feature columns. + feature_columns = [tf.feature_column.numeric_column(my_feature)] + + # Create input functions. + training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size) + prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False) + + # Create a linear regressor object. + my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) + my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0) + linear_regressor = tf.estimator.LinearRegressor( + feature_columns=feature_columns, + optimizer=my_optimizer + ) + + + # Train the model, but do so inside a loop so that we can periodically assess + # loss metrics. + print("Training model...") + print("RMSE (on training data):") + root_mean_squared_errors = [] + for period in range (0, periods): + # Train the model, starting from the prior state. + linear_regressor.train( + input_fn=training_input_fn, + steps=steps_per_period + ) + # Take a break and compute predictions. + predictions = linear_regressor.predict(input_fn=prediction_input_fn) + predictions = np.array([item['predictions'][0] for item in predictions]) + + # Compute loss. + root_mean_squared_error = math.sqrt( + metrics.mean_squared_error(predictions, targets)) + # Occasionally print the current loss. + print(" period %02d : %0.2f" % (period, root_mean_squared_error)) + # Add the loss metrics from this period to our list. + root_mean_squared_errors.append(root_mean_squared_error) + + + print("Model training finished.") + print("Final RMSE (on training data): %0.2f" % root_mean_squared_error) + +train_model(learning_rate=0.01, steps=100, batch_size=1) + From be961a98eb14df742607705334277d7aa9cdb802 Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sat, 4 Aug 2018 13:13:19 +0530 Subject: [PATCH 31/71] Update tensorflow.md --- tensorflow.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow.md b/tensorflow.md index c18a880..5ecf890 100644 --- a/tensorflow.md +++ b/tensorflow.md @@ -201,3 +201,5 @@ and p is the predicted output. ## First Steps with Tensorflow Lets start ! + +https://bit.ly/2KrnCJ5 From f4a2883ef238b27ff9aed71b013b3c85c619e8ca Mon Sep 17 00:00:00 2001 From: Gopikrishnan Sasikumar Date: Sat, 4 Aug 2018 13:13:47 +0530 Subject: [PATCH 32/71] Update tensorflow.md From e08f94f2851e7f13816890d564cc8265b9f11354 Mon Sep 17 00:00:00 2001 From: akshaydevml <42264146+akshaydevml@users.noreply.github.com> Date: Fri, 10 Aug 2018 13:41:49 +0530 Subject: [PATCH 33/71] Create regularisation.md --- regularisation.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 regularisation.md diff --git a/regularisation.md b/regularisation.md new file mode 100644 index 0000000..5af95a5 --- /dev/null +++ b/regularisation.md @@ -0,0 +1,20 @@ +# Regularisation + +### Underfitting and Overfitting +* Last session we talked about two sets,the training and test set +* Training set is used in the creation of the model +* We can run the model on the training set to determine the correct weights and biases +* We then use a seperate test,to see how the model performs on new examples +* The central challenge in machine learning is to make our model perform well on previously unseen inputs +* Typically,when training a machine learning model,we compute the "loss" on the training set and try to minmimise it using Gradient Descent +* This is simply Optimisation,but in Machine Learning,we are also interested in minimising the test error +* When we use a ML model,we do not set the parameteres ahead of time ,we first train it on the training set,then se the parameters to test it on the test set +* Under this process,the expected test set error is greater than or equal to the expected value of the training error +* So,there are mainly two factors which determine how well a ML model will perform +* The first one is, the training error should be small +* Secondly, the gap between the training and test error should be small +* These two factors are the reason for the two biggest challenges in ML, Overfitting and Underfitting +* Undeffitting occurs,when the model is not able to obtain a sufficiently low error value +* Overfitting occurs, when the gap between the training and test error is too large + + From 9415483f0dc986c75777492ff78c56d82d7b55c3 Mon Sep 17 00:00:00 2001 From: akshaydevml <42264146+akshaydevml@users.noreply.github.com> Date: Fri, 10 Aug 2018 13:55:03 +0530 Subject: [PATCH 34/71] Add regularization notes --- regularisation.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/regularisation.md b/regularisation.md index 5af95a5..cb4819c 100644 --- a/regularisation.md +++ b/regularisation.md @@ -18,3 +18,10 @@ * Overfitting occurs, when the gap between the training and test error is too large +### Regularisation + +* To alleviate the problem of underfitting and overfitting,we implement Regularisation +* Last lecture, we talked about features,and how each feature contributes differently to the loss function +* Sometimes, we have to express preference for one feature over the other +* For example, we might express a preference for linear features over non-linear features +* There are many ways of expressing preferences, together they are called "Regularisation" From a55b0589ec99efaae1cca9b0a7d718332f3d5e60 Mon Sep 17 00:00:00 2001 From: akshaydevml <42264146+akshaydevml@users.noreply.github.com> Date: Fri, 10 Aug 2018 14:14:02 +0530 Subject: [PATCH 35/71] Add image files for regularisation --- regularisation_1.jpg | Bin 0 -> 57553 bytes uofitting_1.jpg | Bin 0 -> 60860 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 regularisation_1.jpg create mode 100644 uofitting_1.jpg diff --git a/regularisation_1.jpg b/regularisation_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e1cd44b848fe7f325ea5d25f7d6190a7826e2d74 GIT binary patch literal 57553 zcmdSAXIxWD7cU&JP&}Y0MJa;PK|rO4qN0EV2`!<7CPxuM3_Vm43s|U1RUjZj2uTQ` 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zQaFrU=B{%*i-|a$oqR+)(y-4XorF$eacjO%Ey4Z4^#gDDr@|+^_80I9K2)ljw#3d z>e{m&4c1ND!-9R-#aeFWsvNt`in6oYrt*u3C9(Z7-IBet6^7*g%~B+=wJi@lk??;-Ut{0i1 zT{&>Y3$b}T!(aF4Y?CkD zk^Z>rXB)URV&_)v>lSW*iqMPWP-e{mW2{N@LEHqsE;xW0kd#)NRX1~zlXo-_2CT>1 z^ge?+{O=3@zbR)VP}zK7Cu;ovnMy01{8p|WI8p$NM<7sA60jv(@4GAC?)fhCX`lt{ zn*M&{M<@#ez!d$nEqQk0r#!5Mg83mCJimW4UPpFtM(zI@s4kn)`S$*&32I5!<+i)$ z1^Dkd)KckR_wRrLSPyXOWhZ+><7mhVgc6*!=1LE(5nd2$C(gc$_N z8aU@aYy4?%c=H!A)r_u$5{3FM&Ow>)XtEpWgwuM Date: Fri, 10 Aug 2018 14:19:27 +0530 Subject: [PATCH 36/71] Update regularisation.md --- regularisation.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/regularisation.md b/regularisation.md index cb4819c..882a4b3 100644 --- a/regularisation.md +++ b/regularisation.md @@ -14,6 +14,11 @@ * The first one is, the training error should be small * Secondly, the gap between the training and test error should be small * These two factors are the reason for the two biggest challenges in ML, Overfitting and Underfitting + +