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Navaneeth Malingan 2020-04-23 20:07:19 +05:30
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README.md
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@ -281,6 +281,7 @@ _Note: Below you can find the best lectures for popular Machine Learning Algorit
- [CS131 Computer Vision: Foundations and Applications Fall 2019](http://vision.stanford.edu/teaching/cs131_fall1920/index.html)
- [CS231A: Computer Vision, From 3D Reconstruction to Recognition Winter 2018](http://web.stanford.edu/class/cs231a/)
- [CS231n Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/)
### CNN

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autonomous_car/README.md
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@ -5,3 +5,7 @@
## [waymo-open-dataset](https://github.com/waymo-research/waymo-open-dataset)
Waymo Open Dataset https://www.waymo.com/open
### MIT 6.S094: Deep Learning for Self-Driving Cars
- [DeepTraffic](https://selfdrivingcars.mit.edu/deeptraffic/)

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gans/README.md
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@ -36,6 +36,11 @@ Look at the papers I mentioned. Links in the link below
- Papers - https://github.com/terryum/awesome-deep-learning-papers#new-papers
- Ai blog - https://ai.googleblog.com/
## Demo
- [Image-to-Image Translation with Conditional Adversarial Nets](https://phillipi.github.io/pix2pix/)
- [Image-to-Image Demo](https://affinelayer.com/pixsrv/) _Interactive Image Translation with pix2pix-tensorflow_
# Courses
- [A to Z ML/DL](https://www.udemy.com/course/machinelearning/)

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webinars/computer_vision/Computer Vision Share.pdf 100644
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webinars/decision_trees_and_random_forest/Ensemble_Methods.ipynb 100644
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Ensemble Methods.ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "-mCmaGdYCtBc",
"colab_type": "code",
"colab": {}
},
"source": [
"# Load Library\n",
"from sklearn.datasets import make_moons\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4ja4yb4_CzlY",
"colab_type": "code",
"colab": {}
},
"source": [
"# Step1: Create data set\n",
"X, y = make_moons(n_samples=10000, noise=.5, random_state=0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WbIUBF8pdXUF",
"colab_type": "code",
"colab": {}
},
"source": [
"# Step2: Split the training test set\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8vRma5CzC1id",
"colab_type": "code",
"outputId": "47d557ac-f8d7-4b3d-8fb8-94893afbc5fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"# Step 3: Fit a Decision Tree model as comparison\n",
"clf = DecisionTreeClassifier()\n",
"clf.fit(X_train, y_train)\n",
"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
" Train Accuracy 1.0\n",
" Test Accuracy 0.751\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LeoG6gvlC1oR",
"colab_type": "code",
"outputId": "42772dfc-410f-474d-c442-a940260ac783",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"# Step 4: Fit a Random Forest model, \" compared to \"Decision Tree model, accuracy go up by 5%\n",
"clf = RandomForestClassifier(n_estimators=100, max_features=\"auto\",random_state=0)\n",
"clf.fit(X_train, y_train)\n",
"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
" Train Accuracy 1.0\n",
" Test Accuracy 0.7965\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "h_ntW2CiC1ll",
"colab_type": "code",
"outputId": "753a8327-e25f-4c2d-9d39-e3ee62c13192",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"# Step 5: Fit a AdaBoost model, \" compared to \"Decision Tree model, accuracy go up by 10%\n",
"clf = AdaBoostClassifier(n_estimators=100)\n",
"clf.fit(X_train, y_train)\n",
"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
" Train Accuracy 0.825625\n",
" Test Accuracy 0.833\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GXw2cMWuC92S",
"colab_type": "code",
"outputId": "ce935fdf-df0a-4405-e6d3-a41af02a7ae6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"# Step 6: Fit a Gradient Boosting model, \" compared to \"Decision Tree model, accuracy go up by 10%\n",
"clf = GradientBoostingClassifier(n_estimators=100)\n",
"clf.fit(X_train, y_train)\n",
"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
" Train Accuracy 0.829875\n",
" Test Accuracy 0.8335\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QY37GWzl9CQD",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}