Added links to notebooks, some Kaggle links still missing (Kaggle is down)

Author: JannesKlaas

README.md

@@ -1 +1,71 @@
-# The code for this repository is under development :construction_worker:
+# The code for this repository is under development :construction_worker:
+
+# How to run this code 
+The code in this repository is quite compute heavy and best run on a GPU enabled machine. The datascience platform [Kaggle](http://kaggle.com/) offers free GPU recourses together with free online Jupyter notebooks. To make edits on the Kaggle notebooks, click 'Fork' to create a new copy of the notebook. You will need a Kaggle account for this.
+
+Alternatively you can just view the notebooks on [NB Viewer](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/tree/master/) or download the code and run it locally.
+
+## Chapter 1 - A neural Network from Scratch
+A neural network from Scratch & Intro to Keras: [Run on Kaggle](https://www.kaggle.com/jannesklaas/machine-learning-for-finance-chapter-1-code), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/1%20A%20neural%20network%20from%20scratch%20%26%20Intro%20to%20Keras.ipynb)
+
+Excercise excel sheet: [Download](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/1%20Excel%20Exercise.xlsx)
+
+## Chapter 2 - Structured Data
+
+Credit card fraud detection: [Run On Kaggle](https://www.kaggle.com/jannesklaas/structured-data-code), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/2%20structured%20data.ipynb)
+
+## Chapter 3 - Computer Vision 
+Classifying MNIST digits: [Run On Kaggle](https://www.kaggle.com/jannesklaas/classifying-mnist-digits), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/3.1%20MNIST.ipynb)
+
+Classifying Plants: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/3.2%20Plant%20Classification.ipynb)
+
+## Chapter 4 - Time Series 
+Forecasting Web Traffic: Classic Methods: [Run On Kaggle](https://www.kaggle.com/jannesklaas/time-series-eda), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/4.1%20EDA%20%26%20Classic%20methods.ipynb)
+
+Forecasting Web Traffic: Time Series Neural Nets: [Run On Kaggle](https://www.kaggle.com/jannesklaas/nns-on-time-series), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/4.2%20NN%20on%20time%20series.ipynb)
+
+Expressing Uncertainty with Bayesian Deep Learning: [Run On Kaggle](https://www.kaggle.com/jannesklaas/bayesian), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/4.3%20Bayesian%20Deep%20Learning.ipynb)
+
+## Chapter 5 - Natural Language processing
+Analyzing the News: [Run On Kaggle](https://www.kaggle.com/jannesklaas/analyzing-the-news), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.1%20Analyzing%20the%20news.ipynb)
+
+Classifying Tweets: [Run On Kaggle](https://www.kaggle.com/jannesklaas/nlp-disasters), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.2%20Classifying%20Tweets.ipynb)
+
+Topic modeling with LDA: [Run On Kaggle](https://www.kaggle.com/jannesklaas/topic-modeling-with-lda), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.3%20Topic%20Modeling.ipynb)
+
+Sequence to Sequence models: [Run On Kaggle](https://www.kaggle.com/jannesklaas/a-simple-seq2seq-translator), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.4%20Translation.ipynb)
+
+## Chapter 6 - Generative Models 
+(Variational) Autoencoder for MNIST: [Run On Kaggle](https://www.kaggle.com/jannesklaas/mnist-autoencoder-vae), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/6.1%20MNIST%20examples.ipynb)
+
+(Variational) Autoencoder for Fraud Detection: [Run On Kaggle](https://www.kaggle.com/jannesklaas/credit-vae), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/6.2%20Fraud%20examples.ipynb)
+
+Semi Supervised Generative Adversarial Network for Fraud Detection: [Run On Kaggle](https://www.kaggle.com/jannesklaas/semi-supervised-gan-for-fraud-detection), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/6.3%20SGAN.ipynb)
+
+## Chapter 7 - Reinforcement Learning 
+Q-Learning: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/7.1%20Q-Learning.ipynb)
+
+A2C Pole Balancing: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/7.1%20Q-Learning.ipynb)
+
+A2C for Trading: [Run On Kaggle](https://www.kaggle.com/jannesklaas/a2c-stock-trading) [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/7.3%20A2C%20Trading.ipynb)
+
+## Chapter 8 - Debugging ML Systems
+Unit Testing Data: [Run On Kaggle](https://www.kaggle.com/jannesklaas/marbles-test), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.1%20Unit%20Testing%20Data.ipynb)
+
+Hyperparameter Optimization: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.2%20Hyperopt.ipynb)
+
+Learning Rate Search: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.4%20LR_Search.ipynb)
+
+Using Tensorboard: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.5%20Tensorboard.ipynb)
+
+Converting Keras Models to TF Estimators: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.6%20TF%20Estimator.ipynb)
+
+Faster Python with Cython: [Download Part 1](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/cython_fib_8_7.pyx), [Download Part 2](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/8_7_cython_setup.py)
+
+## Chapter 9 - Fighting Bias in Machine Learning 
+
+Understanding Parity in Excel: [Download](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/9.1_parity.xlsx)
+
+Learning How to Pivot: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/9.2_Learning_to_be_fair.ipynb)
+
+Interpretability with SHAP: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/9.3_SHAP.ipynb)