shervinea · rmaso · Sep 25, 2018 · Oct 3, 2018 · Nov 16, 2018
diff --git a/CONTRIBUTORS b/CONTRIBUTORS
@@ -1,7 +1,12 @@
 --ar
   Amjad Khatabi (translation of deep learning)
   Zaid Alyafeai (review of deep learning)
-
+
+--ca
+  Ruben Maso Carcases (translation of probabilities and statistics)
+  Ruben Maso Carcases (translation of linear algebra)
+  Ruben Maso Carcases (translation of machine learning tips and tricks)
+
 --de
 
 --es 

diff --git a/ca/cheatsheet-deep-learning.md b/ca/cheatsheet-deep-learning.md
@@ -0,0 +1,321 @@
+**1. Deep Learning cheatsheet**
+
+&#10230;
+
+<br>
+
+**2. Neural Networks**
+
+&#10230;
+
+<br>
+
+**3. Neural networks are a class of models that are built with layers. Commonly used types of neural networks include convolutional and recurrent neural networks.**
+
+&#10230;
+
+<br>
+
+**4. Architecture ― The vocabulary around neural networks architectures is described in the figure below:**
+
+&#10230;
+
+<br>
+
+**5. [Input layer, hidden layer, output layer]**
+
+&#10230;
+
+<br>
+
+**6. By noting i the ith layer of the network and j the jth hidden unit of the layer, we have:**
+
+&#10230;
+
+<br>
+
+**7. where we note w, b, z the weight, bias and output respectively.**
+
+&#10230;
+
+<br>
+
+**8. Activation function ― Activation functions are used at the end of a hidden unit to introduce non-linear complexities to the model. Here are the most common ones:**
+
+&#10230;
+
+<br>
+
+**9. [Sigmoid, Tanh, ReLU, Leaky ReLU]**
+
+&#10230;
+
+<br>
+
+**10. Cross-entropy loss ― In the context of neural networks, the cross-entropy loss L(z,y) is commonly used and is defined as follows:**
+
+&#10230;
+
+<br>
+
+**11. Learning rate ― The learning rate, often noted α or sometimes η, indicates at which pace the weights get updated. This can be fixed or adaptively changed. The current most popular method is called Adam, which is a method that adapts the learning rate.**
+
+&#10230;
+
+<br>
+
+**12. Backpropagation ― Backpropagation is a method to update the weights in the neural network by taking into account the actual output and the desired output. The derivative with respect to weight w is computed using chain rule and is of the following form:**
+
+&#10230;
+
+<br>
+
+**13. As a result, the weight is updated as follows:**
+
+&#10230;
+
+<br>
+
+**14. Updating weights ― In a neural network, weights are updated as follows:**
+
+&#10230;
+
+<br>
+
+**15. Step 1: Take a batch of training data.**
+
+&#10230;
+
+<br>
+
+**16. Step 2: Perform forward propagation to obtain the corresponding loss.**
+
+&#10230;
+
+<br>
+
+**17. Step 3: Backpropagate the loss to get the gradients.**
+
+&#10230;
+
+<br>
+
+**18. Step 4: Use the gradients to update the weights of the network.**
+
+&#10230;
+
+<br>
+
+**19. Dropout ― Dropout is a technique meant at preventing overfitting the training data by dropping out units in a neural network. In practice, neurons are either dropped with probability p or kept with probability 1−p**
+
+&#10230;
+
+<br>
+
+**20. Convolutional Neural Networks**
+
+&#10230;
+
+<br>
+
+**21. Convolutional layer requirement ― By noting W the input volume size, F the size of the convolutional layer neurons, P the amount of zero padding, then the number of neurons N that fit in a given volume is such that:**
+
+&#10230;
+
+<br>
+
+**22. Batch normalization ― It is a step of hyperparameter γ,β that normalizes the batch {xi}. By noting μB,σ2B the mean and variance of that we want to correct to the batch, it is done as follows:**
+
+&#10230;
+
+<br>
+
+**23. It is usually done after a fully connected/convolutional layer and before a non-linearity layer and aims at allowing higher learning rates and reducing the strong dependence on initialization.**
+
+&#10230;
+
+<br>
+
+**24. Recurrent Neural Networks**
+
+&#10230;
+
+<br>
+
+**25. Types of gates ― Here are the different types of gates that we encounter in a typical recurrent neural network:**
+
+&#10230;
+
+<br>
+
+**26. [Input gate, forget gate, gate, output gate]**
+
+&#10230;
+
+<br>
+
+**27. [Write to cell or not?, Erase a cell or not?, How much to write to cell?, How much to reveal cell?]**
+
+&#10230;
+
+<br>
+
+**28. LSTM ― A long short-term memory (LSTM) network is a type of RNN model that avoids the vanishing gradient problem by adding 'forget' gates.**
+
+&#10230;
+
+<br>
+
+**29. Reinforcement Learning and Control**
+
+&#10230;
+
+<br>
+
+**30. The goal of reinforcement learning is for an agent to learn how to evolve in an environment.**
+
+&#10230;
+
+<br>
+
+**31. Definitions**
+
+&#10230;
+
+<br>
+
+**32. Markov decision processes ― A Markov decision process (MDP) is a 5-tuple (S,A,{Psa},γ,R) where:**
+
+&#10230;
+
+<br>
+
+**33. S is the set of states**
+
+&#10230;
+
+<br>
+
+**34. A is the set of actions**
+
+&#10230;
+
+<br>
+
+**35. {Psa} are the state transition probabilities for s∈S and a∈A**
+
+&#10230;
+
+<br>
+
+**36. γ∈[0,1[ is the discount factor**
+
+&#10230;
+
+<br>
+
+**37. R:S×A⟶R or R:S⟶R is the reward function that the algorithm wants to maximize**
+
+&#10230;
+
+<br>
+
+**38. Policy ― A policy π is a function π:S⟶A that maps states to actions.**
+
+&#10230;
+
+<br>
+
+**39. Remark: we say that we execute a given policy π if given a state s we take the action a=π(s).**
+
+&#10230;
+
+<br>
+
+**40. Value function ― For a given policy π and a given state s, we define the value function Vπ as follows:**
+
+&#10230;
+
+<br>
+
+**41. Bellman equation ― The optimal Bellman equations characterizes the value function Vπ∗ of the optimal policy π∗:**
+
+&#10230;
+
+<br>
+
+**42. Remark: we note that the optimal policy π∗ for a given state s is such that:**
+
+&#10230;
+
+<br>
+
+**43. Value iteration algorithm ― The value iteration algorithm is in two steps:**
+
+&#10230;
+
+<br>
+
+**44. 1) We initialize the value:**
+
+&#10230;
+
+<br>
+
+**45. 2) We iterate the value based on the values before:**
+
+&#10230;
+
+<br>
+
+**46. Maximum likelihood estimate ― The maximum likelihood estimates for the state transition probabilities are as follows:**
+
+&#10230;
+
+<br>
+
+**47. times took action a in state s and got to s′**
+
+&#10230;
+
+<br>
+
+**48. times took action a in state s**
+
+&#10230;
+
+<br>
+
+**49. Q-learning ― Q-learning is a model-free estimation of Q, which is done as follows:**
+
+&#10230;
+
+<br>
+
+**50. View PDF version on GitHub**
+
+&#10230;
+
+<br>
+
+**51. [Neural Networks, Architecture, Activation function, Backpropagation, Dropout]**
+
+&#10230;
+
+<br>
+
+**52. [Convolutional Neural Networks, Convolutional layer, Batch normalization]**
+
+&#10230;
+
+<br>
+
+**53. [Recurrent Neural Networks, Gates, LSTM]**
+
+&#10230;
+
+<br>
+
+**54. [Reinforcement learning, Markov decision processes, Value/policy iteration, Approximate dynamic programming, Policy search]**
+
+&#10230;