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# Terjemahan Bahasa Indonesia | ||
Ini adalah transliterasi catatan ringkas materi pembelajaran [Machine learning](https://stanford.edu/~shervine/teaching/cs-229/) dan [Deep Learning](https://github.com/afshinea/stanford-cs-230-deep-learning) dari [Shervine Amidi](https://stanford.edu/~shervine/). | ||
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## Semoga bermanfaat. |
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**1. Deep Learning cheatsheet** | ||
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⟶ **1. Catatan ringkas Deep Learning** | ||
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<br> | ||
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**2. Neural Networks** | ||
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⟶ **2. Neural Networks** | ||
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<br> | ||
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**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.** | ||
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⟶ **3. Neural networks merupakan sebuah kelas model yang disusun atas beberapa layer. Jenis umum dari neural networks yang umum digunakan adalah convolutional (CNN) dan recurrent neural networks (RNN).** | ||
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<br> | ||
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**4. Architecture ― The vocabulary around neural networks architectures is described in the figure below:** | ||
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⟶ **4. Arsitektur - Beberapa istilah yang umum digunakan dalam arsitektur neural network dijelaskan pada gambar di bawah ini** | ||
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**5. [Input layer, hidden layer, output layer]** | ||
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⟶ **5. [Input layer, hidden layer, output layer]** | ||
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<br> | ||
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**6. By noting i the ith layer of the network and j the jth hidden unit of the layer, we have:** | ||
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⟶ **6. Dengan i adalah layer ke-i dari network dan j adalah unit hidden layer ke-j, maka:** | ||
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<br> | ||
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**7. where we note w, b, z the weight, bias and output respectively.** | ||
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⟶ **7. Catatan: w, b, z adalah weight, bias, dan output.** | ||
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<br> | ||
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**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:** | ||
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⟶ **8. Fungsi aktivasi - Fungsi aktivasi di unit hidden terakhir berfungsi untuk menunjukkan kompleksitas non-linear terhadap model. Beberapa yang umum digunakan:** | ||
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<br> | ||
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**9. [Sigmoid, Tanh, ReLU, Leaky ReLU]** | ||
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⟶ **9. [Sigmoid, Tanh, ReLU, Leaky ReLU]** | ||
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<br> | ||
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**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:** | ||
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⟶**10. Cross-entroy loss - Dalam konteks neural networks, cross-entroy loss L(z,y) sangat umum digunakan untuk mendefinisikan:** | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Cross-entropy loss - Dalam konteks jaringan neural, cross-entropy loss L(z,y) umumnya digunakan dan didefinisikan sebagai berikut: |
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<br> | ||
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**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.** | ||
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⟶**11. Learning rate - Learning rate (Tingkat pembelajaran), sering dinotasikan sebagai α atau η, merupakan fase pembaruan pembobotan. Tingkat pembelajaran dapat diperbaiki atau diubah secara adaptif. Metode yang paling populer saat ini disebut Adam, yang merupakan metode yang dapat menyesuaikan tingkat pembelajaran.** | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Learning rate- Learning rate, sering dinotasikan sebagai α atau η, mendefinisikan seberapa cepat nilai weight diperbaharui. Learning rate bisa diset dengan nilai fix atau dirubah secara adaptif. Metode yang paling terkenal saat ini adalah Adam, sebuah method yang merubah learning rate secara adaptif. |
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<br> | ||
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**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:** | ||
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⟶**12. Backpropagation - Backpropagation adalah metode untuk memperbarui bobot dalam neural networks dengan memperhitungkan output aktual dan output yang diinginkan. Bobot w dihitung dengan menggunakan aturan rantai turunan dalam bentuk berikut:** | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Backprogation - Backprogation adalah sebuah metode untuk memperbarui nilai weights pada jaringan neural dengan memperhitungkan keluaran riil dan keluaran yang dikehendaki. Turunan yang berhubungan dengan nilai weight w dihitung dengan menggunakan kaidah rantai dan berbentuk sebagai berikut: |
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**13. As a result, the weight is updated as follows:** | ||
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⟶ **13. Sebagai hasilnya, nilai bobot diperbaharui sebagai berikut: | ||
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**14. Updating weights ― In a neural network, weights are updated as follows:** | ||
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⟶**14. Memperbaharui bobot w - Dalam neural network, bobot w diperbarui nilainya dengan cara berikut:** | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Memperbaharui nilai weights - Dalam neural network, nilai weights diperbaharui nilainya dengan cara berikut: Menurut saya weights tidak usah diterjemahkan, karena merupakan kosakata teknis pada neural network |
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**15. Step 1: Take a batch of training data.** | ||
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⟶**15. Langkah 1: Mengambil jumlah batch dari data latih.** | ||
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<br> | ||
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**16. Step 2: Perform forward propagation to obtain the corresponding loss.** | ||
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⟶**16. Langkah 2: Melakukan forward propagation untuk mendapatkan nilai loss yang sesuai. ** | ||
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<br> | ||
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**17. Step 3: Backpropagate the loss to get the gradients.** | ||
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⟶ **17. Langkah 3: Melakukan backpropagate terhadap loss untuk mendapatkan gradient.** | ||
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**18. Step 4: Use the gradients to update the weights of the network.** | ||
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⟶**18. Langkah 4: Menggunakan gradient untuk untuk memperbarui nilai dari network.** | ||
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<br> | ||
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**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** | ||
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⟶**19. Dropout - Dropout adalah teknik untuk mencegah overfitting data latih dengan menghilangkan satu atau lebih unit layer dalam neural network. Pada praktiknya, neurons melakukan drop dengan probabilitas p atau tidak melakukannya dengan probabilitas 1-p** | ||
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<br> | ||
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**20. Convolutional Neural Networks** | ||
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⟶ **20. Convolutional Neural Networks** | ||
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<br> | ||
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**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:** | ||
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⟶ **21. Kebutuhan layer convolutional - W adalah ukuran volume input, F adalah ukuran dari layer neuron convolutional, P adalah jumlah zero padding, maka jumlah neurons N yang dapat dibentuk dari volume yang diberikan adalah: ** | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. maka jumlah neuron N yang sesuai dengan ukuran dimensi masukan adalah: |
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**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:** | ||
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⟶ **22. Batch normalization - Adalah salah satu step hyperparameter γ,β yang menormalisasikan batch {xi}. Dengan notasi μB,σ2B adalah rata-rata dan variansi nilai yang digunakan untuk perbaikan dalam batch, dapat diselesaikan sebagai berikut:** | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Dengan mendefinisikan μB,σ2B sebagai nilai rata-rata dan variansi dari batch yang ingin kita normalisasi, hal tersebut dapat dilakukan dengan cara: |
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**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.** | ||
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⟶ **23. Biasanya dilakukan setelah layer sepenuhnya terhubung / konvolusional dan sebelum layer non-linearitas, yang bertujuan untuk peningkatan tingkat pembelajaran yang lebih tinggi dan mengurangi ketergantungan yang kuat pada inisialisasi.** | ||
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<br> | ||
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**24. Recurrent Neural Networks** | ||
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**25. Types of gates ― Here are the different types of gates that we encounter in a typical recurrent neural network:** | ||
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**26. [Input gate, forget gate, gate, output gate]** | ||
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**27. [Write to cell or not?, Erase a cell or not?, How much to write to cell?, How much to reveal cell?]** | ||
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**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.** | ||
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**29. Reinforcement Learning and Control** | ||
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**30. The goal of reinforcement learning is for an agent to learn how to evolve in an environment.** | ||
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**31. Definitions** | ||
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**32. Markov decision processes ― A Markov decision process (MDP) is a 5-tuple (S,A,{Psa},γ,R) where:** | ||
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**33. S is the set of states** | ||
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**34. A is the set of actions** | ||
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**35. {Psa} are the state transition probabilities for s∈S and a∈A** | ||
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**36. γ∈[0,1[ is the discount factor** | ||
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**37. R:S×A⟶R or R:S⟶R is the reward function that the algorithm wants to maximize** | ||
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**38. Policy ― A policy π is a function π:S⟶A that maps states to actions.** | ||
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**39. Remark: we say that we execute a given policy π if given a state s we take the action a=π(s).** | ||
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**40. Value function ― For a given policy π and a given state s, we define the value function Vπ as follows:** | ||
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**41. Bellman equation ― The optimal Bellman equations characterizes the value function Vπ∗ of the optimal policy π∗:** | ||
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**42. Remark: we note that the optimal policy π∗ for a given state s is such that:** | ||
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**43. Value iteration algorithm ― The value iteration algorithm is in two steps:** | ||
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**44. 1) We initialize the value:** | ||
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**45. 2) We iterate the value based on the values before:** | ||
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**46. Maximum likelihood estimate ― The maximum likelihood estimates for the state transition probabilities are as follows:** | ||
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**47. times took action a in state s and got to s′** | ||
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**48. times took action a in state s** | ||
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**49. Q-learning ― Q-learning is a model-free estimation of Q, which is done as follows:** | ||
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**50. View PDF version on GitHub** | ||
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**51. [Neural Networks, Architecture, Activation function, Backpropagation, Dropout]** | ||
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**52. [Convolutional Neural Networks, Convolutional layer, Batch normalization]** | ||
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**53. [Recurrent Neural Networks, Gates, LSTM]** | ||
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**54. [Reinforcement learning, Markov decision processes, Value/policy iteration, Approximate dynamic programming, Policy search]** | ||
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Fungsi aktivasi - Fungsi aktivasi digunakan oleh unit tersembunyi untuk menunjukkan kompleksitas non-linear terhadap model. Berikut beberapa model yang umum digunakan: