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DEEP LEARNING
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Courses
MATEMATIKA DAN ILMU PENGETAHUAN ALAM (MIPA)
MATEMATIKA
Ilmu Komputer
14624533 - DEEP LEARNING
11. Application Deep Feedforward Network in TensorFlow
Rubrik Penilaian Penugasan 10
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Rubrik Penilaian Penugasan 10
Kriteria dan Bobot Penilaian:
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Eksplorasi Data (35%)
Prapemrosesan Data (35%)
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Rubrik Penilaian Penugasan 10 - Progress 1 Proyek Akhir.pdf
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◄ Kode Program Deep Feedforward
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Rencana Pembelajaran Semester (RPS)
Komposisi Penilaian & Evaluasi
Rencana Pembelajaran Semester (RPS)
Komposisi Penilaian & Evaluasi
1.1. Deep Learning Application 2025_1
1.2. Hubungan AI & Deep Learning 2025_1
1.3. Why Deep Learning 2025_1
Materi 1: Konsep Dasar Deep Learning 2025_1
Penugasan 1: Meringkas literatur Deep Learning
Rubrik Penilaian Penugasan 1
2.1.1. Perkalian Matriks & Vektor
2.1.2. Determinan
2.1.3. Nilai Eigen dan Vektor Eigen
Materi 2.1. Math For ML - Aljabar Linier
2.2.1. Random Variable
2.2.2. Probability Mass Function (PMF)
2.2.3. Statistika Marginal
2.2.4.Probability Density Function
2.2.5. Statistika Variansi dan Kovariansi
2.2.6. Gaussian Distribution
Materi 2.2. Math for ML - Probabilitas
2.3.1. Gradient Based Optimization
2.3.2.Jacobian Matrices
2.3.3. Hessian Matrices
Materi 2.3. Math For ML - Komputasi Numerik
2.4. Dasar Mesin Pemelajar
Materi 2.4. Dasar Mesin Pemelajar
Penugasan 2: Identifikasi permasalahan matematika dan machine learning 2025_1
Rubrik Penilaian Penugasan 2
3.1. Feedforward Neural Network
3.2. Backpropagation Algorithm
3.3. Minibatch
3.4. XOR Learning
Materi 3 : Deep Feedforward Networks
Rubrik Penilaian Penugasan 3
4.1. Data Splitting
4.2. Problem of Fitting
4.3. Parameter Norm Penalties
4.4. Data Augmentation
4.5. Early Stopping
4.6. Bagging
4.7. Dropout
Materi 4: Regularization For Deep Learning
Rubrik Penilaian Penugasan 4
5.1. NN as Computational Graph
5.2. Gradient Descent for NN
5.3. Optimization Algorithm
Materi 5: Optimization for Deep Learning
Rubrik penilaian penugasan 5
6.1. Aplikasi Visi Komputer
6.2. Apa yang komputer lihat
6.3. Mempelajari Fitur Visual melalui Jaringan Saraf
6.4.Feature Extraction Case Study
6.5.Convolutional Neural Network (CNN)
6.6. Non Linearity & Pooling
6.7. Arsitektur Berbagai Aplikasi
Materi 6: Deep Convolutional Networks
Rubrik Penilaian Penugasan 6
7.1. Introduction to Sequence Modelling
Materi 7: Deep Sequence Modelling
Rubrik Penugasan 7: Deep Sequence Modelling
9.1. Autoregressive Models
Materi 8: Deep Generative Modeling
Penugasan 8: Deep Generative Modeling
Rubrik Penilaian Penugasan 8
10. Practical Methodology - Konsep
Materi 10: Practical Methodology
Rubrik Penilaian Penugasan 9
Kode Program 10-Practical Methodology
11. Application Deep Feedforward Network in TensorFlow
Kode Program Deep Feedforward
Kode Program Telaah Data Terstruktur
Kode Program Visualisasi Data Terstruktur
12. Application Convolutional Neural Network in Tensor Flow
Kode Program Convolutional Neural Network
Rubrik Penilaian Tugas 11
13. Application Recurrent Neural Network (RNN) in Tensor Flow
Kode Program Recurrent Neural Network
Rubrik Penilaian Tugas 12
14. Application Deep Convolution Generative Adversarial Network (DC-GAN) in Tensor Flow
Kode Program Deep Convolutional Generative Adversarial Network
Rubrik Penilaian Tugas 13
Kode Program Telaah Data Terstruktur ►
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