Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch with linear layers.

Introduction to deep learning with PyTorch

Getting started with PyTorch tensors

Checking and adding tensors

Neural networks and layers

Linear layer network

Understanding weights

Hidden layers and parameters

Your first neural network

Stacking linear layers

Counting the number of parameters

Introduction to PyTorch, a Deep Learning Library

To train a neural network in PyTorch, you will first need to understand additional components, such as activation and loss functions. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters.


Mengenal fungsi aktivasi

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Fungsi sigmoid dan softmax

Menjalankan forward pass

Membangun pengklasifikasi biner di PyTorch

Dari regresi ke klasifikasi multi-kelas

Menggunakan fungsi loss untuk menilai prediksi model

Membuat label one-hot encoded

Menghitung cross-entropy loss

Menggunakan turunan untuk memperbarui parameter model

Mengakses parameter model

Memperbarui bobot secara manual

Menggunakan optimizer PyTorch

Neural Network Architecture and Hyperparameters

Now that you've learned the key components of a neural network, you'll train one using a training loop. You'll explore potential issues like vanishing gradients and learn strategies to address them, such as alternative activation functions and tuning learning rate and momentum.

A deeper dive into loading data

Using TensorDataset

Using DataLoader

Writing our first training loop

Using the MSELoss

Writing a training loop

ReLU activation functions

Implementing ReLU

Implementing leaky ReLU

Understanding activation functions

Learning rate and momentum

Experimenting with learning rate

Experimenting with momentum

Training a Neural Network with PyTorch

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting.


Layer initialization and transfer learning

Fine-tuning process

Freeze layers of a model

Layer initialization

Evaluating model performance

Writing the evaluation loop

Calculating accuracy using torchmetrics

Fighting overfitting

Experimenting with dropout

Understanding overfitting

Improving model performance

Implementing random search

Wrap-up video

Evaluating and Improving Models

Water Potability

Face Mask Dataset

Course Glossary

Deep learning ada di mana-mana, dari kamera ponsel hingga asisten suara dan mobil swakemudi. Dalam kursus ini, Anda akan mempelajari teknologi yang kuat ini dan cara memanfaatkannya menggunakan PyTorch, salah satu pustaka deep learning paling populer. Di akhir kursus, Anda akan mampu menggunakan PyTorch untuk menyelesaikan masalah klasifikasi dan regresi dengan deep learning.

Video dilengkapi transkrip langsung yang dapat Anda tampilkan dengan mengklik "Show transcript" di kiri bawah video.

Glosarium kursus dapat ditemukan di sisi kanan pada bagian sumber daya.

Untuk memperoleh kredit CPE, Anda perlu menyelesaikan kursus dan mencapai skor 70% pada penilaian yang memenuhi syarat. Anda dapat membuka penilaian dengan mengklik pemberitahuan kredit CPE di sisi kanan.

Supervised Learning with scikit-learn

Introduction to NumPy

Python Toolbox

Pelajari cara membangun jaringan saraf, atur hiperparameter, dan selesaikan masalah di PyTorch.

Pengantar Deep Learning dengan PyTorch

Insinyur AI Asisten untuk Ilmuwan Data

Pembelajaran Mendalam dalam Python

Dasar-Dasar Pembelajaran Mesin dalam Python

Ilmuwan Pembelajaran Mesin dalam Python

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Menggunakan loss function untuk menilai prediksi model

Mengapa kita perlu loss function?

Konsep one-hot encoding

Transformasi label dengan one-hot encoding

Cross entropy loss di PyTorch

Menggabungkan semuanya

Ayo berlatih!

Neural Network Architecture and Hyperparameters - Menggunakan fungsi loss untuk menilai prediksi model

Menggunakan fungsi loss untuk menilai prediksi model

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