Introduction to PyTorch with Engaging Projects

Name: Introduction to PyTorch with Engaging Projects
Rating: 4.5 (30 reviews)

Get Started with PyTorch: Build Your First AI Project

Created byAbdurrahman TEKIN

Last updated 6/2024

English

English [Auto],

What you'll learn

Fundamentals of PyTorch.
Building and training neural networks.
Implementing advanced deep learning architectures.
Evaluating model performance and making predictions.

Course content

1 section • 12 lectures • 1h 33m total length

1 Define Input and Output Data3:50
Train an AI with PyTorch to learn the linear function y = 2x + 1 using tensors for input and output data, weights, and biases, and test the results.
2 Understand the Lineer Module8:43
Understand the linear module in PyTorch, including weights, bias, and fully connected layers, with matrix multiplication and loss-based convergence toward correct outputs.
3 Create an Optimizer and Learn Math Behind It11:42
Learn how PyTorch optimizers, starting from random weights and biases, update parameters using stochastic gradient descent, with learning rate effects and the math of loss and gradient updates.
4 Forward pass4:52
Engage in the forward pass by computing y_hat from inputs X with a model's weight and bias, then observe how predictions evolve during training as these parameters are optimized.
5 Compute loss function3:51
Compute the loss function in PyTorch using mean squared error (MSE), averaging the squared differences between y_pred and y, illustrated with a numeric example.
6 Backward pass and optimization13:40
Compute the loss gradients via the backward pass, then update weight and bias with stochastic gradient descent and clear gradients with zero_grad, observing convergence toward optimal parameters.
7 Use ReLU (Rectified Linear Unit) activation function9:43
Build a PyTorch model with two hidden layers using ReLU activation, defined as a class, and train it with SGD to optimize weights and biases.
8 Add a dropout layer10:30
Explore increasing a linear layer's output, using dropout as a regularization technique to combat overfitting. See a four-layer network with linear, ReLU, and dropout, and a basic SGD training loop.
9 Number of the Weight and Bias of Lineer(1,10)9:52
Build a PyTorch model with a linear1 (1→10), a ReLU, and a linear2 (10→1). Train with SGD (lr 0.01) and MSE loss for 10,000 iterations, then inspect weights, biases, and predictions.
10 Mean squared error loss2:30
Learn to use mean squared error loss from PyTorch's functional package (import as F) by comparing predicted outputs with correct targets in a network with two linear layers and ReLU.
11 Save and Load the Model9:02
Learn to save and load a PyTorch model by saving its state dictionary, then reload it, switch to eval mode, and run predictions on new inputs.
12 Use 2 size input4:46
Train a two-input, one-output model to learn the input-output relation with a linear function in PyTorch, using a loss function and optimizer, then save, load, and test new inputs.

Requirements

Basic programming knowledge
Familiarity with fundamental concepts of machine learning.

Description

Are you ready to dive into the exciting world of PyTorch? Discover the power of deep learning and embark on an incredible journey with our course, "Introduction to PyTorch with Engaging Projects."

PyTorch has revolutionized the field of artificial intelligence, enabling researchers and developers to build cutting-edge models with ease. Whether you are a beginner or an experienced coder, this course is designed to unlock your potential and equip you with the skills to create remarkable projects.

Through a series of hands-on projects, you will unleash the true potential of PyTorch. From building your first neural network to implementing advanced architectures, you will gain a solid foundation in deep learning principles. Our expert instructors will guide you every step of the way, providing clear explanations and practical examples that bring concepts to life.

But it doesn't stop there! This course goes beyond theory, ensuring an engaging and immersive learning experience. Get ready to tackle real-world challenges and build exciting projects that showcase your newfound knowledge. Develop image recognition systems, natural language processing models, and even delve into the fascinating realm of generative adversarial networks (GANs).

What sets this course apart is its emphasis on practicality. You'll work on captivating projects that mirror real-world scenarios, giving you the confidence to apply your skills to industry challenges. Gain hands-on experience with data preprocessing, model training, and performance evaluation, equipping you with the tools to create impactful solutions.

Join a vibrant community of learners, collaborate with fellow students, and tap into a wealth of resources that will accelerate your learning journey. Our course brings together a supportive network of like-minded individuals who share a passion for PyTorch and are eager to push the boundaries of what's possible.

Are you ready to take the first step towards mastering PyTorch? Enroll in "Introduction to PyTorch with Engaging Projects" today and unlock the world of deep learning innovation. Unleash your creativity, build awe-inspiring models, and make your mark in the exciting field of artificial intelligence.

Who this course is for:

Beginners who want to learn PyTorch and dive into the world of deep learning.
Students and researchers interested in expanding their knowledge of artificial intelligence.
Programmers and developers looking to enhance their skills in deep learning frameworks.