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Start Your Free Trial Today >>Master PyTorch: From Fundamentals to Advanced Deep Learning Architectures
Are you ready to transform your understanding of Deep Learning and master PyTorch from the ground up? "PyTorch: From Zero to Hero" is your comprehensive guide to building, training, and optimizing deep learning models using one of the most popular frameworks in the AI industry. This course is designed for learners at any stage, whether youāre just starting out or looking to solidify and expand your skills in PyTorch.
Course Overview:
In this course, youāll start by mastering the fundamentals of PyTorch and progress to designing sophisticated neural networks, including CNNs and RNNs. Through hands-on projects and real-world case studies, you will develop the skills needed to apply deep learning to diverse applications like computer vision and natural language processing. By the end of the course, you will be equipped to implement, fine-tune, and deploy PyTorch models efficiently in practical scenarios.
What You Will Learn:
PyTorch Fundamentals:
Introduction to PyTorch and Deep Learning: Get an overview of deep learning concepts and set up your development environment with PyTorch.
Working with Tensors: Learn to create, manipulate, and perform operations on tensors, the building blocks of deep learning models.
Building Neural Networks: Understand the components of neural networks and start building your first model with PyTorch.
Architectures in PyTorch:
Neural Network Architectures: Explore various deep learning architectures, with a focus on their use cases and advantages.
Computer Vision with PyTorch: Learn to preprocess image datasets and implement Convolutional Neural Networks (CNNs) for image classification.
Advanced CNN Techniques: Enhance your CNN models with data augmentation and regularization methods to improve performance.
Sequence Data Processing and RNNs: Dive into sequence data and build Recurrent Neural Networks (RNNs) and LSTM networks for tasks like language modeling.
Advanced Topics in PyTorch:
Transfer Learning and Fine-Tuning: Leverage pre-trained models and fine-tune them for specific tasks to accelerate your model development process.
Custom Layers and Modules: Design and integrate custom layers and modules into your models for specialized tasks.
Scalable Training and Hyperparameter Tuning: Implement distributed training techniques and fine-tune hyperparameters to optimize model performance on large datasets.
Real-World Application:
Case Study: Develop a PyTorch model to analyze and classify customer feedback into positive, neutral, or negative sentiments, using real-world datasets.
End-to-End Project: Walk through the creation of a complete PyTorch project, from dataset preparation to model evaluation, and apply best practices in model development and deployment.
Why Choose "PyTorch: From Zero to Hero"?
Comprehensive Learning Path: Start from the basics and progress to advanced topics, ensuring a deep and thorough understanding of PyTorch.
Practical, Hands-On Experience: Engage in real-world projects and case studies that demonstrate the application of deep learning in various fields.
Expert Guidance: Learn from seasoned professionals with extensive experience in AI and deep learning.
Flexibility: Whether youāre a beginner or an experienced practitioner, this course adapts to your learning pace and goals.
Community and Support: Join a vibrant community of learners and receive support to overcome challenges and accelerate your learning journey.
Take the Leap with PyTorch!
This course isnāt just about learning PyTorch; itās about mastering deep learning and empowering you to tackle complex challenges in AI with confidence.
Enroll in "PyTorch: From Zero to Hero" today and start your journey towards becoming a PyTorch expert.
$35
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
$157
/ MonthAccelerate your Career and boost your Earning Potential with our Expert Instructors & Community! What Youāll Unlock: - š 40+ Courses (over 200 hours!) - š 17 Specialized Career Paths - š Quizzes and Practice Activities - š Certificates for Courses & Career Paths - š Prizes for Learning - š§Ŗ Weekly Live Labs (plus recordings!) - šÆ Monthly Missions to practice even more - š¼ Monthly Career Booster Events - š¬ Full access to the SDS Community - ā” Monthly Speed Networking - š„ Monthly Resume Clinics - š„ Group Mentorship Program Plus, Pro Plan perks just for you: - š A Personalized Career Path built around your goals - š§āš« 1-on-1 Mentoring Sessions every month - āļø Personalized Resume Reviews "In just a few months of learning at SDS, I landed a Data Analyst job!" ā Sanaz Afshar, California
Course content
What is PyTorch?
07:57
How to Install PyTorch?
07:16
Tensors: Scalars
06:52
Tensors: Matrices
04:36
Checking Tensor's Shape and Size
06:07
Operations with Tensors - Part 1
06:51
Operations with Tensors - Part 2
06:11
Implementing the First Model in PyTorch - Linear Regression - Part 1
08:03
Implementing the First Model in PyTorch - Linear Regression - Part 2
07:41
Loss Functions and Optimizers
06:52
What Is "epochs"?
03:59
Training Loop for PyTorch Models
06:44
Boston Housing Price Prediction: Regression Dataset Processing
05:38
Implementing FullyConnected Neural Network (FCNN) in PyTorch
05:18
What Are Non-linear Functions and Why Do We Need Them?
03:35
Let's Put Everything Together
05:45
Loading Datasets with PyTorch
06:28
Image Dataset Preprocessing
05:53
PyTorch DataLoader - Part 1
04:54
PyTorch DataLoader - Part 2
05:36
Introduction to Convolutional Neural Networks (CNNs)
08:01
Implementing CNNs with PyTorch
06:10
Training Our First CNN
06:28
Improving Our CNN Model with the MaxPooling Layer - Part 1
06:59
Improving Our CNN Model with the MaxPooling Layer - Part 2
04:04
What is Image Augmentation and How to Perform It Using PyTorch?
06:57
Improving CNN Models Using an Augmented Dataset
07:50
Working with Textual Data Using Torch-Text
05:57
CustomDataset Object in PyTorch
06:49
Implementing Recurrent Neural Network (RNN) - Part 1
04:18
Implementing Recurrent Neural Network (RNN) - Part 2
05:44
LSTM Neural Network in PyTorch
06:48
What is Transfer Learning?
06:29
How to Perform Transfer Learning in PyTorch? - Part 1
05:22
How to Perform Transfer Learning in PyTorch? - Part 2
08:01
What is Fine-Tuning and How To Do It?
09:39
Creating Custom Layer for Your Models
06:13
How to Train Neural Networks on Multiple GPUs?
09:25
Hyperparameters Tuning for Deep Learning Using RayTunner - Part 1
06:00
Hyperparameters Tuning for Deep Learning Using RayTunner - Part 2
06:26
COCO Dataset Preparation
08:08
COCO Dataset Preparation - Solution
04:31
Creating Captions Encoders
05:48
Creating Advanced DatasetLoader for Text and Images
05:51
Coding CNNEncoder Neural Network
05:28
Coding RNNDecoder Neural Network
09:02
Training Our Image Captioning System and Making First Predictions
05:26