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Deep Learning with Pytorch

Name: Deep Learning with PyTorch: Build & Deploy Neural Networks
Price: 36 USD

with Gaurav Sarkar

Create State of the Art Neural Networks for Deep Learning with Meta's PyTorch Deep Learning Library!

4.9

808 reviews on

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301 students already have enrolled

Skill level:

Advanced

Duration:

2 hours

Lessons (2 hours)

CPE credits:

2.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited:

certificate

What You Learn

Build your first neural network using PyTorch.
Perform regression tasks with PyTorch models.
Implement classification models in PyTorch.
Evaluate model performance effectively.
Learn how to use Kaggle for projects.

Topics & tools

deep learningpytorchneural networkspythonkagglemachine and deep learning

Your instructor

Gaurav Sarkar

AI Solution Engineer at Intel | Hugging Face Fellow | Kaggle Expert

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Computer Science

Dayananda Sagar College of Engineering, BANGALORE

Ask Gaurav a question

Course Overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 2.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

Ready to master deep learning with PyTorch? This course takes you from foundational concepts to hands-on neural network deployment, designed to provide you with practical skills and real-world experience.

Dive into the world of tensors, understand dimensions, notation, and tensor construction. Seamlessly integrate PyTorch with NumPy and leverage common PyTorch functions. Master GPU acceleration to dramatically enhance computational performance. Explore automatic differentiation and PyTorch’s powerful autograd system. Build efficient data pipelines and construct your first neural networks. Implement essential components like loss functions, training loops, and evaluation metrics with hands-on coding exercises.

Tackle real-world regression and classification problems. Learn debugging techniques and best practices for developing reliable models.

Get introduced to Kaggle, the global playground for data science. Step-by-step guidance to participate effectively in regression and classification competitions.

Solidify your skills and prepare to confidently apply deep learning to practical challenges.

Prerequisites

Python (version 3.8 or later), PyTorch library, and a code editor or IDE (e.g., Jupyter Notebook, VS Code, or Google Colab)
Intermediate Python and machine learning knowledge is required.
Familiarity with NumPy and neural network fundamentals is recommended.

Advanced preparation

Curriculum

41 lessons 18 exercises 1 exam

Free preview

1.1 Meet your instructor

1 min

1.2 Why PyTorch for Deep Learning

2 min

1.3 Learning Objectives

2 min

1.4 PyTorch vs. TensorFlow

4 min

2.1 Deep Learning as a Concept

3 min

3.1 PyTorch: World of Tensors

3 min

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Saquib Rahman K.

After 365: Data Scientist | EXL

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4.9

Based on 808 reviews

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