PyTorch for Deep Learning Bootcamp: Zero to Mastery

PyTorch for Deep Learning Bootcamp: Zero to Mastery, available at $59.99, has an average rating of 4.68, with 41 lectures, based on 79 reviews, and has 659 subscribers.

You will learn about Understand the basic concepts about neural network and how it works Use PyTorch for Linear Regression using Multilayer Perceptron (MLP) Use PyTorch for image classification using Deep Artificial Neural Network (ANN) Learn how to work with different data types such as tensors and arrays Use PyTorch for image classification using Convolutional Neural Network (CNN) Use PyTorch for time series prediction using Recurrent Neural Network (RNN) This course is ideal for individuals who are beginner to advance python developers, data analysts, engineers and overall data science enthusiast want to learn about deep learning with PyTorch It is particularly useful for beginner to advance python developers, data analysts, engineers and overall data science enthusiast want to learn about deep learning with PyTorch.

Enroll now: PyTorch for Deep Learning Bootcamp: Zero to Mastery

Summary

Title: PyTorch for Deep Learning Bootcamp: Zero to Mastery

Price: $59.99

Average Rating: 4.68

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the basic concepts about neural network and how it works
Use PyTorch for Linear Regression using Multilayer Perceptron (MLP)
Use PyTorch for image classification using Deep Artificial Neural Network (ANN)
Learn how to work with different data types such as tensors and arrays
Use PyTorch for image classification using Convolutional Neural Network (CNN)
Use PyTorch for time series prediction using Recurrent Neural Network (RNN)

Who Should Attend

beginner to advance python developers, data analysts, engineers and overall data science enthusiast want to learn about deep learning with PyTorch

Target Audiences

beginner to advance python developers, data analysts, engineers and overall data science enthusiast want to learn about deep learning with PyTorch

Deep learning has become one of the most popular machine learning techniques in recent years, and PyTorch has emerged as a powerful and flexible tool for building deep learning models. In this course, you will learn the fundamentals of deep learning and how to implement neural networks using PyTorch.

Through a combination of lectures, hands-on coding sessions, and projects, you will gain a deep understanding of the theory behind deep learning techniques such as deep Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs). You will also learn how to train and evaluate these models using PyTorch, and how to optimize them using techniques such as stochastic gradient descent and backpropagation. During the course, I will also show you how you can use GPU instead of CPU and increase the performance of the deep learning calculation.

In this course, I will teach you everything you need to start deep learning with PyTorch such as:

NumPy Crash Course
Pandas Crash Course
Neural Network Theory and Intuition
How to Work with Torchvision datasets
Convolutional Neural Network (CNN)
Long-Short Term Memory (LSTM)
and much more

Since this course is designed for all levels (from beginner to advanced), we start with basic concepts and preliminary intuitions.

By the end of this course, you will have a strong foundation in deep learning with PyTorch and be able to apply these techniques to various real-world problems, such as image classification, time series analysis, and even creating your own deep learning applications.

Course Curriculum

Chapter 1: Course Introduction & Overview

Lecture 1: Course Content

Lecture 2: Why Google Colab?

Lecture 3: Introduction to Colab Environment

Chapter 2: Useful Packages

Lecture 1: NumPy Basics

Lecture 2: Pandas Basics

Chapter 3: PyTorch Tensor Basics

Lecture 1: Introduction to Tensors

Lecture 2: Working with PyTorch Tensors

Chapter 4: Neural Network Basic Concepts

Lecture 1: Basic Terms About NN

Lecture 2: Activation Function

Lecture 3: How Neural Network Learn?

Lecture 4: Gradient Decent Optimization

Chapter 5: PyTorch for Multilayer Perceptron (MLP)

Lecture 1: PyTorch Regression Using MLP – Part1

Lecture 2: PyTorch Regression Using MLP – Part2

Lecture 3: PyTorch Regression Using MLP – Part3

Lecture 4: PyTorch Regression Using MLP – Part4

Chapter 6: PyTorch for Deep Artificial Neural Network (ANN)

Lecture 1: Deep Artificial Neural Network Introduction

Lecture 2: PyTorch Image Classification Using ANN – Part1

Lecture 3: PyTorch Image Classification Using ANN – Part2

Lecture 4: PyTorch Image Classification Using ANN – Part3

Lecture 5: PyTorch Image Classification Using ANN – Part4

Chapter 7: PyTorch for Convolutional Neural Network (CNN)

Lecture 1: Introduction

Lecture 2: Convolutional Layer (Image Filter)

Lecture 3: Pooling Layer

Lecture 4: Flattening

Lecture 5: Conclusion

Lecture 6: PyTorch Image Classification Using CNN – Part1

Lecture 7: PyTorch Image Classification Using CNN – Part2

Lecture 8: PyTorch Image Classification Using CNN – Part3

Lecture 9: PyTorch Image Classification Using CNN – Part4

Chapter 8: Using GPU Instead of CPU

Lecture 1: Introduction to CPU & GPU

Lecture 2: Watch if You Don't Use Colab!

Lecture 3: How to Use GPU?

Lecture 4: Important Note!

Lecture 5: How to Save & Load a Model Using PyTorch

Chapter 9: PyTorch for Recurrent Neural Network

Lecture 1: Introduction to Recurrent Neural Network

Lecture 2: What is LSTM and How it Works?

Lecture 3: PyTorch for Time Series Forecasting Using LSTM-Part1

Lecture 4: PyTorch for Time Series Forecasting Using LSTM-Part2

Lecture 5: PyTorch for Time Series Forecasting Using LSTM-Part3

Lecture 6: PyTorch for Time Series Forecasting Using LSTM-Part4

Chapter 10: Bonus!

Lecture 1: Bonus Lecture

Instructors

Navid Shirzadi, Ph.D.
Data Science & Optimization Expert

Rating Distribution

1 stars: 2 votes
2 stars: 0 votes
3 stars: 5 votes
4 stars: 16 votes
5 stars: 58 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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