Modern Deep Convolutional Neural Networks with PyTorch

Modern Deep Convolutional Neural Networks with PyTorch, available at Free, has an average rating of 4.05, with 29 lectures, based on 122 reviews, and has 7598 subscribers.

You will learn about Convolutional Neural Networks Image Processing Advance Deep Learning Techniques Regularization, Normalization Transfer Learning This course is ideal for individuals who are Who knows a bit about neural networks or Who wants to enrich their Deep Learning and Image Processing knowledge or Who wants to study advanced techniques and practices It is particularly useful for Who knows a bit about neural networks or Who wants to enrich their Deep Learning and Image Processing knowledge or Who wants to study advanced techniques and practices.

Enroll now: Modern Deep Convolutional Neural Networks with PyTorch

Summary

Title: Modern Deep Convolutional Neural Networks with PyTorch

Price: Free

Average Rating: 4.05

Number of Lectures: 29

Number of Published Lectures: 29

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Convolutional Neural Networks

Image Processing

Advance Deep Learning Techniques

Regularization, Normalization

Transfer Learning

Who Should Attend

Who knows a bit about neural networks

Who wants to enrich their Deep Learning and Image Processing knowledge

Who wants to study advanced techniques and practices

Target Audiences

Who knows a bit about neural networks

Who wants to enrich their Deep Learning and Image Processing knowledge

Who wants to study advanced techniques and practices

Dear friend, welcome to the course “Modern Deep Convolutional Neural Networks”! I tried to do my best in order to share my practical experience in Deep Learning and Computer vision with you.

The course consists of 4 blocks:

1. Introduction section, where I remind you, what is Linear layers, SGD, and how to train Deep Networks.

2. Convolution section, where we discuss convolutions, it’s parameters, advantages and disadvantages.

3. Regularization and normalization section, where I share with you useful tips and tricks in Deep Learning.

4. Fine tuning, transfer learning, modern datasets and architectures

If you don’t understand something, feel free to ask equations. I will answer you directly or will make a video explanation.

Prerequisites:

Matrix calculus, Linear Algebra, Probability theory and Statistics

Basics of Machine Learning: Regularization, Linear Regression and Classification,

Basics of Deep Learning: Linear layers, SGD,? Multi-layer perceptron

Python, Basics of PyTorch

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Computer Vision Problems

Lecture 3: Linear Layer and Classification Pipeline

Lecture 4: Loss functions and Softmax

Lecture 5: Stochastic Gradient Descend

Lecture 6: PRACTICE #1: Data loading

Lecture 7: PRACTICE #2: Linear Classifier in PyTorch (part 1)

Lecture 8: PRACTICE #3: Linear Classifier in PyTorch (part 2)

Lecture 9: PRACTICE #4: Multi-layer perceptron

Chapter 2: Convolutional Neural Networks

Lecture 1: What is image

Lecture 2: Motivation to Convolutions

Lecture 3: Convolution operation

Lecture 4: Parameters of the convolution

Lecture 5: Non-linear function

Lecture 6: Max Pooling and Average Pooling

Lecture 7: Building deep convolutional network

Lecture 8: PRACTICE #5: Convolutional Neural Network

Chapter 3: Regularization and Normalization

Lecture 1: Overfitting. L2 regularization

Lecture 2: DropOut regularization. DropConnect regularization

Lecture 3: DropBlock regularization

Lecture 4: Early Stopping regularization

Lecture 5: Batch Normalization

Chapter 4: Improving the quality

Lecture 1: Data Augmentation

Lecture 2: Existing datasets

Lecture 3: Modern Architectures

Lecture 4: Transfer Learning

Chapter 5: Boat Recognition Project

Lecture 1: Data Loading

Lecture 2: Data Augmentation

Lecture 3: Transfer Learning: ResNet-18

Instructors

Denis Volkhonskiy
AI Researcher

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 30 votes

4 stars: 48 votes

5 stars: 39 votes

Frequently Asked Questions

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