Hands-On Deep Learning With Pytorch: A Beginner'S Course

This course is designed for beginners with no experience in Deep learning or PyTorch.

What you'll learn

Train Convolutional Neural Networks.

How to apply data transformations using the torchvision library.

How to efficiently store and load data samples on PyTorch.

How to leverage GPU acceleration to train neural networks efficiently

Overall the student will build a solid foundation in the fundamental concepts and techniques required to train neural networks effectively

Requirements

As long as you have a basic understanding of Python, you're all set to dive into the world of Deep learning.

Description

Discover the power of deep learning with our beginner's course, "Hands-On Deep Learning with PyTorch." Whether you're new to neural networks or looking to expand your skills, this course will provide you with a hands-on approach to training neural networks from scratch.Our comprehensive curriculum covers all the essential components of deep learning, including neural networks, loss functions, optimizers, datasets, and data loaders. You'll also learn how to leverage the GPU for accelerated training and gain practical insights into building and training basic neural networks using PyTorch.What sets this course apart is its accessibility. You don't need any previous knowledge of neural networks or PyTorch. All you need is a basic understanding of Python, and we'll guide you through the rest.By the end of the course, you'll have gained the skills to confidently train basic neural networks using PyTorch. Unlock your potential in deep learning and embark on this exciting journey today. Enroll now and start building your expertise in the world of artificial intelligence.Content of the Course:DatasetsData Loaders.Image AugmentationLoss FunctionsOptimizers.Activation FunctionsNormalization TechniquesConvolutional Neural Networks (CNN)Training Neural NetworksGPU AccelerationRequirements:The only requirement is basic knowledge of Python.No experience on Deep learning requiredNo experience on PyTorch required,

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Theory: Data Formats.

Lecture 2 Float Tensor

Lecture 3 Long Tensor

Lecture 4 Bool Tensor

Lecture 5 No_grad Context Manager

Lecture 6 torchvision: Compose Object (Theory)

Lecture 7 torchvision: Compose Object (Example)

Section 3: Theory: Datasets and DataLoaders.

Lecture 8 PyTorch Dataset (Theory)

Lecture 9 PyTorch Dataset (Example)

Lecture 10 PyTorch DataLoader (Theory)

Lecture 11 PyTorch DataLoader (Example)

Section 4: Theory: Model components.

Lecture 12 Linear Layer

Lecture 13 Convolutional Operation (Theory)

Lecture 14 Convolutional Operation (Example)

Lecture 15 Activation Functions

Lecture 16 Softmax Normalization Function

Lecture 17 Argmax Function

Lecture 18 How to create a CNN.

Lecture 19 Neural Network Evaluation Mode

Section 5: Theory: CUDA

Lecture 20 What's CUDA

Lecture 21 CUDA Example.

Section 6: Theory: Optimization Components.

Lecture 22 What's a Loss Function

Lecture 23 Cross Entropy Loss (Theory)

Lecture 24 Cross Entropy Loss (Example)

Lecture 25 What's an Optimizer

Lecture 26 What's a Learning Rate

Lecture 27 How to initiate Adam (Example)

Section 7: Theory: How to Train a Neural Network.

Lecture 28 How to Train a Neural Network (Example)

Section 8: Practice: Training a CNN

Lecture 29 Gather Data.

Lecture 30 Build Dataset

Lecture 31 Build the Neural Network

Lecture 32 Training the Neural Network

Section 9: Farewell and Assignment.

Lecture 33 Farewell

This course is designed for beginners who are interested in deep learning but lack the theoretical/technical background.,Beginners that feel overwhelmed with the massive influx of information around and want a streamlined path to build a solid foundation on deep learning.