Hands-On Deep Learning on PyTorch

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Emanuel Riquelme

1:11:47

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1. Introduction

1. Introduction.mp4

01:01

2. Theory Data Formats

1. Float Tensor.mp4

00:51

2. Long Tensor.mp4

00:50

3. Bool Tensor.mp4

00:40

4. No grad Context Manager.mp4

02:31

5. torchvision Compose Object (Theory).mp4

00:40

6. torchvision Compose Object (Example).mp4

02:01

3. Theory Datasets and DataLoaders

1. PyTorch Dataset (Theory).mp4

02:27

2. PyTorch Dataset (Example).mp4

04:16

3. PyTorch DataLoader (Theory).mp4

00:41

4. PyTorch DataLoader (Example).mp4

01:14

4. Theory Model components

1. Linear Layer.mp4

02:42

2. Convolutional Operation (Theory).mp4

02:19

3. Convolutional Operation (Example).mp4

02:12

4. Activation Functions.mp4

01:22

5. Softmax Normalization Function.mp4

02:33

6. Argmax Function.mp4

01:01

7. How to create a CNN..mp4

04:23

8. Neural Network Evaluation Mode.mp4

01:06

5. Theory CUDA

1. Whats CUDA.mp4

01:36

2. CUDA Example..mp4

01:53

6. Theory Optimization Components

1. Whats a Loss Function.mp4

01:19

2. Cross Entropy Loss (Theory).mp4

02:40

3. Cross Entropy Loss (Example).mp4

02:02

4. Whats an Optimizer.mp4

01:20

5. Whats a Learning Rate.mp4

01:42

6. How to initiate Adam (Example).mp4

01:30

7. Theory How to Train a Neural Network

1. How to Train a Neural Network (Example).mp4

02:59

8. Practice Training a CNN

1.1 Link to the dataset.html

1. Gather Data..mp4

01:58

2.1 dataset.zip

2. Build Dataset.mp4

06:26

3.1 model.zip

3. Build the Neural Network.mp4

04:52

4.1 train.zip

4. Training the Neural Network.mp4

03:23

9. Farewell and Assignment

1.1 answer.zip

1. Farewell.mp4

03:17

Description

This course is designed for beginners with little 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

Who is this for?

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.

What You Need to Know?

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

More details

Description
Hands-On Deep Learning with PyTorch: A Beginner's Course:
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 DataLoaders. 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 it's 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:
Datasets
Data Loaders.
Image Augmentation
Loss Functions
Optimizers.
Activation Functions.
Normalization Techniques.
Convolutional Neural Networks (CNN).
Training Neural Networks.
GPU Acceleration.
Requirements:
The only requirement is basic knowledge of Python.
No experience on Deep learning required.
No experience on PyTorch required.
Who this course is for:
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.

User Reviews

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my preferred fields in deep learning are graph neural networks, computer vision, transformers.outside of deep learning am also interested on linear algebra and differential equations.I can do data analysis i understand a some statistical algorithms and on top of proficient python skills i have some experience on r studio.

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