PyTorch for Deep Learning Bootcamp: Zero to Mastery

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Navid Shirzadi

9:08:28

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1. Course Introduction & Overview

1.1 Source-Codes.zip

1. Course Content.mp4

09:54

2. Why Google Colab.mp4

01:19

3. Introduction to Colab Environment.mp4

09:10

2. Useful Packages

1. NumPy Basics.mp4

21:57

2. Pandas Basics.mp4

15:28

3. PyTorch Tensor Basics

1. Introduction to Tensors.mp4

07:00

2. Working with PyTorch Tensors.mp4

16:55

4. Neural Network Basic Concepts

1. Basic Terms About NN.mp4

06:17

2. Activation Function.mp4

07:17

3. How Neural Network Learn.mp4

07:09

4. Gradient Decent Optimization.mp4

09:03

5. PyTorch for Multilayer Perceptron (MLP)

1. PyTorch Regression Using MLP Part1.mp4

19:45

2. PyTorch Regression Using MLP Part2.mp4

18:50

3. PyTorch Regression Using MLP Part3.mp4

21:56

4. PyTorch Regression Using MLP Part4.mp4

13:17

6. PyTorch for Deep Artificial Neural Network (ANN)

1. Deep Artificial Neural Network Introduction.mp4

01:19

2. PyTorch Image Classification Using ANN Part1.mp4

32:17

3. PyTorch Image Classification Using ANN Part2.mp4

22:32

4. PyTorch Image Classification Using ANN Part3.mp4

29:46

5. PyTorch Image Classification Using ANN Part4.mp4

14:45

7. PyTorch for Convolutional Neural Network (CNN)

1. Introduction.mp4

04:57

2. Convolutional Layer (Image Filter).mp4

16:08

3. Pooling Layer.mp4

07:09

4. Flattening.mp4

02:34

5. Conclusion.mp4

03:15

6. PyTorch Image Classification Using CNN Part1.mp4

18:33

7. PyTorch Image Classification Using CNN Part2.mp4

30:45

8. PyTorch Image Classification Using CNN Part3.mp4

13:49

9. PyTorch Image Classification Using CNN Part4.mp4

13:58

8. Using GPU Instead of CPU

1. Introduction to CPU & GPU.mp4

07:35

2. Watch if You Dont Use Colab!.mp4

08:18

3. How to Use GPU.mp4

19:57

4. How to Save & Load a Model Using PyTorch.mp4

18:39

9. PyTorch for Recurrent Neural Network

1. Introduction to Recurrent Neural Network.mp4

06:01

2. What is LSTM and How it Works.mp4

12:53

3.1 Temp Data.csv

3. PyTorch for Time Series Forecasting Using LSTM-Part1.mp4

27:11

4. PyTorch for Time Series Forecasting Using LSTM-Part2.mp4

21:03

5. PyTorch for Time Series Forecasting Using LSTM-Part3.mp4

13:09

6. PyTorch for Time Series Forecasting Using LSTM-Part4.mp4

16:38

Description

Learn How to Use PyTorch (Facebook Library) for Deep Learning with Practical Examples

What You'll 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)
Use PyTorch for Natural Language Processing (NLP)

Who is this for?

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

More details

Description
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.

Who this course is for:
beginner to advance python developers, data analysts, engineers and overall data science enthusiast want to learn about deep learning with PyTorch

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My name is Navid Shirzaid and I am super excited that you are here to read this section!I am a researcher with more than 7 years of experience in the field of controlling integrated energy systems with extensive skill in using mathematical optimization strategies. I am also proficient in coding with Python and developing machine learning and deep learning models for different applications. I have several publications in the field of designing and control strategies of energy systems using machine learning, deep learning, and artificial intelligence.To Conclude, I am passionate about Data Science and Machine Learning, and Optimization applications in real-world problems and I really like to share my experience with you!

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