English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 15m | 5.30 GB
Hands-on PyTorch boot camp for Artificial Intelligence applications with artificial neural networks and deep learning
Master the latest and hottest deep learning frameworks (PyTorch) for Python data science
This course is your complete guide to practical machine learning and deep learning using the PyTorch framework in Python and covers the important aspects of PyTorch. If you take this course, you’ll have no need to take other courses or buy books on PyTorch.
In this age of big data, companies across the Globe use Python to sift through the avalanche of information at their disposal; the advent of frameworks such as PyTorch is revolutionizing deep learning.
By gaining proficiency in PyTorch, you can give your company a competitive edge and take your career to the next level.
After taking this course, you’ll be able to use packages such as Numpy, Pandas, and PIL to work with real data in Python and you’ll be fluent in PyTorch. We even introduce you to deep learning models such as Convolution Neural Networks (CNNs)!
The underlying motivation for the course is to ensure you can apply Python-based data science on real data today, start analyzing data for your own projects whatever your skill level, and impress potential employers with actual examples of your data science abilities.
Learn
- Deep Learning Basics – Getting started with Anaconda, an important Python data science environment
- Neural Network Python Applications – Configuring the Anaconda environment to get started with PyTorch
- Introduction to Deep Learning Neural Networks – Theoretical underpinnings of important concepts (such as deep learning) without the jargon
- AI Neural Networks – Implementing Artificial Neural Networks (ANNs) with PyTorch
- Neural Network Model – Implementing deep learning (DL) models with PyTorch
- Deep Learning AI – Implement common machine learning algorithms for image classification
- Deep Learning Neural Networks – Implement PyTorch-based deep learning algorithms on image data
Table of Contents
Introduction to the Course – Welcome to the PyTorch Primer
1 Welcome to PyTorch
2 Get Started with the Python Data Science Environment – Anaconda
3 Anaconda for Mac Users
4 Why PyTorch
5 Install PyTorch
6 Further Installation Instructions for Mac
7 Working with CoLabs
Introduction to Python Data Science Packages (Other Than PyTorch)
8 Python Packages for Data Science
9 Introduction to Numpy
10 Create Numpy Arrays
11 Numpy Operations
12 Numpy for Basic Vector Arithmetric
13 Numpy for Basic Matrix Arithmetic
14 PyTorch Basics – What Is a Tensor
15 Explore PyTorch Tensors and Numpy Arrays
16 Some Basic PyTorch Tensor Operations
Other Python Data Science Packages for Dealing with Data
17 Read in CSV data
18 Read in Excel data
19 Basic Data Exploration with Pandas
Basic Statistical Analysis with PyTorch
20 Ordinary Least Squares (OLS) Regression- Theory
21 OLS Linear Regression-Without PyTorch
22 OLS Linear Regression from First Principles-Theory
23 OLS Linear Regression from First Principles-With PyTorch
24 More OLS With PyTorch
25 Generalised Linear Models (GLMs)-Theory
26 Logistic Regression-Without PyTorch
27 Logistic Regression-With PyTorch
Introduction to Artificial Neural Networks (ANN)
28 Introduction to ANN
29 PyTorch ANN Syntax
30 What Are Activation Functions Theory
31 More on Backpropagation
32 Bringing Them Together
33 Setting Up ANN Analysis with PyTorch
34 DNN Analysis with PyTorch
35 More DNNs
36 DNNs For Identifying Credit Card Fraud
37 An Explanation of Accuracy Metrics
Neural Networks on Images
38 What Are Images
39 Read in Images in Python
40 Basic Image Conversions
41 Why AI and Deep Learning
42 Artificial Neural Networks (ANN) For Image Classification
43 Deep Neural Networks (DNN) For Image Classification
Introduction to Artificial Intelligence (AI) and Deep Learning
44 What is CNN
45 Implement CNN on Imagery Data
46 More on CNN
47 Introduction to Transfer Learning – Theory
48 Implement CNN Using a Pre-Trained Model
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