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Machine Learning with PyTorch for Developers

Published by Pearson

Advanced content levelAdvanced

Build state-of-the-art Machine Learning and AI systems with PyTorch

  • Understand classical Machine Learning algorithms, a foundational skill for AI initiatives.
  • Master PyTorch by implementing ML algorithms from scratch.
  • Explore Deep Learning architectures.

PyTorch is the most popular framework for high-performance numerical computation across a wide range of CPU and GPU hardware. While originally intended for quick development of Deep Learning applications, PyTorch is capable of much more and easily applicable to a wide range of numerical applications. In this course, you learn how to leverage the ease of use and power of PyTorch for all your Machine Learning needs. We introduce classical ML and Deep Learning algorithms, analyze their pros and cons, explore the best way to evaluate their performance, and practice how to implement them using PyTorch through a number of practical examples designed to best get you up and running quickly.

What you’ll learn and how you can apply it

  • Supervised and Unsupervised Learning - Develop Artificial Intelligent system using Supervised and Unsupervised Machine Learning algorithms
  • Optimization in Machine Learning - Deploy optimization algorithms for Machine Learning and Artificial Intelligence
  • Deep Neural Network Architectures - Use the flexibility of PyTorch to integrate Deep Neural Network approaches in your system and go beyond classical ML algorithms and achieve better performance
  • End-to-End Pipeline - Build an end-to-end system that converts raw data into AI-driven insights

This live event is for you because...

  • You are a software engineer or programmer who is interested in getting up to speed with machine learning and deep learning.
  • You are proficient with the Python programming language.
  • You have limited or no experience with PyTorch or Machine Learning.

Prerequisites

  • Basic Python
  • NumPy
  • Matplotlib
  • Jupyter

Course Set-up

To work through the examples provided in the course, you need access to the following:

  • Python
  • Pandas
  • Matplotlib
  • Jupyter
  • PyTorch

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1 – Machine Learning Overview (30 min)

  • Basic Principles
  • Unsupervised Learning
  • Supervised Learning
  • Self-Supervised Learning
  • Machine Learning Pipelines
  • Machine Learning as Optimization
  • PyTorch Overview
  • Q&A (length: 5 min)

Segment 2 – Unsupervised Learning (45 min)

  • Use Cases
  • Data Preparation
  • Principal Component Analysis
  • K-Means
  • DBScan
  • Latent Dirichlet Analysis (LDA)
  • Q&A (length: 5 min)
  • Break (length: 5 min)

Segment 3 – Supervised Learning (50 min)

  • Linear Regression
  • Logistic Regression (Classification)
  • K-Nearest Neighbors
  • Support Vector Machines
  • Q&A (length: 5 min)

Segment 4 – Neural Networks (30 min)

  • Perceptrons
  • Activation Functions
  • Feed Forward Networks
  • Deep Learning
  • Break (5 min)

Segment 5 – Deep Learning Applications (50 min)

  • Convolutional Neural Networks
  • Generative Adversarial Networks
  • Recurrent Neural Networks
  • Graph Neural Networks
  • Q&A (5 min)

Your Instructor

  • Bruno Gonçalves

    Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.

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Skill covered

PyTorch