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LIVE TRAINING: Introduction to PYTHON for Programming 

October 12th @12 PM EST

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Price: $147

Regular price $210, discounted 10%
  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

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Price: FREE with ODSC Mini Bootcamp ticket

  • Purchase virtual or in-person Mini Bootcamp ticket and attend Pytorch 101 live training for free

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  • All the Bootcamp training sessions, workshops and talks at ODSC West (more than 200+ sessions)
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  • Pytorch 101 live training and recordings

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Meet Your Instructor

Daniel Voigt Godoy

Daniel Voigt Godoy has 20+ years experience in developing solutions, programs and models using analytical skills across different industries: software development, government, fintech, retail and mobility. 7+ years experience with data processing, data analysis, machine learning and statistical tools: Python (numpy, scipy, pandas, scikit-learn), Spark, R Studio, MatLab and Statistica. Experience in stochastic simulation and agent-based modeling. Experienced programmer in SQL, Python, Java, R, PowerBuilder, PHP. Strong programming skills and eagerness to learn different languages, frameworks and tools. Solid background in statistics, economics, capital markets, debt management and financial instruments.

Course Overview

What’s the plan? 

Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more. The main goal of this training is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and “torch” it! At the end of it, you should be able to understand PyTorch’s key components and how to assemble them together into a working model.

Learning Objectives

  • Understand the basic building blocks of PyTorch: tensors, autograd, models, optimizers, losses, datasets, and data loaders.

  • Identify the basic steps of gradient descent, and how to use PyTorch to make each one of them more automatic.

  • Build, train, and evaluate a model using mini-batch gradient descent.

Course Outline

What’s the plan? 

Matt Harrison has been working with Python and data since 2000. He has a computer science degree from Stanford. He has worked at many amazing companies, created cool products, wrote a couple books, and taught thousands Python and Data Science. Currently, he is working as a corporate trainer, author, and consultant through his company Metasnake, which provides consulting and teaches corporations how to be effective with Python and data science.

Course Overview

Python is a powerful tools that XXX

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Course Outline Day 1

Module 1: PyTorch: tensors, tensors, tensors 

 

Module 2:
Gradient Descent in Five Easy Steps 

Module 3:
 Autograd, your companion for all your gradient needs!

Module 4: Building a Model in PyTorch 

 

  • Introducing a simple and familiar example: linear regression
  • Generating synthetic data
  • Tensors: what they are and how to create them
  • CUDA: GPU vs CPU tensors  
  • Parameters: tensors meet gradients 
  • Step 0: initializing parameters
  • Step 1: making predictions in the forward pass
  • Step 2: computing the loss, or “how bad is my model?”
  • Step 3: computing gradients, or “how to minimize the loss?”
  • Step 4: updating parameters
  • Bonus: learning rate, the most important hyper-parameter
  • Step 5: Rinse and repeat 
  • Computing gradients automatically with the backward method
  • Dynamic Computation Graph: what is that?
  • Optimizers: updating parameters, the PyTorch way
  • Loss functions in PyTorch 
  • Your first custom model in PyTorch
  • Peeking inside a model with state dictionaries
  • The importance of setting a model to training model
  • Nested models, layers, and sequential models
  • Organizing our code: the training step 

Module 5:
Datasets and data loaders

  • Your first custom dataset in PyTorch
  • Data loaders and mini-batches
  • Evaluation phase: setting up the stage
  • Organizing our code: the training loop
  • Putting it all together: data preparation, model configuration, and model training
  • Taking a break: saving and loading models

Key on emojidex Key Details

DATE

TIME:

DURATION:

LEVEL:

AUGUST 24th

12 PM EST, 9 AM PST

4 HOURS

BEGINNER

Prerequisites

This course is for current or aspiring Data Scientists, Machine Learning Engineers, and Deep Learning Practioners.

Knowledge of Python, Jupyter notebooks, Numpy and, preferably, object oriented programming is welcome.

Basic machine learning concepts may be helpful, but it is not required.

Upcoming Live Training

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