LIVE TRAINING
SAVE THE DATE: August 24th, 12 PM ET
LIVE TRAINING: Introduction to PYTHON for Programming
October 12th @12 PM EST
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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Subscribe and get an additional 10% to 35% off ALL live training session
Pricing
Price: FREE with ODSC Gold ticket or above
- Purchase virtual or in-person Gold ticket or above and attend Pytorch 101 live training for free
Includes:
- All the Bootcamp training sessions, workshops and talks at ODSC West (more than 200+ sessions)
- ODSC West recordings
- Networking events
- Access to Career Expo
- Pytorch 101 live training and recording
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Price: $95 with Annual Ai+ Premium Subscription
- Purchase Annual Premium Subscription and attend Pytorch 101 live training for $95
Includes:
- Access to all on-demand Ai+ Sessions (more than 100)
- Previous ODSC Conference recordings
- $500 credit for upcoming ODSC Conferences
- Machine Learning Certification
- Deep Learning Bootcamp
- Pytorch 101 live training and recordings
Price: $189 (includes 10% discount)
- Purchase your Pytorch 101 ticket by 2022/08/19 to redeem 30% discount
Includes:
- Pytorch 101 live training and recordings
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
Join the live session with Daniel Voigt Godoy
SAVE 30%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 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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