The deep learning models that power AI systems are often black boxes. Explainable AI tries to understand how these models make decisions, so that we can use them responsibly. In this course, you will learn the basic techniques of Explainable AI, including Generative Adversarial Networks (GANs). You will also learn about legal rights to explanation, and the role of explanation in regulating AI.
Large Language Models (LLMs) and text generation are at the heart of many cutting edge AI applications today. This course is a no-code introduction to LLMs, covering their history up to ChatGPT, how they generate text with neural networks, and how they can be adjusted via parameters like temperature. This course is a great starting point for exploring LLMs and generative AI more broadly.
Familiarity with deep learning frameworks (Caffe, Torch, etc.)
Python programming experience
Audience
AI Researchers
Developers
Overview
Chainer is an open source framework based on Python, built for accelerating research and implementing neural network models. It provides flexible, efficient, and simplified approaches to developing deep learning algorithms.
This instructor-led, live training (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug.
By the end of this training, participants will be able to:
Set up the necessary development environment to start developing neural network models.
Define and implement neural network models using a comprehensible source code.
Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
Format of the Course
Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.
Course Customization Options
To request a customized training for this course, please contact us to arrange.
Basic understanding of machine translation concepts.
Audience
Localization specialists with a technical background
Global content managers
Localization engineers
Software developers in charge of implementing global content solutions
Overview
In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience’s requirements.
Format of the Course
Part lecture, part discussion, heavy hands-on practice
Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.
Overview
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Course Outline
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Supervised Learning and Unsupervised Learning
Machine Learning Languages, Types, and Examples
Supervised vs Unsupervised Learning
Supervised Learning
Decision Trees
Random Forests
Model Evaluation
Machine Learning with Python
Choice of libraries
Add-on tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means
Introduction to Machine Learning and Deep Learning
PyTorch Basics: Tensors & Gradients
Linear Regression with PyTorch
Working with Image Data in PyTorch
Image Classification using Convolutional Neural Networks
Residual Networks, Data Augmentation and Regularization Techniques
Generative Adverserial Networks
Requirements
Basic Linear Algebra (matrix multiplication)
Basic Python Programming
Basic Calculus (Derivatives)
Description
“Deep Learning with PyTorch for Beginners is a series of courses covering various topics like the basics of Deep Learning, building neural networks with PyTorch, CNNs, RNNs, NLP, GANs, etc. This course is Part 1 of 5.
Topics Covered:
1. Introduction to Machine Learning & Deep Learning 2. Introduction on how to use Jovian platform 3. Introduction to PyTorch: Tensors & Gradients 4. Interoperability with Numpy 5. Linear Regression with PyTorch – System setup – Training data – Linear Regression from scratch – Loss function – Compute gradients – Adjust weights and biases using gradient descent – Train for multiple epochs – Linear Regression using PyTorch built-ins – Dataset and DataLoader – Using nn.Linear – Loss Function – Optimizer – Train the model – Commit and update the notebook 7. Sharing Jupyter notebooks online with Jovian
Who this course is for:
Beginner Python developers curious about Deep Learning and PyTorch
