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Dive into the Ultimate BusinessToolkit: Curated Prompts from GPT-4 + Powerful Business Prompts

The ultimate catalyst for entrepreneurial success, now available on Gumroad.

Unlock the potential of your startup with this comprehensive digital guide, showcasing curated prompts directly from GPT-4. Whether you're in SAAS, E-commerce, Fintech, or Edtech, this toolkit is designed to spark innovative thinking and guide your strategic planning.

Agents powered by large language models (LLMs) are quickly gaining popularity from both individuals and companies as people are finding new emerging capabilities and opportunities to greatly improve their productivity. An especially powerful recent development has been the popularization of retrieval-based LLM systems that can hold informed conversations by using tools, looking at documents, and planning their approaches. These systems are very fun to experiment with and offer unprecedented opportunities to make life easier, but also require many queries to large deep learning models and need to be implemented efficiently. This course will observe how you can deploy an agent system in practice and scale up your system to meet the demands of users and customers.

This course focuses on teaching participants how to perform large-scale image classification, specifically in the context of winning the Google Landmark Recognition 2020 Kaggle competition. The learning outcomes include understanding the challenges of landmark recognition with a vast number of classes, exploring different modeling techniques, and implementing efficient code for image classification. The course covers topics such as classical approaches, validation strategies, model architecture, fine-tuning, post-processing, ensembling, and augmentation techniques like cutout. The teaching method involves a video presentation by industry experts from NVIDIA, sharing their winning solution and insights. This course is intended for data scientists, machine learning practitioners, and individuals interested in deep learning competitions, particularly in the field of computer vision.

What you'll learn

You will learn what a network is and why it is needed.

Describe the network components and provide the requirements for a networking solution.

Introduce the OSI model and the TCP/IP protocol suite and their role in networking.

Cover the basics of Ethernet technology and understand how data is forwarded in an Ethernet network.

This course delves into the strategies used by Kaggle Grandmasters of NVIDIA to excel in a data science competition focused on building a recommendation system for e-commerce. The learning outcomes include understanding the 2-stage model of recommender systems, creating candidate generation and co-visitation matrices, feature selection and engineering for a reranker model, and model ensembling. The teaching method involves video lectures with detailed explanations and real-world examples. This course is intended for data scientists, machine learning engineers, and anyone interested in mastering recommender systems and participating in data science competitions.

Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.

What you'll learn

What is AI and AI use cases, Machine Learning, Deep Leaning, and how training and inference happen in a Deep Learning Workflow.

The history and architecture of GPUs,  how they differ from CPUs, and how they are revolutionizing AI.    

Become familiar with deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem or cloud.

Requirements for multi-system AI clusters and considerations for infrustructure planning, including servers, networking, storage and tools. 

Retrieval Augmented Generation (RAG) - Introduced by Facebook AI Research in 2020, is an architecture used to optimize the output of an LLM with dynamic, domain specific data without the need of retraining the model. RAG is an end-to-end architecture that combines an information retrieval component with a response generator. In this introduction we provide a starting point using components we at NVIDIA have used internally. This workflow will jumpstart you on your LLM and RAG journey.

Generative AI describes technologies that are used to generate new content based on a variety of inputs. In recent time, Generative AI involves the use of neural networks to identify patterns and structures within existing data to generate new content. In this course, you will learn Generative AI concepts, applications, as well as the challenges and opportunities in this exciting field.

Course Abstract

This notebook explores the biological and psychological inspirations to the world's first neural networks.

Learning Objectives

The goals of this exercise include:

Exploring how neural networks use data to learn

Understanding the math behind a neuron