Knowledge of Deep Learning, and one of the following languages:
Java
Scala
and the following software:
Java (developer version) 1.7 or later (Only 64-Bit versions supported)
Apache Maven
IntelliJ IDEA or Eclipse
Git
Overview
Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.
Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
Course Outline
Getting Started
DL4J Examples in a Few Easy Steps
Using DL4J In Your Own Projects: Configuring the POM.xml File
Programmers who seek an understanding of NLP (Natural Language Processing)
Overview
DL (Deep Learning) is a subset of ML (Machine Learning).
Python is a popular programming language that contains libraries for Deep Learning for NLP.
Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos.
In this instructor-led, live training, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.
By the end of this training, participants will be able to:
Design and code DL for NLP using Python libraries.
Create Python code that reads a substantially huge collection of pictures and generates keywords.
Create Python Code that generates captions from the detected keywords.
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
Course Outline
Introduction to Deep Learning for NLP
Differentiating between the various types of DL models
Using pre-trained vs trained models
Using word embeddings and sentiment analysis to extract meaning from text
How Unsupervised Deep Learning works
Installing and Setting Up Python Deep Learning libraries
Using the Keras DL library on top of TensorFlow to allow Python to create captions
Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) to use as extended DL libraries for the purpose of creating captions.
Using Keras on top of TensorFlow or Theano to quickly experiment on Deep Learning
Creating a simple Deep Learning application in TensorFlow to add captions to a collection of pictures
TensorFlow™ is an open source software library for numerical computation using data flow graphs.
SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.
Word2Vec is used for learning vector representations of words, called “word embeddings”. Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).
Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.
Audience
This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.
After completing this course, delegates will:
understand TensorFlow’s structure and deployment mechanisms
be able to carry out installation / production environment / architecture tasks and configuration
be able to assess code quality, perform debugging, monitoring
be able to implement advanced production like training models, embedding terms, building graphs and logging
Course Outline
Getting Started
Setup and Installation
TensorFlow Basics
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
Course Outline
Overview of Python packages related to NLP
Introduction to NLP (examples in Python of course)
Simple Text Manipulation
Searching Text
Counting Words
Splitting Texts into Words
Lexical dispersion
Processing complex structures
Representing text in Lists
Indexing Lists
Collocations
Bigrams
Frequency Distributions
Conditionals with Words
Comparing Words (startswith, endswith, islower, isalpha, etc…)
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
Course Outline
Introduction
NLP and R vs Python
Installing and Configuring R Studio
Installing R Packages Related to Natural Language Processing (NLP)
An Overview of R’s Text Manipulation Capabilities
Getting Started with an NLP Project in R
Reading and Importing Data Files into R
Text Manipulation with R
Document Clustering in R
Parts of Speech Tagging in R
Sentence Parsing in R
Working with Regular Expressions in R
Named-Entity Recognition in R
Topic Modeling in R
Text Classification in R
Working with Very Large Data Sets
Visualizing Your Results
Optimization
Integrating R with Other Languages (Java, Python, etc.)
This is an introductory course on Deep Learning. The students will get to know the evolution of deep neural network and their application in areas like image recognition, natural language processing etc.
Requirements
Basic Mathematics
Description
Have you ever wondered what is Deep Learning and how it is helping today in powering Artificial Intelligence?
This basic course in Deep Learning may unravel some of them. You dont need any technical or coding background to know the basic fundamentals of Neural Network. This course is designed for functional consultants, product managers as well as developers and architects.
Contents of the course:
1. Inspiration for Deep Learning
2. Key Concepts of Deep Learning
3. Improving the model
4. Convolutional network
5. Recurrent network
6. Word representation
Who this course is for:
All Developers, Business Managers, Functional leads, AI enthusiasts
Integrate with Zapier, APIs and Native Integrations
The Basics of Natural Language Processing with Dialogflow
Entities Detection in Chatbots with NLP
Requirements
No programming experience needed. You’ll learn everything on the course.
Description
In this course, you will learn how to build a chatbot with no code.
Building a chatbot for your business can be very useful if :
– you want to generate more leads from your website
– automate customer support
– automate employee support
We’ll go through all the steps necessary to build the best chatbots. Without code, you will learn how to use variables, manipulate AI and connect to your tools in the simplest fashion.
The course is planned for 7 days so that you can take the time to apply what you learnt from the videos into your chatbot. It is not a requirement though and you can definitely binge watch it in a day and finish your chatbot in one go.
This course tries to give you a complete overview of how to build a chatbot. You may not need everything though.
By the end of Lecture 3 : you can already build a basic chatbot.
By the end of Lecture 5 : you can use chatbots combined with AI.
By the end of the course : you will master all aspects of chatbot building.
If at any moment, you fill the need to ask us a question, please do not hesitate. Write us an email at tanuki@ideta.io and we’ll help you.
We also welcome feedback, good or bad.
Who this course is for:
Companies who need a chatbot, agencies, chatbot developers, UX agencies, freelancers
Understand the importance of large language models in natural language processing (NLP) and their role in production.
Gain familiarity with prominent large language models, such as GLaM, Megatron-Turing NLG, Gopher, Chinchilla, PaLM, OPT, and BLOOM, and learn the key insights f
Learn the basics of transfer learning and transformer training to optimize AI models in NLP applications.
Be able to articulate the advancements and key contributions of each large language model, from GPT-3 to present day
What skill you need to keep your job or find a new one
Improve professional communication using ChatGPT
Improve dating and relationship communication with Chat GPT
Reduce stress with ChatGPT
Entertain yourself and others with ChatGPT
Solve problems with ChatGPT
Learn a language with ChatGPT
Requirements
No prerequisites needed
No programming experience needed
Description
If you’re eager to explore the amazing benefits of ChatGPT in your professional and personal life, you’re in luck! Our expert instructor, a 15-year Microsoft veteran, has designed the perfect solution for you – a beginner’s guide that covers everything you need to know about using ChatGPT effectively.
Now, hold on – this course is not for those seeking to master programming or build APIs. Instead, it’s a comprehensive guide that demystifies the fundamentals of ChatGPT, giving you the power to unleash your AI creativity without breaking a sweat.
By the end of the course, you’ll have the skills and knowledge to communicate with ChatGPT for practical applications like improving communication in your personal and professional life, problem-solving, stress reduction, entertainment, and even dating! And that’s not all – you’ll also be able to create high-quality prompts by learning about all the essential elements that your prompts need.
Not only will this course equip you with the tools to stay ahead of the game in a rapidly changing job market, but it will also give you a competitive edge and enable you to leverage the many benefits that ChatGPT has to offer.
Whether you’re a beginner or have some experience with AI technology, this course is perfect for anyone who wants to learn how to use ChatGPT effectively. Plus, with resources like over 200 prompts for different professions and a comprehensive list of prompts for most common use cases, you’ll have everything you need to succeed.
So, don’t wait any longer – join this course today and unlock the world of ChatGPT!
Who this course is for:
Business professionals who want to understand how AI can impact their business and how they can leverage AI-powered tools like ChatGPT to improve their operations.
General public: Anyone who is interested in learning about artificial intelligence and its applications. This audience could include individuals who are curious about AI and its implications, but may not have a technical background
Marketers and content creators who want to learn how to use AI-powered tools like ChatGPT to create engaging and personalized content.
Educators who want to learn how to use AI-powered tools like ChatGPT to create more interactive and engaging learning experiences for their students.
esearchers who want to learn about the latest developments in the field of AI and natural language processing, and how they can apply these advancements to their own research.
Understand the theory behind Artificial Intelligence
Requirements
No Programming experience needed. You will learn everything you need to know.
Description
Do you want to build super-powerful applications in Artificial intelligence (AI) but you don’t know how to code?
Are you intimidated by AI and don’t know where to start?
Or maybe you don’t have a computer science degree and want to break into AI?
Are you an aspiring entrepreneur who wants to maximize business revenue and reduce costs with AI but don’t know how to get there quickly and efficiently?
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
The AI operates by collecting information on the website analytics, its user base, product catalog, and other key aspects. Once the data is complete, the AI will take the final instructions from you such as theme, content, and color preferences to create an entirely unique design for your website.
Enter the name of your website. Select a short and crisp name for your AI website.
Add unique features to your website. Create your own AI website without coding and add your desired features by easy drag-n-drop action.
Launch your AI website.
Who this course is for:
Anyone who want to create and design websites at incredible speed with Artificial Intelligence (AI) technology and tools
Entrepreneurs who have passion to learn website creation with no coding involved in the process.
Developers who want to become aware of artificial intelligence new-age technologies to create webpages and sites
Ardent learners and web-developers who want to create a difference in the field of web-design leveraging the power of Artificial Intelligence (AI)
