Training Program Topics - List 3

Introduction to Julia???????

?????????????? Julia basics

with Julia

?????????????? Using the console

?????????????? Julia as a calculator

?????????????? Printing

?????????????? Variables

?????????????? Valid variable names

?????????????? Assigning variables

?????????????? Calculating with variables

?????????????? Basic data types

?????????????? Recognizing data types

?????????????? Finding the data type

?????????????? Converting types

?????????????? Data structures

?

?????????????? Introduction to strings

?????????????? Indexing strings

?????????????? String interpolation

?????????????? Slicing strings

?????????????? Introduction to arrays

?????????????? Creating arrays

?????????????? Indexing arrays

?????????????? Slicing arrays

?????????????? Working with arrays

?????????????? Modifying arrays

?????????????? Fibonacci sequence

?????????????? Appending arrays

?????????????? Operating on arrays

?????????????? Finding the array length

?????????????? Array operating fluency

?????????????? Operating on body temperatures

?????????????? Functions and packages

?

?????????????? Conditionals

?????????????? Comparisons

?????????????? Check input data type

?????????????? If-else practice

?????????????? Conditioning on body temperature

?????????????? Basic Functions

?????????????? Writing a function for strings

?????????????? Writing a function with multiple arguments

?????????????? Absolute value

?????????????? Mutating functions and multiple dispatch

?????????????? Modifying arrays

?????????????? Everyone wins

?????????????? Multiple dispatch

?????????????? Using packages

?????????????? Importing packages

?????????????? Using the Statistics package

?????????????? DataFrames

?

?????????????? DataFrames

?????????????? Loading and examining data

?????????????? Creating a DataFrame

?????????????? DataFrame properties

?????????????? Sorting and slicing data

?????????????? Indexing DataFrames

?????????????? Slicing DataFrames

?????????????? Sorting patients

?????????????? Descriptive statistics

?????????????? Literary analysis

?????????????? Describing patient data

?????????????? Standardize heart rate

?????????????? Filtering

?????????????? Constructing filters

?????????????? Filtered body temp

?????????????? Classic books

?????????????? Final thoughts

Intermediate Julia???????????

?????????????? Loops and Ranges

?????????????? For loops

?????????????? For loops question

?????????????? Loop over a vector

?????????????? Loops and enumerate

?????????????? Looping over nested structures

?????????????? While loops

?????????????? Writing a while loop

?????????????? While loops for iteration

?????????????? Ranges

?????????????? Ranges - iteration

?????????????? Defining ranges

?????????????? Looping over ranges

?????????????? Splat unpacking

?????????????? Data Structures

?

?????????????? Tuples

?????????????? When should you use a tuple?

?????????????? Create, index, and slice a tuple

?????????????? Create a NamedTuple for a person

?????????????? Dictionaries

?????????????? Create untyped dict, iterate

?????????????? Create typed dict, iterate

?????????????? Modify keys/values in dict, use get()

?????????????? Multi-dimensional arrays

?????????????? Create 1D and 2D arrays

?????????????? Index and slice a 2D array

?????????????? Array merging

?????????????? Structs

?????????????? Define a structure

?????????????? Mutable and typed structs

?????????????? Advanced Functions in Julia

?

?????????????? Execution time and measurement

?????????????? Built-in time macro

?????????????? Timing a function

?????????????? Positional and Default Arguments, Type Declarations

?????????????? Positional arguments recap

?????????????? Default arguments

?????????????? Type declarations

?????????????? Keyword Arguments

?????????????? Keyword arguments

?????????????? Variable number of arguments

?????????????? Writing Your Own Function

?????????????? Writing your own functions

?????????????? Writing your own functions - structs

?????????????? Dataframe Operations and Python/R Packages in Julia

?

?????????????? Anonymous Functions and Multiple Dispatch

?????????????? Multiple dispatch

?????????????? Anonymous functions

?????????????? Filtering DataFrames

?????????????? Importing Functions from Python and R

?????????????? Importing Python and R

?????????????? Python functions in Julia

?????????????? R functions in Julia

?????????????? Cleaning Data

?????????????? Renaming columns

?????????????? Missing data

?????????????? Advanced missing data

Introduction to Data Visualization with Julia?????????

?????????????? First steps with Plots.jl

?????????????? Visualizing data

?????????????? Why should I plot?

?????????????? Should I plot?

?????????????? Interpreting a plot

with Plots.jl

?????????????? Installing Plots.jl

?????????????? Price goes up

?????????????? Price changes

?????????????? Trading volume

?????????????? Plotting multiple variables

?????????????? Multiple line plots

?????????????? Correlated ETF funds

?????????????? Multiple line plots!

?????????????? Histograms, time series and more

?

?????????????? Visualizing distributions

?????????????? Potato prices

?????????????? Rajasthan normalized

?????????????? Instant and powdered coffee

?????????????? Stacking states

?????????????? Dropping Bars

?????????????? Bar chart or histogram

?????????????? Price deviations

?????????????? Saree prices

?????????????? Visualizing time series

?????????????? Them apples

?????????????? (Super)fine rice

?????????????? Boxes and violins

?????????????? Boxing states

?????????????? Price distributions

?????????????? Tomato seasons

?????????????? Customizing Julia Plots

?

?????????????? Customizing with themes

?????????????? Previewing themes

?????????????? Saving to file

?????????????? Theme showdown

?????????????? Plot Attributes and Color Palettes

?????????????? Pump it up

?????????????? Music effects on depression

?????????????? Self-reported conditions

?????????????? Efficient visualizations with layouts

?????????????? Composers mental health

?????????????? Streaming while working

?????????????? Favorite genres

?????????????? Series Recipes

?????????????? Series recipe notation

?????????????? Spotify as therapy

?????????????? Log scatter recipe

?????????????? Plotting Data in DataFrames

?

?????????????? Plotting data in DataFrames

?????????????? Upcharging older smokers

?????????????? Upcharging heavier smokers

?????????????? BMI versus age

?????????????? Plotting in More Dimensions

?????????????? Slicing by region

?????????????? Aging and having kids

?????????????? Multiple plots from DataFrames

?????????????? Correlation matrix plot

?????????????? BMI per region

?????????????? Smokers by age

?????????????? Region premiums

?

Data Manipulation in Julia???????????

?????????????? Inspecting DataFrames

?????????????? Diving into DataFrames

?????????????? Symbols vs. Strings

?????????????? Describe it to me

?????????????? Missing anything?

?????????????? Selecting columns

?????????????? Column selection

?????????????? Selecting patterns

?????????????? Regular penguins

?????????????? Exploring Data with Visualizations

?????????????? Flipper distribution

?????????????? Rating vs. cocoa percentages

?????????????? Plotting minimum wages over time

?????????????? Working with columns

?

?????????????? Dropping and moving columns

?????????????? Penguin drop

?????????????? Reordering of wages

?????????????? Manipulating columns

?????????????? Using select()

?????????????? Penguin transformations

?????????????? Combining chocolates

?????????????? What shall you use?

?????????????? Creating new columns

?????????????? Copy the capitals

?????????????? Column or row?

?????????????? Chocolate percentages

?????????????? Aggregating DataFrames

?

?????????????? Exploring grouped data

?????????????? Wages multiple ways

?????????????? Penguin group counts

?????????????? Unique chocolate beans

?????????????? Duplicate rows or not?

?????????????? Grouped summary statistics

?????????????? Penguin characteristics

?????????????? Chocolate location vs. rating

?????????????? Pivoting data

?????????????? Reshaping wages

?????????????? Chocolate location pivot

?????????????? Improving readability with Chain.jl

?????????????? Chaining chocolates

?????????????? Penguin plotting in chain

?????????????? Minimum wage by region

?????????????? Improving Your Workflow

?

?????????????? Loading and writing CSV files

?????????????? Decimals and delimiters

?????????????? Loading the 80s

?????????????? Write it down

?????????????? Joining data

?????????????? State joins capitals

?????????????? Penguin joins

?????????????? Handling missing values

?????????????? Dropping missing values

?????????????? Replacing rating with median

?????????????? Replacing rating with group median

?????????????? Efficient workflow

?????????????? First steps with flights

?????????????? Missing delays?

?????????????? Delays on US airports

?

Introduction to Mlflow??

?????????????? Introduction to MLflow

?????????????? What is MLflow?

?????????????? Components of MLflow

?????????????? MLflow experiments

?????????????? MLflow Tracking

?????????????? Starting a run

?????????????? Logging a run

?????????????? How to retrieve active run data?

?????????????? Querying runs

?????????????? Search runs query options

?????????????? Searching runs

?????????????? MLflow Models

?????????????? Introduction to MLflow Models

?????????????? Package a machine learning model

?????????????? Storage Format

?????????????? What's in an MLmodel file?

?????????????? Model API

?????????????? Saving and loading a model

?????????????? Logging and loading a model

?????????????? Custom models

?????????????? Creating a custom Python Class

?????????????? Custom scikit-learn model

?????????????? Scikit-learn flavor and evaluation

?????????????? Model serving

?????????????? Serving a model

?????????????? Score from a served model

?????????????? Mlflow Model Registry

?

?????????????? Introduction to MLflow Model Registry

?????????????? Create a Model

?????????????? Searching Models

?????????????? Registering Models

?????????????? Registering existing models

?????????????? Registering new models

?????????????? Model stages

?????????????? Model stages in Model Registry

?????????????? Transitioning model stages

?????????????? Model deployment

?????????????? Serving models from the Model Registry

?????????????? Loading models from the Model Registry

?????????????? MLflow Projects

?

?????????????? Introduction to MLflow Projects

?????????????? MLproject file layout

?????????????? Creating an MLproject

?????????????? Running MLflow Projects

?????????????? MLflow run command

?????????????? MLflow projects module

?????????????? Specifying parameters

?????????????? Adding parameters to MLproject

?????????????? Adding parameters to project run

?????????????? Workflows

?????????????? Creating an MLproject for the ML Lifecycle: Model Engineering

?????????????? Creating an MLproject for the ML Lifecycle: Model Evaluation

?????????????? Creating a multi-step workflow: Model Engineering

?????????????? Creating a multi-step workflow: Model Evaluation

?

?????????????? FastAPI Basics

?????????????? Why FastAPI?

?????????????? First application

?????????????? FastAPI vs. Django

?????????????? GET operations

?????????????? Hello world

?????????????? Hello who?

?????????????? POST operations

?????????????? Pydantic model

?????????????? POST operation in action

?????????????? FastAPI Advanced topics

?

?????????????? PUT and DELETE operations

?????????????? PUT operation in action

?????????????? DELETE operation in action

?????????????? Handling errors

?????????????? Status code classification

?????????????? Handling a client error

?????????????? Using async for concurrent work

?????????????? When should we use async?

?????????????? Asynchronous DELETE operation

?????????????? Building and testing a JSON CRUD API

?

?????????????? FastAPI automated testing

?????????????? Unit tests vs. system tests

?????????????? System test

?????????????? Building a JSON CRUD API

?????????????? HTTP operations & CRUD steps

?????????????? DELETE operation response

?????????????? Complete JSON CRUD API

?????????????? Writing a manual functional test

?????????????? System tests vs. functional tests

?????????????? Functional test

?

Introduction to Data Versioning with DVC?????????????

?????????????? Introduction to DVC

?????????????? Data Versioning Motivation

?????????????? Anatomy of a Machine Learning Model

?????????????? Differences Between Data and Code Versioning

?????????????? Understanding Hyperparameters

?????????????? Introduction to DVC

?????????????? Working with Git CLI

?????????????? Review DVC CLI

?????????????? DVC features and use cases

?????????????? DVC pipelines

?????????????? CI/CD for machine learning

?????????????? DVC Configuration and Data Management

?

?????????????? DVC Setup and Initialization

?????????????? Setting up DVC

?????????????? .dvcignore Patterns

?????????????? DVC Cache and Staging Files

?????????????? Working with DVC Cache

?????????????? Understanding .dvc Files

?????????????? Configuring DVC Remotes

?????????????? Purpose of DVC Remotes

?????????????? Setup a DVC Remote

?????????????? Interacting with DVC Remotes

?????????????? Versioning Data using DVC Remote

?????????????? Checking out Versioned Data

?????????????? Pipelines in DVC

?

?????????????? Code organization and refactoring

?????????????? Understanding parameter files in DVC

?????????????? Write a parameter file

?????????????? Writing and visualizing DVC pipelines

?????????????? Designing a DVC pipeline

?????????????? Visualizing a DVC pipeline

?????????????? Executing DVC pipelines

?????????????? DVC pipeline execution concepts

?????????????? Execute a ML model training pipeline

?????????????? Evaluation: Metrics and plots in DVC

?????????????? Tracking DVC Metrics

?????????????? Adding plots to dvc.yaml

Introduction to dbt?????????

?????????????? Welcome to dbt

?????????????? What is dbt?

?????????????? Users of dbt

?????????????? Version of dbt

?????????????? dbt subcommands

?????????????? Creating a dbt project

?????????????? Initializing a dbt project

?????????????? Creating a project profile

?????????????? Working with a first project

?????????????? dbt project workflow

?????????????? Running a project

?????????????? Modifying a model

?????????????? dbt models

?

?????????????? What is a dbt model?

?????????????? Features of a data model

?????????????? dbt model statements

?????????????? Creating a dbt model

?????????????? Updating dbt models

?????????????? Config files

?????????????? Updating a dbt model

?????????????? Hierarchical models in dbt

?????????????? No hierarchy model creation

?????????????? Hierarchical model creation

?????????????? Updating model hierarchies

?????????????? Model troubleshooting

?????????????? Error classification

?????????????? Process of troubleshooting

?????????????? Troubleshooting model errors

?????????????? Testing & Documentation

?

?????????????? Introduction to testing in dbt

?????????????? Built-in tests

?????????????? Defining tests on a model

?????????????? Finding bad data

?????????????? Creating singular tests

?????????????? Steps to develop a singular test

?????????????? Verifying trip duration

?????????????? Verifying test queries

?????????????? Creating custom reusable tests

?????????????? Testing, testing, testing

?????????????? Implementing a reusable test

?????????????? Updating from singular to reusable test

?????????????? Creating and generating dbt documentation

?????????????? dbt docs Command Options

?????????????? dbt documentation flow

?????????????? Creating dbt documentation

?????????????? Implementing dbt in production

?

?????????????? dbt sources

?????????????? Orderly YML

?????????????? Models, sources, or both?

?????????????? Adding a source

?????????????? dbt seeds

?????????????? Kernels of truth

?????????????? ZIP is the code

?????????????? SCD2 with dbt snapshots

?????????????? Snapshot process

?????????????? Snapshot issue

?????????????? Adding a snapshot

?????????????? Automating with dbt build

?????????????? What can't dbt build do?

?????????????? Helping the intern!

?????????????? Putting it all together

?

Working with Llama 3????

?????????????? Understanding LLMs and Llama

?????????????? What is Llama?

?????????????? Loading and using Llama 3

?????????????? Parsing Llama 3 completion outputs

with Llama

?????????????? More creative Llama completions

?????????????? Make a philosophy chatbot

?????????????? Prompt engineering Llama 3

?????????????? Make Llama speak like a pirate

?????????????? 3-shot prompting with Llama

?????????????? Using Llama Locally

?????????????? Performing inference with Llama

?????????????? Creating a JSON inventory list

?????????????? Generating answers with a JSON schema

?????????????? Tuning inference parameters

?????????????? Making safe responses

?????????????? Making a creative chatbot

?????????????? Creating an LLM inference class

?????????????? Personal shopping agent

?????????????? Multi-agent conversations

?????????????? Improving the Agent class

?????????????? Recap: Working with Llama 3

?????????????? Preparing for Llama fine-tuning

?????????????? The Llama fine-tuning libraries

?????????????? Listing TorchTune recipes

?????????????? Running a TorchTune task

?????????????? Preprocessing data for fine-tuning

?????????????? Filtering datasets for evaluation

?????????????? Creating training samples

?????????????? Saving preprocessed datasets

?????????????? Fine-tuning with TorchTune

?????????????? Defining custom recipes

?????????????? Saving custom recipes

?????????????? Running custom fine-tuning

?????????????? Fine-tuning with SFTTrainer on Hugging Face

?????????????? Model fine-tuning with Hugging Face

?????????????? Setting up Llama training arguments

?????????????? Fine-tuning Llama for customer service QA

?????????????? Evaluate generated text using ROUGE

?????????????? Efficient fine-tuning with LoRA

?????????????? Using LoRA adapters

?????????????? LoRA fine-tuning Llama for customer service

?????????????? Making models smaller with quantization

?????????????? Loading 8-bit models

?????????????? Speeding up inference in quantized models

Understanding Prompt Engineering??????????

?????????????? Prompting unveiled

?????????????? A practical demonstration with ChatGPT

?????????????? Your first prompt

?????????????? What is a prompt?

?????????????? Starting strong

?????????????? Contextualizing importance

?????????????? The next level

?????????????? Different approaches

?????????????? Prompting Strategies and Techniques

?????????????? Structuring a prompt

?????????????? ChatGPTea

?????????????? Steeping GPT

?????????????? Output control techniques

?????????????? How long?

?????????????? Salty prompts

?????????????? Evaluating responses

?????????????? Hallucinations

?????????????? Evaluation

?????????????? Prompt formatting

?????????????? To the limit

?????????????? Can I quote you?

?????????????? Advanced Prompt Engineering

?????????????? Training techniques

?????????????? Insightful shot

?????????????? Thinking deep

?????????????? Mitigating model limitations

?????????????? Getting the right fit

?????????????? Beating bias

?????????????? Building advanced applications

?????????????? Chaining context

?????????????? Prompt iteration tips

?????????????? Your "first" prompt

?????????????? Introduction to PyTorch, a Deep Learning Library

?????????????? Introduction to deep learning with PyTorch

with PyTorch tensors

?????????????? Checking and adding tensors

?????????????? Creating our first neural network

?????????????? Your first neural network

?????????????? Stacking linear layers

?????????????? Discovering activation functions

?????????????? Activate your understanding!

?????????????? The sigmoid and softmax functions

?????????????? Training Our First Neural Network with PyTorch

?????????????? Running a forward pass

?????????????? Building a binary classifier in PyTorch

?????????????? From regression to multi-class classification

?????????????? Using loss functions to assess model predictions

?????????????? Creating one-hot encoded labels

?????????????? Calculating cross entropy loss

?????????????? Using derivatives to update model parameters

?????????????? Accessing the model parameters

?????????????? Updating the weights manually

?????????????? Using the PyTorch optimizer

?????????????? Writing our first training loop

?????????????? Using the MSELoss

?????????????? Writing a training loop

?????????????? Neural Network Architecture and Hyperparameters

?????????????? Discovering activation functions between layers

?????????????? Implementing ReLU

?????????????? Implementing leaky ReLU

?????????????? Understanding activation functions

?????????????? A deeper dive into neural network architecture

?????????????? Counting the number of parameters

?????????????? Manipulating the capacity of a network

?????????????? Experimenting with learning rate

?????????????? Experimenting with momentum

?????????????? Layer initialization and transfer learning

?????????????? Fine-tuning process

?????????????? Freeze layers of a model

?????????????? Layer initialization

?????????????? Evaluating and Improving Models

?????????????? A deeper dive into loading data

?????????????? Using the TensorDataset class

?????????????? From data loading to running a forward pass

?????????????? Evaluating model performance

?????????????? Writing the evaluation loop

?????????????? Calculating accuracy using torchmetrics

?????????????? Fighting overfitting

?????????????? Experimenting with dropout

?????????????? Understanding overfitting

?????????????? Improving model performance

?????????????? Implementing random search

?

Developing LLM Applications with LangChain???????

?????????????? Introduction to LangChain & Chatbot Mechanics

?????????????? The LangChain ecosystem

?????????????? Hugging Face models in LangChain!

?????????????? OpenAI models in LangChain!

?????????????? Prompting strategies for chatbots

?????????????? Prompt templates and chaining

?????????????? Chat prompt templates

?????????????? Few-shot prompting

?????????????? Creating the few-shot example set

?????????????? Building the few-shot prompt template

?????????????? Implementing few-shot prompting

?????????????? Chains and Agents

?????????????? Sequential chains

?????????????? Building prompts for sequential chains

?????????????? Sequential chains with LCEL

?????????????? Introduction to LangChain agents

?????????????? What's an agent?

?????????????? ReAct agents

?????????????? Custom tools for agents

?????????????? Defining a function for tool use

?????????????? Creating custom tools

?????????????? Integrating custom tools with agents

?????????????? Retrieval Augmented Generation (RAG)

?????????????? Integrating document loaders

?????????????? PDF document loaders

?????????????? CSV document loaders

?????????????? HTML document loaders

?????????????? Splitting external data for retrieval

?????????????? Splitting by character

?????????????? Recursively splitting by character

?????????????? Splitting HTML

?????????????? RAG storage and retrieval using vector databases

?????????????? Preparing the documents and vector database

?????????????? Building a retrieval prompt template

?????????????? Creating a RAG chain

AI Ethics??????????????

?????????????? Approaching AI Ethics

?????????????? Understand the foundations of AI Ethics and observe some of the challenges of the ethical use of AI.

?????????????? AI ethics: What's the buzz?

?????????????? Guiding principle

?????????????? What's covered by AI ethics?

?????????????? Digging deeper: AI ethics principles

?????????????? Identifying actions

?????????????? AI principles unfolded

?????????????? AI ethics: where's the line?

?????????????? Striking a balance

?????????????? CineLlama Navigator

?????????????? Below the Surface: AI Ethics

?????????????? Unpacking the blackbox: Transparency

?????????????? Transparent AI system

?????????????? Transparency best practices

?????????????? AI fairness: not just a dream

?????????????? Tackling fairness issue

?????????????? Can we make things right?

?????????????? Safeguarding AI: Accountability

?????????????? Accountability common misconceptions

?????????????? Who's responsible?

?????????????? Explainable AI

?????????????? How would you apply XAI?

?????????????? The perfect match

?????????????? The Way Forward: AI Ethics

?????????????? Summaries Ethical frameworks

?????????????? Framing success

?????????????? When a plan comes together

?????????????? The value of ethical AI

?????????????? A proactive approach

?????????????? Identify the stakeholders

?????????????? The future of AI ethics

?????????????? All part of the plan

?????????????? Honing ethics by design

Generative AI Concepts?

?????????????? Introduction to Generative AI

?????????????? What is generative AI?

?????????????? Getting to know generative AI

?????????????? Working with a generative AI model

?????????????? Applications of generative AI

?????????????? Generative AI in the machine learning landscape

?????????????? Picking the right tool for the job

?????????????? Differentiating generative AI from traditional ML

?????????????? More dogs and GANs

?????????????? The evolution of generative AI

?????????????? Driving the field forward

?????????????? Transformers

?????????????? Generative AI breakthroughs

?????????????? Developing Generative AI Models

?????????????? Model design and data collection

?????????????? Research and design

?????????????? A dataset windfall?

?????????????? Model training

?????????????? Factors impacting model training

?????????????? Techniques for developing specialized models

?????????????? Model evaluation

?????????????? PixAIr animation

?????????????? Helping Intellibrand evaluate

?????????????? Using AI Models and Generated Content Responsibly

?????????????? Evaluating and mitigating social bias

?????????????? Sources of bias

?????????????? Detecting and mitigating bias

?????????????? Copyright and ownership

?????????????? Legal and privacy considerations

?????????????? Identifying IP ownership claims

?????????????? Responsible generative AI applications

?????????????? Usage principles

?????????????? Implementing responsible AI practices

?????????????? Artificial general intelligence (AGI)

?????????????? AGI characteristics

?????????????? AGI outcomes

?????????????? Getting Ready for the Age of Generative AI

?????????????? Bringing new AI into old workflows

?????????????? Leveraging generative AI for data science

?????????????? Designing a new marketing campaign

?????????????? Progress in generative AI

?????????????? To open source or not to open source

?????????????? Super Duper GANs

?????????????? Preparing for a future of generative AI

?????????????? Generative AI in education

?????????????? The value of skills in an AI world

?

Understanding Artificial Intelligence????????

?????????????? What is Artificial Intelligence (AI)?

?????????????? What is Artificial Intelligence?

?????????????? The big question

?????????????? Narrow or General?

?????????????? What can AI do?

?????????????? AI at work

?????????????? AI in action

?????????????? Areas and related disciplines of AI

?????????????? How does AI learn?

?????????????? Listen and learn

?????????????? Tasks AI can solve

?????????????? Algorithms and AI systems demystified

?????????????? Inside a pizza AI system

?????????????? Unmasking a hotel booking AI system

?????????????? Acquiring data

?????????????? The AI and Internet-of-Things (IoT) symbiosis

?????????????? Structured or unstructured?

?????????????? Training and classifying with penguins

?????????????? The unsupervised intruder

?????????????? Deep Learning is here to stay

?????????????? Interacting with the Environment

?????????????? Robots, vision and natural language mix-up

?????????????? The True-False challenge of things AI can do

?????????????? Harnessing AI in Organizations

?????????????? Establishing an AI culture

?????????????? Four ingredients to AI-driven organizations

?????????????? Home, secured home

?????????????? Data strategy, resources, and people

?????????????? Infrastructure dilemmas

?????????????? The "zen" of MLOps

?????????????? Team building!

?????????????? Is your deployed AI system successful?

?????????????? Performance stories

?????????????? An academic Proof-of-Concept (PoC)

?????????????? Challenges and success stories

?????????????? Ways to foster an AI culture

?????????????? Paola and the fashion project

?????????????? The human side of AI

?????????????? Democratizing Artificial Intelligence

?????????????? Best practices for AI democratization

?????????????? Opening the Open Data doors

?????????????? Explainability and interpretability

?????????????? A tree inside the white-box

?????????????? Explaining wine quality

?????????????? Unboxing the SHAP

?????????????? Social challenges: ethics, fairness and privacy

?????????????? Data sharing for AI progress

?????????????? Biased or unbiased behavior?

?????????????? The biased vehicle

?????????????? Social challenges: the future of AI

?????????????? The thousand faces of sustainable AI

?????????????? Paola and the human side of AI

?????????????? One journey ends, another begins

Introduction to LLMs in Python??

with Large Language Models (LLMs)

?????????????? Introduction to large language models (LLMs)

?????????????? Using a pipeline for summarization

?????????????? Cleaning up replies

?????????????? Using pre-trained LLMs

?????????????? Generating text

?????????????? Translating text

?????????????? Understanding the transformer

?????????????? Identify the transformer

?????????????? Using the correct model structure

?????????????? Fine-tuning LLMs

?????????????? Preparing for fine-tuning

?????????????? Tokenizing text

?????????????? Mapping tokenization

?????????????? Fine-tuning through training

?????????????? Setting up training arguments

?????????????? Setting up the trainer

?????????????? Using the fine-tuned model

?????????????? Fine-tuning approaches

?????????????? Transfer learning with one-shot learning

?????????????? Transfer learning approaches

?????????????? Evaluating LLM performance

?????????????? The evaluate library

?????????????? Loading metrics with evaluate

?????????????? Describing metrics

?????????????? Using evaluate metrics

?????????????? Metrics for language tasks: perplexity and BLEU

?????????????? Evaluating perplexity

?????????????? BLEU translations

?????????????? Metrics for language tasks: ROUGE, METEOR, EM

?????????????? Evaluating with ROUGE

?????????????? Evaluating with METEOR

?????????????? Evaluating with EM

?????????????? Safeguarding LLMs

?????????????? Checking toxicity

?????????????? Evaluating regard

?

Intermediate ChatGPT???

?????????????? Understanding GPT Model Architecture

?????????????? Introduction

?????????????? Advancements of transformer architecture

?????????????? Features of RNNs versus transformers

?????????????? Tokenization and Transformers

?????????????? The role of decoders

?????????????? Encoding versus Decoding

?????????????? GPT models and training LLMs

?????????????? Components of the GPT Model

?????????????? Exploring fine-tuning

?????????????? Advanced prompt crafting

?????????????? Negotiating with an FBI agent

?????????????? XML basics

?????????????? Negotiate a raise

?????????????? Powerful storytelling

?????????????? Structuring a story with the PAIPS framework

?????????????? Content creation with the PFS principle

?????????????? Finding your next big idea

?????????????? Using thinking tags

?????????????? Prompts for startup success

?????????????? Prompt power

?????????????? Advanced Functions of ChatGPT

?????????????? Custom instructions

?????????????? Benefits of custom instructions

?????????????? Crafting effective custom instructions

?????????????? Building and deploying custom GPTs

?????????????? Custom GPTs versus Custom Instructions

?????????????? Configuring "EcoGuide"

?????????????? The future of ChatGPT

Implementing AI Solutions in Business????

with AI

?????????????? Overview of AI

?????????????? Do you know AI?

?????????????? The phases of an AI solution

?????????????? 10,000 ft view: how does AI work?

?????????????? Still know AI?

?????????????? Generative AI vs. Other AI

?????????????? Components of an AI solution

?????????????? Responsible AI

?????????????? Why is Responsible AI important?

?????????????? Common Principles of Responsible AI

?????????????? Assessing risk for a new AI solution

?????????????? Benefits, Limitations, and Use Cases of AI

?????????????? The benefits of AI for a business

?????????????? Pillars of AI Value

?????????????? Business value or nice to have?

?????????????? Limitations of AI

?????????????? Identify limitations of AI

?????????????? Limitation or benefit?

?????????????? Human-in-the-loop

?????????????? Use cases for AI

?????????????? Common business use cases

?????????????? AI advantage in a scenario

?????????????? Identify a use case for AI

?????????????? Principles of a good use case

?????????????? Classify use cases

?????????????? Deconstructing a task

?????????????? Building a Proof of Concept

with a POC

?????????????? Phases of a POC

?????????????? What makes a successful POC?

?????????????? Questions to clarify goals

?????????????? Data

?????????????? Importance of data

?????????????? Profiling and evaluating data

?????????????? Biased data

?????????????? Infrastructure

?????????????? Questions about the POC infrastructure

?????????????? The AI development environment

?????????????? Buy or build?

?????????????? Building the project team

?????????????? Types of roles

?????????????? Responsibilities

?????????????? Beyond the POC

?????????????? Measuring the POC

?????????????? What makes a POC successful?

?????????????? Classifying metrics in e-commerce scenario

?????????????? Assessing the POC

?????????????? Components for scaling

?????????????? Balancing value and feasibility

?????????????? Make a decision about scaling

?????????????? Considerations for full implementation

?????????????? Focus areas for a full implementation

?????????????? Monitoring and model drift

?????????????? DevOps, MLOps, or compliance?

?????????????? Adoption and skilling

?????????????? Reasons for no financial benefits

?????????????? Resistance to AI

?????????????? Benefits for change management, culture, and upskilling

?

Introduction to Deep Learning in Python

?????????????? Basics of deep learning and neural networks

?????????????? Introduction to deep learning

?????????????? Comparing neural network models to classical regression models

?????????????? Forward propagation

?????????????? Coding the forward propagation algorithm

?????????????? Activation functions

?????????????? The Rectified Linear Activation Function

?????????????? Applying the network to many observations/rows of data

?????????????? Deeper networks

?????????????? Forward propagation in a deeper network

?????????????? Multi-layer neural networks

?????????????? Representations are learned

?????????????? Levels of representation

?????????????? Optimizing a neural network with backward propagation

?????????????? The need for optimization

?????????????? Calculating model errors

?????????????? Understanding how weights change model accuracy

?????????????? Coding how weight changes affect accuracy

?????????????? Scaling up to multiple data points

?????????????? Gradient descent

?????????????? Calculating slopes

?????????????? Improving model weights

?????????????? Making multiple updates to weights

?????????????? Backpropagation

?????????????? The relationship between forward and backward propagation

?????????????? Thinking about backward propagation

?????????????? Backpropagation in practice

?????????????? A round of backpropagation

?????????????? Building deep learning models with keras

?????????????? Creating a Keras model

?????????????? Understanding your data

?????????????? Specifying a model

?????????????? Compiling and fitting a model

?????????????? Compiling the model

?????????????? Fitting the model

?????????????? Classification models

?????????????? Understanding your classification data

?????????????? Last steps in classification models

?????????????? Using models

?????????????? Making predictions

?????????????? Fine-tuning keras models

?????????????? Understanding model optimization

?????????????? Diagnosing optimization problems

?????????????? Changing optimization parameters

?????????????? Model validation

?????????????? Evaluating model accuracy on validation dataset

?????????????? Early stopping: Optimizing the optimization

?????????????? Experimenting with wider networks

?????????????? Adding layers to a network

?????????????? Thinking about model capacity

?????????????? Experimenting with model structures

?????????????? Stepping up to images

?????????????? Building your own digit recognition model

?

Intermediate Deep Learning with PyTorch?????????????

?????????????? Training Robust Neural Networks

?????????????? PyTorch and object-oriented programming

?????????????? PyTorch Dataset

?????????????? PyTorch DataLoader

?????????????? PyTorch Model

?????????????? Optimizers, training, and evaluation

?????????????? Training loop

?????????????? Optimizers

?????????????? Model evaluation

?????????????? Vanishing and exploding gradients

?????????????? Initialization and activation

?????????????? Activations: ReLU vs. ELU

?????????????? Batch Normalization

?????????????? Images & Convolutional Neural Networks

?????????????? View Chapter DetailsStart Chapter

?????????????? Sequences & Recurrent Neural Networks

?????????????? Handling sequences with PyTorch

?????????????? Generating sequences

?????????????? Sequential Dataset

?????????????? Recurrent Neural Networks

?????????????? Sequential architectures

?????????????? Building a forecasting RNN

?????????????? LSTM and GRU cells

?????????????? RNN vs. LSTM vs. GRU

?????????????? LSTM network

?????????????? GRU network

?????????????? Training and evaluating RNNs

?????????????? RNN training loop

?????????????? Evaluating forecasting models

?????????????? Multi-Input & Multi-Output Architectures

?????????????? Multi-input models

?????????????? Two-input dataset

?????????????? Two-input model

?????????????? Training two-input model

?????????????? Multi-output models

?????????????? Two-output Dataset and DataLoader

?????????????? Two-output model architecture

?????????????? Training multi-output models

?????????????? Evaluation of multi-output models and loss weighting

?????????????? Multi-output model evaluation

?????????????? Loss weighting