Training Program Topics - List 4

Working with Hugging Face????????

with Hugging Face

?????????????? Introduction to Hugging Face

?????????????? What are Large Language Models?

?????????????? Use cases for Hugging Face

?????????????? Transformers and the Hub

?????????????? Transformer components

?????????????? Searching the Hub with Python

?????????????? Saving a model

?????????????? Working with datasets

?????????????? Inspecting datasets

?????????????? Loading datasets

?????????????? Manipulating datasets

?????????????? Building Pipelines with Hugging Face

?????????????? Pipelines with Hugging Face

with pipelines

?????????????? Using AutoClasses

?????????????? Comparing models with the pipeline

?????????????? NLP and tokenization

?????????????? Normalizing text

?????????????? Comparing tokenizer output

?????????????? Text classification

?????????????? Grammatical correctness

?????????????? Question Natural Language Inference

?????????????? Zero-shot classification

?????????????? Summarization

?????????????? Summarizing long text

?????????????? Using min_length and max_length

?????????????? Summarizing several inputs

?????????????? Building Pipelines for Image and Audio

?????????????? Processing and classifying images

?????????????? Processing image data

?????????????? Creating an image classifier

?????????????? What about the original image?

?????????????? Question answering and multi-modal tasks

?????????????? Document question and answering

?????????????? Visual question and answering

?????????????? Audio classification

?????????????? Resampling audio files

?????????????? Filtering out audio files

?????????????? Classifying audio files

?????????????? Automatic speech recognition

?????????????? Instantiating an ASR pipeline

?????????????? Word error rate

?????????????? Iterating over a dataset

?????????????? Fine-tuning and Embeddings

?????????????? Fine-tuning a model

?????????????? Preparing a dataset

?????????????? Building the trainer

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

?????????????? Text generation

?????????????? The process of generating text

?????????????? Generating text from a text prompt

?????????????? Generating a caption for an image

?????????????? Embeddings

?????????????? Use cases for embeddings

?????????????? Benefits and challenges of embeddings

?????????????? Generate embeddings for a sentence

?????????????? Semantic search

?????????????? Semantic search versus keyword search

?????????????? Using semantic search

?

Retrieval Augmented Generation (RAG) with LangChain??

?????????????? Building RAG Applications with LangChain

?????????????? Loading Documents for RAG with LangChain

?????????????? Loading PDF files for RAG

?????????????? Loading HTML files for RAG

?????????????? Text splitting, embeddings, and vector storage

with text splitting

?????????????? Recursively splitting documents

?????????????? Embedding and storing documents

?????????????? Building an LCEL retrieval chain

?????????????? Creating the retrieval prompt

?????????????? Building the retrieval chain

?????????????? Improving the RAG Architecture

?????????????? Loading and splitting code files

?????????????? Loading code files

?????????????? Splitting Python files

?????????????? Advanced splitting methods

?????????????? Splitting by tokens

?????????????? Splitting semantically

?????????????? Optimizing document retrieval

?????????????? Sparse vs. dense retrieval

?????????????? Understanding BM25

?????????????? Sparse retrieval with BM25

?????????????? Introduction to RAG evaluation

?????????????? Ragas context precision evaluation

?????????????? Ragas faithfulness evaluation

?????????????? String evaluation

?????????????? Introduction to Graph RAG

?????????????? From vectors to graphs

?????????????? Creating graph documents

?????????????? Getting to know graphs

?????????????? Storing and querying documents

?????????????? Building-up your graph database

?????????????? Querying your graph database

?????????????? Creating the Graph RAG chain

?????????????? A journey through the Graph RAG system

?????????????? Chaining, Graph RAG style!

?????????????? Improving graph retrieval

?????????????? Graph RAG with filtering

?????????????? Validating Cypher queries

?????????????? Creating a Cypher few-shot prompt

Generative AI for Business???????????

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

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

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

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

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

?????????????? Key milestones

?????????????? Generative AI Breakthroughs

?????????????? Innovations that led to Generative AI

?????????????? The progress of generative AI

?????????????? The key players

?????????????? The open-source question

?????????????? Open-source advantages are widespread

?????????????? Accelerators vs. decelerators

?????????????? Generative AI Solutions

?????????????? Enhancing workflows with AI

?????????????? Augment, Co-create or Replace?

?????????????? Generative AI for marketing

?????????????? Impact on business functions

?????????????? Responsible AI: Social bias, copyright, ownership

?????????????? Detection and mitigation techniques

?????????????? Copyright and legal implications

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

?????????????? Uneven impact

?????????????? Knowledge Explosion

?????????????? Leading change in AI era

?????????????? Skills in an AI World

?????????????? Preparing your company

?????????????? The Building Blocks of Transformer Models

?????????????? Transformers with PyTorch

?????????????? Breaking down the Transformer

?????????????? PyTorch Transformers

?????????????? Embedding and positional encoding

?????????????? Creating input embeddings

?????????????? Creating positional encodings

?????????????? Multi-head self-attention

?????????????? Implementing multi-head attention

?????????????? Starting the MultiHeadAttentionClass

?????????????? Adding methods to the MultiHeadAttention class

?????????????? Building Transformer Architectures

?????????????? Encoder transformers

?????????????? Feed-forward sublayers

?????????????? The encoder transformer layer

?????????????? The encoder transformer body

?????????????? Adding the transformer head

?????????????? Decoder transformers

?????????????? Designing a mask for self-attention

?????????????? The decoder layer

?????????????? Completing the decoder transformer

?????????????? Encoder-decoder transformers

?????????????? Adding cross-attention to the decoder layer

?????????????? Constructing the encoder-decoder transformer

Introduction to Microsoft Copilot?????????????

?????????????? Overview of Microsoft Copilot

?????????????? Understanding Microsoft Copilot

?????????????? Copilot vs Copilot

?????????????? Copilot system components

?????????????? Microsoft Copilot

?????????????? Prompting in Copilot

?????????????? Copilot in Microsoft Edge

?????????????? Copilot features

?????????????? Microsoft 365 Copilot

?????????????? Using Copilot with your documents

?????????????? Content or context?

?????????????? Getting team updates

?????????????? Working with Microsoft 365 Copilot

?????????????? Copilot for Microsoft Outlook and Teams

?????????????? Copilot features in Outlook

?????????????? Business Chat capabilities

?????????????? Copilot for Microsoft Word

?????????????? Initiating Copilot in Word

?????????????? Edit in Word with Copilot

?????????????? Copilot for Microsoft PowerPoint

?????????????? Creating presentations with Copilot

?????????????? Copilot features in PowerPoint

?????????????? Copilot for Microsoft Excel

?????????????? Using Copilot on your data

?????????????? Using Copilot in Microsoft Excel

?????????????? Copilot features in Excel

?????????????? Copilot Best Practices and Responsible AI

?????????????? Productivity with Copilot for Microsoft 365

?????????????? What can Business Chat do?

?????????????? Classify Copilot tasks

?????????????? Enabling individuals across an organization

?????????????? Copilot for marketing

?????????????? Copilot Lab features

?????????????? Responsible AI: Security and Compliance

?????????????? Defining compliance

?????????????? Principles of RAI framework

Reinforcement Learning with Gymnasium in Python?????????

?????????????? Introduction to Reinforcement Learning

?????????????? Fundamentals of reinforcement learning

?????????????? What is Reinforcement Learning?

?????????????? RL vs. other ML sub-domains

?????????????? Scenarios for applying RL

?????????????? Navigating the RL framework

?????????????? RL interaction loop

?????????????? Episodic and continuous RL tasks

?????????????? Calculating discounted returns for agent strategies

?????????????? Interacting with Gymnasium environments

?????????????? Setting up a Mountain Car environment

?????????????? Visualizing the Mountain Car Environment

?????????????? Interacting with the Frozen Lake environment

?????????????? Model-Based Learning

?????????????? Markov Decision Processes

?????????????? Custom Frozen Lake MDP components

?????????????? Exploring state and action spaces

?????????????? Transition probabilities and rewards

?????????????? Policies and state-value functions

?????????????? Defining a deterministic policy

?????????????? Computing state-values for a policy

?????????????? Comparing policies

?????????????? Action-value functions

?????????????? Computing Q-values

?????????????? Improving a policy

?????????????? Policy iteration and value iteration

?????????????? Applying policy iteration for optimal policy

?????????????? Implementing value iteration

?????????????? Model-Free Learning

?????????????? Monte Carlo methods

?????????????? Episode generation for Monte Carlo methods

?????????????? Implementing first-visit Monte Carlo

?????????????? Implementing every-visit Monte Carlo

?????????????? Temporal difference learning

?????????????? Implementing the SARSA update rule

?????????????? Solving 8x8 Frozen Lake with SARSA

?????????????? Q-learning

?????????????? Implementing Q-learning update rule

?????????????? Solving 8x8 Frozen Lake with Q-learning

?????????????? Evaluating policy on a slippery Frozen Lake

?????????????? Advanced Strategies in Model-Free RL

?????????????? Expected SARSA

?????????????? Expected SARSA update rule

?????????????? Applying Expected SARSA

?????????????? Double Q-learning

?????????????? Implementing double Q-learning update rule

?????????????? Applying double Q-learning

?????????????? Balancing exploration and exploitation

?????????????? Defining epsilon-greedy function

?????????????? Solving CliffWalking with epsilon greedy strategy

?????????????? Solving CliffWalking with decayed epsilon-greedy strategy

?????????????? Multi-armed bandits

?????????????? Creating a multi-armed bandit

?????????????? Solving a multi-armed bandit

?????????????? Assessing convergence in a multi-armed bandit

?????????????? The Essentials of LangChain agents

?????????????? Agents in LangChain

?????????????? Creating a ReAct agent

?????????????? Components of LangChain agents

?????????????? Building custom tools

?????????????? Create a tool for math calculations

?????????????? Integrating custom tools and queries

?????????????? Conversation with a ReAct agent

?????????????? Conversation setup

?????????????? Ask questions about conversation history

?????????????? Building Chatbots with LangGraph

?????????????? Building graphs for chatbots

?????????????? Graph and agent states

?????????????? Adding nodes and edges

?????????????? Generating chatbot responses

?????????????? Queries and responses

?????????????? Generate a graph diagram

?????????????? Adding external tools to a chatbot

?????????????? Build a Wikipedia tool

?????????????? Add a tool to a graph

?????????????? Adding memory and conversation

?????????????? Visualize the graph and return tool outputs

?????????????? Adding graph memory

?????????????? Using graph memory for conversation

?????????????? Build Dynamic Chat Agents

?????????????? Defining multiple tools

?????????????? Build a tool that invokes the LLM

?????????????? Build a tool with Python code

?????????????? Binding multiple tools

?????????????? Defining nodes and edges for flexible function calling

?????????????? Define a function that stops the chatbot

?????????????? Create a function to return an LLM response

?????????????? Create the graph workflow for multiple tools

?????????????? Organize chatbot outputs with memory

?????????????? Configure outputs for multiple tools

?????????????? Enable multi-turn conversation with memory

Artificial Intelligence (AI) Strategy?????????????

?????????????? Fundamentals of AI Strategy

?????????????? Different types of strategies

?????????????? Charting the course

?????????????? Which strategy?

?????????????? Pillars of an Effective AI Strategy

?????????????? Navigating the strategic water

?????????????? The pillars of AI strategy

?????????????? Role of an AI Strategist

?????????????? Onboarding an AI strategist

?????????????? A successful AI strategist

?????????????? Designing a Winning AI Strategy

?????????????? When should one think of AI?

?????????????? Adapting to the AI revolution

?????????????? Understanding software paradigms

?????????????? Uncovering core business drivers

?????????????? Foundations of a successful AI plan

?????????????? AI suitability test

?????????????? Distinguishing AI-friendly scenarios

?????????????? Setting realistic business goals

?????????????? AI goals with the SMART lens

?????????????? A test of time and ambition

?????????????? Assessing the ROI of an AI initiative

?????????????? AI's dual-faced ROI

?????????????? Tracing revenue roots

?????????????? Components of AI Strategy

?????????????? Building an AI culture

?????????????? Diverse AI attempts

?????????????? Exploration & experimentation

?????????????? Building high-performing AI teams

?????????????? Finding the gap

?????????????? The essential trio

?????????????? Getting the right data

?????????????? Getting the right mix

?????????????? Quality saves cost

?????????????? AI risk assessment

?????????????? Privacy and ethics

?????????????? Compliance with regulations

?????????????? Compliance

?????????????? AI project lifecycle

?????????????? Time for Action

?????????????? It all starts with a PoC

?????????????? Decision points for AI diagnostic tool

?????????????? Evaluating essentials of an AI initiative

?????????????? Scaling beyond PoCs

?????????????? Sorting AI scaling obstacles

?????????????? Scaling an AI system

?????????????? MLOps

?????????????? Navigating MLOps

?????????????? Sorting architectural practices

?????????????? Barriers to adoption

?????????????? Assigning roles in AI transformation

?????????????? Blueprint for AI transformation

?????????????? It's time to !

Introduction to Deep Learning with Keras?????????????

?????????????? Introducing Keras

?????????????? What is Keras?

?????????????? Describing Keras

?????????????? Would you use deep learning?

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

?????????????? Hello nets!

?????????????? Counting parameters

?????????????? Build as shown!

?????????????? Surviving a meteor strike

?????????????? Specifying a model

?????????????? Training

?????????????? Predicting the orbit!

?????????????? Going Deeper

?????????????? Binary classification

?????????????? Exploring dollar bills

?????????????? A binary classification model

?????????????? Is this dollar bill fake ?

?????????????? Multi-class classification

?????????????? A multi-class model

?????????????? Prepare your dataset

?????????????? Training on dart throwers

?????????????? Softmax predictions

?????????????? Multi-label classification

?????????????? An irrigation machine

?????????????? Training with multiple labels

?????????????? Keras callbacks

?????????????? The history callback

?????????????? Early stopping your model

?????????????? A combination of callbacks

?????????????? Improving Your Model Performance

?????????????? Is the model overfitting?

?????????????? Do we need more data?

?????????????? Activation functions

?????????????? Different activation functions

?????????????? Comparing activation functions

?????????????? Comparing activation functions II

?????????????? Batch size and batch normalization

?????????????? Changing batch sizes

?????????????? Batch normalizing a familiar model

?????????????? Batch normalization effects

?????????????? Hyperparameter tuning

?????????????? Preparing a model for tuning

?????????????? Tuning the model parameters

?????????????? Training with cross-validation

?????????????? Advanced Model Architectures

?????????????? Tensors, layers, and autoencoders

?????????????? It's a flow of tensors

?????????????? Neural separation

?????????????? Building an autoencoder

?????????????? De-noising like an autoencoder

?????????????? Intro to CNNs

?????????????? Building a CNN model

?????????????? Looking at convolutions

?????????????? Preparing your input image

?????????????? Using a real world model

?????????????? Intro to LSTMs

?????????????? Text prediction with LSTMs

?????????????? Build your LSTM model

?????????????? Decode your predictions

?????????????? Test your model!

?????????????? You're done!

Explainable AI in Python

?????????????? Foundations of Explainable AI

?????????????? Introduction to explainable AI

?????????????? Decision trees vs. neural networks

?????????????? Model-agnostic vs. model-specific explainability

?????????????? Explainability in linear models

?????????????? Computing feature impact with linear regression

?????????????? Computing feature impact with logistic regression

?????????????? Explainability in tree-based models

?????????????? Computing feature importance with decision trees

?????????????? Computing feature importance with random forests

?????????????? Model-Agnostic Explainability

?????????????? Permutation importance

?????????????? Permutation importance for MLPClassifier

?????????????? Coefficients vs. permutation importance

?????????????? SHAP explainability

?????????????? Finding key medical charge predictors with SHAP

?????????????? Finding key heart disease predictors with SHAP

?????????????? SHAP kernel explainer

?????????????? Kernel explainer for MLPRegressor

?????????????? Kernel explainer for MLPClassifier

?????????????? SHAP vs. model-specific approaches

?????????????? Visualizing SHAP explainability

?????????????? Feature Importance plots for admissions analysis

?????????????? Analyzing feature effects with beeswarm plots

?????????????? Assessing impact with partial dependence plots

?????????????? Local Explainability

?????????????? Local explainability with SHAP

?????????????? Global vs. local explainability

?????????????? SHAP for explaining income levels

?????????????? Local explainability with LIME

?????????????? Interpreting regressors locally

?????????????? Interpreting classifiers locally

?????????????? Text and image explainability with LIME

?????????????? Explaining sentiment analysis predictions

?????????????? Explaining food image predictions

?????????????? Advanced topics in explainable AI

?????????????? Explainability metrics

?????????????? Evaluating SHAP explanation consistency

?????????????? Evaluating faithfulness with LIME

?????????????? Explaining unsupervised models

?????????????? Feature impact on cluster quality

?????????????? Feature importance in clustering with ARI

?????????????? Explaining chat-based generative AI models

?????????????? Chain-of-thought to discover reasoning

?????????????? Self-consistency to assess confidence

?

Deep Learning for Images with PyTorch??

?????????????? Image Classification with CNNs

?????????????? Binary and multi-class image classification

?????????????? The number of classes

?????????????? Binary classification model

?????????????? Multi-class classification model

?????????????? Convolutional layers for images

?????????????? RGB, grayscale, or alpha?

?????????????? Adding a new convolutional layer

?????????????? Creating a sequential block

?????????????? Working with pre-trained models

?????????????? Save and load a model

?????????????? Loading a pre-trained model

?????????????? Image classification with ResNet

?????????????? Object Recognition

?????????????? Bounding boxes

?????????????? Object recognition

?????????????? Image tensors

?????????????? Drawing a bounding box

?????????????? Evaluating object recognition models

?????????????? Calculate IoU

?????????????? Bounding boxes prediction

?????????????? Calculate NMS

?????????????? Object detection using R-CNN

?????????????? Pre-trained model backbone

?????????????? Classifier block

?????????????? Box regressor block

?????????????? Region network proposals with Faster R-CNN

?????????????? Anchor generator

?????????????? Faster R-CNN model

?????????????? Define losses for RPN and R-CNN

?????????????? Image Segmentation

?????????????? Introduction to image segmentation

?????????????? Segmentation types

?????????????? Creating binary masks

?????????????? Segmenting image with a mask

?????????????? Instance segmentation with Mask R-CNN

?????????????? Segmenting with pre-trained Mask R-CNN

?????????????? Analyzing model output

?????????????? Displaying soft masks

?????????????? Semantic segmentation with U-Net

?????????????? Building a U-Net: layers definitions

?????????????? Building a U-Net: forward method

?????????????? Running semantic segmentation

?????????????? Panoptic segmentation

?????????????? Setup up semantic masks

?????????????? Overlay instance masks

?????????????? Image Generation with GANs

?????????????? Introduction to GANs

?????????????? GANs intuition

?????????????? Generator

?????????????? Discriminator

?????????????? Deep Convolutional GAN

?????????????? Convolutional Generator

?????????????? Convolutional Discriminator

?????????????? Training GANs

?????????????? Generator loss

?????????????? Discriminator loss

?????????????? Training loop

?????????????? Evaluating GANs

?????????????? Generating images

?????????????? Fréchet Inception Distance

?

Large Language Models for Business????????

?????????????? The Rise of LLMs in the Business Realm

?????????????? Introduction to LLMs

?????????????? Decoding the "large" in LLMs

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

?????????????? LLMs in your business

?????????????? LLMs in the business world

?????????????? Unraveling Workspace Buddy

?????????????? The journey of LLMs

?????????????? LLMs through time

?????????????? LLMs and creativity

?????????????? LLMs in action

?????????????? LLMs Solutions

?????????????? Enhancing existing workflows with LLMs

?????????????? The right tool for the job

?

?????????????? When should you trust LLMs

?????????????? LLMs that Advertise

?????????????? Troubleshooting LLMs

?????????????? Business, operational and ethical concerns

?????????????? To train or not to train

?????????????? The LLM concerns

?????????????? Leading change in an AI era

Deep Learning for Text with PyTorch????????

?????????????? Introduction to Deep Learning for Text with PyTorch

?????????????? Introduction to preprocessing for text

?????????????? Word frequency analysis

?????????????? Preprocessing text

?????????????? Encoding text data

?????????????? One-hot encoded book titles

?????????????? Bag-of-words for book titles

?????????????? Applying TF-IDF to book descriptions

?????????????? Introduction to building a text processing pipeline

?????????????? Shakespearean language preprocessing pipeline

?????????????? Shakespearean language encoder

?????????????? Text Classification with PyTorch

?????????????? Overview of Text Classification

?????????????? Embedding in PyTorch

?????????????? Categorizing text classification tasks

?????????????? Convolutional neural networks for text classification

?????????????? Build a CNN model for text

?????????????? Train a CNN model for text

?????????????? Testing the Sentiment Analysis CNN Model

?????????????? Recurrent neural networks for text classification

?????????????? Building an RNN model for text

?????????????? Building an LSTM model for text

?????????????? Building a GRU model for text

?????????????? Evaluation metrics for text classification

?????????????? Evaluating RNN classification models

?????????????? Evaluating the model's performance

?????????????? Comparing models

?????????????? Text Generation with PyTorch

?????????????? Introduction to text generation

?????????????? Creating a RNN model for text generation

?????????????? Text generation using RNN - Training and Generation

?????????????? Generative adversarial networks for text generation

?????????????? Building a generator and discriminator

?????????????? Training a GAN model

?????????????? Pre-trained models for text generation

?????????????? Text completion with pre-trained GPT-2 models

?????????????? Language translation with pretrained PyTorch model

?????????????? Evaluation metrics for text generation

?????????????? Evaluating pretrained text generation model

?????????????? Understanding text generation metrics

?????????????? Advanced Topics in Deep Learning for Text with PyTorch

?????????????? Transfer learning for text classification

?????????????? Transfer learning using BERT

?????????????? Evaluating the BERT model

?????????????? Transformers for text processing

?????????????? Creating a transformer model

?????????????? Training and testing the Transformer model

?????????????? Attention mechanisms for text processing

?????????????? Creating a RNN model with attention

?????????????? Training and testing the RNN model with attention

?????????????? Adversarial attacks on text classification models

?????????????? Adversarial attack classification

?????????????? Safeguarding AI at PyBooks

?

Understanding the EU AI Act??????

?????????????? Introduction to the EU AI Act

?????????????? The EU AI Act

?????????????? AI Act global reach

?????????????? Growing AI

?????????????? Risk classification

?????????????? Unacceptable risk

?????????????? Providers versus deployers

?????????????? Limited exceptions

?????????????? Limited-risk AI systems

?????????????? Building the pyramid

?????????????? EU AI Act Obligations

?????????????? General purpose AI models

?????????????? AI systems versus models

?????????????? GPAI models

?????????????? Classifying high-risk AI systems

?????????????? Identifying high-risk

?????????????? Embedded AI oversight

?????????????? High-risk provider obligations

?????????????? Conformity

?????????????? Classifying conformity and compliance

?????????????? High-risk deployer obligations

?????????????? Deployers' duties

?????????????? Changing roles

LLMOps Concepts???????????

?????????????? Introduction to LLMOps & Ideation Phase

?????????????? Overview of LLMOps

?????????????? LLMOps versus MLOps

?????????????? Relevancy of LLMOps

?????????????? Lifecycle of LLMs

?????????????? The purpose of lifecycle phases

?????????????? The application lifecycle

?????????????? Ideation phase

?????????????? Data sourcing

?????????????? Proprietary versus open-source models

?????????????? Selecting the right base model

?????????????? Development Phase

?????????????? Prompt engineering

?????????????? The importance of prompt engineering

?????????????? Trying out prompt engineering

?????????????? Keeping track of prompts

?????????????? Chains and agents

?????????????? The difference between agents and chains

?????????????? Choosing the right architecture

?????????????? RAG versus fine-tuning

?????????????? The RAG workflow

?????????????? Compare RAG with fine-tuning

?????????????? Testing

?????????????? Choosing the right metric

?????????????? The importance of testing

?????????????? Operational Phase

?????????????? Deployment

?????????????? The need for CI/CD

?????????????? The right scaling strategy

?????????????? Monitoring and observability

?????????????? Monitoring your application

?????????????? Alert handling

?????????????? Cost management

?????????????? Prompt compression

?????????????? Making a cost prognosis

?????????????? Governance and security

?????????????? Prompt injection

?????????????? Mitigation strategies

?????????????? Data integrity and poisoning

?

Deep Reinforcement Learning in Python?

?????????????? Introduction to Deep Reinforcement Learning

?????????????? Introduction to deep reinforcement learning

?????????????? Environment and neural network setup

?????????????? DRL training loop

?????????????? Introduction to deep Q learning

?????????????? Deep learning and DQN

?????????????? The Q-Network architecture

?????????????? Instantiating the Q-Network

?????????????? The barebone DQN algorithm

?????????????? Barebone DQN action selection

?????????????? Barebone DQN loss function

?????????????? Training the barebone DQN

?????????????? Deep Q-learning

?????????????? DQN with experience replay

?????????????? The double-ended queue

?????????????? Experience replay buffer

?????????????? DQN with experience replay

?????????????? The complete DQN algorithm

?????????????? Epsilon-greediness

?????????????? Fixed Q-targets

?????????????? Implementing the complete DQN algorithm

?????????????? Double DQN

?????????????? Online network and target network in DDQN

?????????????? Training the double DQN

?????????????? Prioritized experience replay

?????????????? Prioritized experience replay buffer

?????????????? Sampling from the PER buffer

?????????????? DQN with prioritized experience replay

?????????????? Introduction to Policy Gradient Methods

?????????????? Introduction to policy gradient

?????????????? The policy network architecture

?????????????? Working with discrete distributions

?????????????? Policy gradient and REINFORCE

?????????????? Action selection in REINFORCE

?????????????? Training the REINFORCE algorithm

?????????????? Advantage Actor Critic

?????????????? Critic network

?????????????? Actor Critic loss calculations

?????????????? Training the A2C algorithm

?????????????? Proximal Policy Optimization and DRL Tips

?????????????? Proximal policy optimization

?????????????? The clipped probability ratio

?????????????? The clipped surrogate objective function

?????????????? Entropy bonus and PPO

?????????????? Entropy playground

?????????????? Training the PPO algorithm

?????????????? Batch updates in policy gradient

?????????????? Minibatch and DRL

?????????????? A2C with batch updates

?????????????? Hyperparameter optimization with Optuna

?????????????? Hyperparameter or not?

?????????????? Hands-on with Optuna

?