Pluralsight Path Deep Learning Literacy Practical Application 2022

Pluralsight Path Deep Learning Literacy Practical Application 2022

Name	Pluralsight Path. Deep Learning Literacy. Practical Application (2022)	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	1.9 GB
Total Files	486
Last Seen	2025-04-08 23:36
Hash	834B6201E4DFFFCAA866824648C67D3D42C40431

/
~i.txt	3.5 KB
/A1. Deep Learning Application for Healthcare (Colin Matthews, 2022)/
exercise.7z	7.2 MB
playlist.m3u	1.1 KB
~i.txt	1.5 KB
/1. Course Overview/
1. Course Overview.mp4	2.2 MB
1. Course Overview.vtt	2.0 KB
/.../2. Fundamentals of Deep Learning/
1. Welcome!.mp4	2.2 MB
1. Welcome!.vtt	3.0 KB
2. The Power of Deep Learning.mp4	4.4 MB
2. The Power of Deep Learning.vtt	3.8 KB
3. What Is Deep Learning.mp4	9.7 MB
3. What Is Deep Learning.vtt	10.5 KB
4. Advantages.mp4	4.6 MB
4. Advantages.vtt	4.1 KB
5. Disadvantages.mp4	4.5 MB
5. Disadvantages.vtt	4.0 KB
6. Case Study - Diagnostic Image Analysis.mp4	8.9 MB
6. Case Study - Diagnostic Image Analysis.vtt	7.4 KB
/3. Healthcare Data/
1. Module Overview.mp4	845.7 KB
1. Module Overview.vtt	1.1 KB
2. Introduction to Healthcare Data.mp4	10.2 MB
2. Introduction to Healthcare Data.vtt	8.9 KB
3. Medical Coding.mp4	6.3 MB
3. Medical Coding.vtt	7.6 KB
4. Document and Text Analysis.mp4	6.9 MB
4. Document and Text Analysis.vtt	6.5 KB
5. Image Analysis.mp4	9.7 MB
5. Image Analysis.vtt	8.3 KB
6. Device Data.mp4	8.6 MB
6. Device Data.vtt	8.0 KB
7. Case study - Automated Medical Coding.mp4	9.1 MB
7. Case study - Automated Medical Coding.vtt	8.9 KB
/.../4. Ethics in Healthcare AI/
1. Module Overview.mp4	581.1 KB
1. Module Overview.vtt	0.7 KB
2. Decision Support vs. Decision Making.mp4	5.5 MB
2. Decision Support vs. Decision Making.vtt	5.6 KB
3. Interpretability.mp4	5.1 MB
3. Interpretability.vtt	5.7 KB
4. Bias.mp4	5.1 MB
4. Bias.vtt	6.2 KB
5. Peer Review.mp4	7.0 MB
5. Peer Review.vtt	5.5 KB
6. Case study - Sepsis Algorithm.mp4	5.5 MB
6. Case study - Sepsis Algorithm.vtt	5.2 KB
7. Course Summary.mp4	633.4 KB
7. Course Summary.vtt	1.0 KB
/A2. Deep Learning Application for Marketing (Netta Tzin, 2022)/
exercise.7z	5.8 MB
playlist.m3u	2.4 KB
~i.txt	1.1 KB
/1. Course Overview/
1. Course Overview.mp4	3.6 MB
1. Course Overview.vtt	2.7 KB
/.../2. Introducing Deep Learning as Part of Artificial Intelligence and Its Usage in Digital Marketing/
1. Overview.mp4	1.2 MB
1. Overview.vtt	0.9 KB
2. Introducing Data Driven Marketing.mp4	5.4 MB
2. Introducing Data Driven Marketing.vtt	3.3 KB
3. Exploring Data Driven Trends in Marketing.mp4	15.8 MB
3. Exploring Data Driven Trends in Marketing.vtt	8.1 KB
4. Getting Started with Artificial Intelligence.mp4	5.8 MB
4. Getting Started with Artificial Intelligence.vtt	4.1 KB
5. Getting Acquainted with Machine Learning.mp4	4.9 MB
5. Getting Acquainted with Machine Learning.vtt	3.7 KB
6. Introducing Deep Learning.mp4	5.3 MB
6. Introducing Deep Learning.vtt	3.9 KB
7. Understanding Deep Learning Applications in Marketing.mp4	4.6 MB
7. Understanding Deep Learning Applications in Marketing.vtt	3.3 KB
8. Summary.mp4	1.1 MB
8. Summary.vtt	0.9 KB
/.../3. Exploring Deep Learning Applications in Marketing through Various Business Use-cases/
1. Overview.mp4	2.4 MB
1. Overview.vtt	1.7 KB
2. Introducing Customer Engagement Marketing.mp4	5.5 MB
2. Introducing Customer Engagement Marketing.vtt	3.5 KB
3. Explaining Conversational Artificial Intelligence.mp4	3.8 MB
3. Explaining Conversational Artificial Intelligence.vtt	2.8 KB
4. Understanding the Three Stages of Conversational Artificial Intelligence.mp4	14.3 MB
4. Understanding the Three Stages of Conversational Artificial Intelligence.vtt	9.7 KB
5. Integrating Deep Learning in Customer Engagement.mp4	3.1 MB
5. Integrating Deep Learning in Customer Engagement.vtt	2.2 KB
6. Introducing Personalized Customer Interactions.mp4	3.9 MB
6. Introducing Personalized Customer Interactions.vtt	2.5 KB
7. Understanding Generative Adversarial Networks.mp4	8.4 MB
7. Understanding Generative Adversarial Networks.vtt	5.5 KB
8. Utilizing Generative Adversarial Networks in Personalized Interactions.mp4	5.6 MB
8. Utilizing Generative Adversarial Networks in Personalized Interactions.vtt	3.1 KB
9. Summary.mp4	1.8 MB
9. Summary.vtt	1.3 KB
/A3. Deep Learning Application for Finance (Jaimin M, 2022)/
exercise.7z	3.4 MB
playlist.m3u	2.0 KB
~i.txt	1.6 KB
/1. Course Overview/
1. Course Overview.mp4	2.3 MB
1. Course Overview.vtt	1.4 KB
/.../2. Understanding the Applications of Deep Learning Algorithms in Finance World/
1. Discussing the Importance of Neural Network in Finance.mp4	6.3 MB
1. Discussing the Importance of Neural Network in Finance.vtt	4.5 KB
2. Importance of Back Propagation.mp4	5.2 MB
2. Importance of Back Propagation.vtt	3.7 KB
3. Understanding CNN and RNN.mp4	4.3 MB
3. Understanding CNN and RNN.vtt	3.1 KB
4. Risks and Problems Associated with Finance Industry.mp4	3.6 MB
4. Risks and Problems Associated with Finance Industry.vtt	3.4 KB
5. Summary.mp4	621.8 KB
5. Summary.vtt	0.7 KB
/.../3. Analyzing the Importance of Deep Learning Algorithm to Predict a Stock Price/
1. Challenges of RNN.mp4	3.6 MB
1. Challenges of RNN.vtt	3.2 KB
2. Overview of LSTM and GRU.mp4	2.5 MB
2. Overview of LSTM and GRU.vtt	1.8 KB
3. Factors Impacting a Stock.mp4	1.8 MB
3. Factors Impacting a Stock.vtt	1.6 KB
4. Demo - Input Data of Tesla Stock.mp4	10.8 MB
4. Demo - Input Data of Tesla Stock.vtt	5.0 KB
5. Demo - Exploratory Data Analysis.mp4	3.1 MB
5. Demo - Exploratory Data Analysis.vtt	1.7 KB
6. Demo - Preparing Training and Test Dataset.mp4	3.9 MB
6. Demo - Preparing Training and Test Dataset.vtt	2.1 KB
7. Summary.mp4	642.2 KB
7. Summary.vtt	0.6 KB
/.../4. Predicting Stock Price Using Stacked LSTM/
1. Parameters Impacting the Stock Price of Tesla.mp4	3.8 MB
1. Parameters Impacting the Stock Price of Tesla.vtt	2.9 KB
2. LSTM Advantages.mp4	1.0 MB
2. LSTM Advantages.vtt	1.0 KB
3. Challenges for Wider Acceptance of AI in Finance.mp4	2.7 MB
3. Challenges for Wider Acceptance of AI in Finance.vtt	2.2 KB
4. Demo - Training Stacked LSTM Model.mp4	6.8 MB
4. Demo - Training Stacked LSTM Model.vtt	3.1 KB
5. Demo - Evaluate the Model to Predict Future Price of Tesla Stock.mp4	5.6 MB
5. Demo - Evaluate the Model to Predict Future Price of Tesla Stock.vtt	2.8 KB
6. Summary.mp4	451.1 KB
6. Summary.vtt	0.5 KB
/A4. Deep Learning Application for Retail (Trent McMillan, 2022)/
exercise.7z	12.2 MB
playlist.m3u	1.0 KB
~i.txt	1.5 KB
/1. Course Overview/
1. Course Overview.mp4	3.1 MB
1. Course Overview.vtt	2.4 KB
/.../2. Deep Learning in Retail Applications/
1. Use Case - Customer Segmentation.mp4	8.5 MB
1. Use Case - Customer Segmentation.vtt	8.2 KB
2. Use Case - Inventory Management.mp4	5.1 MB
2. Use Case - Inventory Management.vtt	5.3 KB
3. Use Case - Product Image Classification.mp4	4.2 MB
3. Use Case - Product Image Classification.vtt	4.2 KB
4. Use Case - Price Optimization.mp4	4.9 MB
4. Use Case - Price Optimization.vtt	4.4 KB
5. Data Collection.mp4	6.7 MB
5. Data Collection.vtt	7.4 KB
6. Current Trends.mp4	5.8 MB
6. Current Trends.vtt	6.4 KB
/.../3. Case Study - Recommendation Systems/
1. Framing the Problem.mp4	6.8 MB
1. Framing the Problem.vtt	6.8 KB
2. Implementation.mp4	5.1 MB
2. Implementation.vtt	4.5 KB
3. Results.mp4	5.0 MB
3. Results.vtt	5.3 KB
4. Tips for Success.mp4	3.3 MB
4. Tips for Success.vtt	3.9 KB
/.../4. Case Study - Churn Prediction/
1. Framing the Problem.mp4	4.0 MB
1. Framing the Problem.vtt	3.9 KB
2. Implementation.mp4	13.9 MB
2. Implementation.vtt	10.5 KB
3. Results.mp4	9.5 MB
3. Results.vtt	6.5 KB
4. Tips for Success.mp4	3.5 MB
4. Tips for Success.vtt	3.8 KB
/5. Summary/
1. Summary.mp4	6.6 MB
1. Summary.vtt	7.4 KB
/B1. Implement Image Recognition with a Convolutional Neural Network (Pratheerth Padman, 2022)/
exercise.7z	13.9 MB
playlist.m3u	2.7 KB
~i.txt	1.6 KB
/1. Course Overview/
1. Course Overview.mp4	4.3 MB
1. Course Overview.vtt	2.5 KB
/.../2. Exploring and Preparing a Dataset for Image Recognition/
1. Course and Module Introduction.mp4	5.5 MB
1. Course and Module Introduction.vtt	4.1 KB
2. What Are We Trying to Solve.mp4	10.4 MB
2. What Are We Trying to Solve.vtt	4.5 KB
3. Demo - Setting up Your Environment.mp4	6.9 MB
3. Demo - Setting up Your Environment.vtt	3.9 KB
4. Demo - Organizing the Dataset.mp4	11.7 MB
4. Demo - Organizing the Dataset.vtt	5.6 KB
5. Demo - Exploring the Dataset.mp4	7.0 MB
5. Demo - Exploring the Dataset.vtt	4.0 KB
6. Demo - Preprocessing and Preparing the Dataset.mp4	10.8 MB
6. Demo - Preprocessing and Preparing the Dataset.vtt	7.1 KB
7. Summary and Up Next.mp4	1.5 MB
7. Summary and Up Next.vtt	1.4 KB
/.../3. Training a Convolutional Neural Network to Classify Images/
01. Module Introduction.mp4	1.5 MB
01. Module Introduction.vtt	1.4 KB
02. What Are Convolutional Neural Networks.mp4	4.2 MB
02. What Are Convolutional Neural Networks.vtt	3.0 KB
03. CNN - Convolutions.mp4	7.1 MB
03. CNN - Convolutions.vtt	5.9 KB
04. CNN - Activation.mp4	8.0 MB
04. CNN - Activation.vtt	6.4 KB
05. CNN - Pooling.mp4	2.1 MB
05. CNN - Pooling.vtt	2.0 KB
06. CNN - Classification.mp4	1.5 MB
06. CNN - Classification.vtt	1.3 KB
07. Demo - Creating the CNN Architecture.mp4	11.3 MB
07. Demo - Creating the CNN Architecture.vtt	6.4 KB
08. Demo - Training the Model.mp4	4.6 MB
08. Demo - Training the Model.vtt	2.3 KB
09. Demo - Performance Metrics - How Well Did Your Model Do.mp4	7.0 MB
09. Demo - Performance Metrics - How Well Did Your Model Do.vtt	4.0 KB
10. Summary and Up Next.mp4	1.8 MB
10. Summary and Up Next.vtt	1.6 KB
/.../4. Improving Performance of the Convolutional Neural Network/
01. Module Introduction.mp4	2.1 MB
01. Module Introduction.vtt	1.8 KB
02. Better Performance – When and How.mp4	2.9 MB
02. Better Performance – When and How.vtt	2.8 KB
03. Procuring Additional Training Data - Image Augmentation.mp4	7.8 MB
03. Procuring Additional Training Data - Image Augmentation.vtt	6.8 KB
04. Hyperparameter Tuning.mp4	4.6 MB
04. Hyperparameter Tuning.vtt	4.4 KB
05. Overfitting and Underfitting.mp4	5.7 MB
05. Overfitting and Underfitting.vtt	4.6 KB
06. Demo - Image Augmentation and Hyperparameter Tuning.mp4	18.0 MB
06. Demo - Image Augmentation and Hyperparameter Tuning.vtt	10.1 KB
07. What Is Transfer Learning.mp4	7.0 MB
07. What Is Transfer Learning.vtt	5.7 KB
08. Transfer Learning – When and How.mp4	6.0 MB
08. Transfer Learning – When and How.vtt	5.5 KB
09. Demo - Improving Performance through Transfer Learning.mp4	7.6 MB
09. Demo - Improving Performance through Transfer Learning.vtt	4.3 KB
10. Summary.mp4	1.8 MB
10. Summary.vtt	2.0 KB
/B2. Implement Text Auto Completion with LSTM (Biswanath Halder, 2022)/
exercise.7z	3.7 MB
playlist.m3u	2.0 KB
~i.txt	1.8 KB
/1. Course Overview/
1. Course Overview.mp4	3.1 MB
1. Course Overview.vtt	2.6 KB
/.../2. Long Short Term Memory Networks (LSTM)/
1. Overview.mp4	1.9 MB
1. Overview.vtt	1.7 KB
2. Introduction to Neural Networks.mp4	10.2 MB
2. Introduction to Neural Networks.vtt	8.4 KB
3. Natural Language Text as Sequential Data.mp4	5.4 MB
3. Natural Language Text as Sequential Data.vtt	5.6 KB
4. Recurrent Neural Networks (RNNs).mp4	12.3 MB
4. Recurrent Neural Networks (RNNs).vtt	12.7 KB
5. Long Short-term Memory Networks (LSTMs).mp4	11.1 MB
5. Long Short-term Memory Networks (LSTMs).vtt	11.1 KB
6. Networks Architectures Using LSTMs.mp4	9.1 MB
6. Networks Architectures Using LSTMs.vtt	8.6 KB
7. Natural Language Generation Using LSTMs.mp4	6.0 MB
7. Natural Language Generation Using LSTMs.vtt	5.3 KB
8. Summary.mp4	1.8 MB
8. Summary.vtt	1.8 KB
/.../3. Data Preparation for Assisted Smart Writing/
1. Overview.mp4	1.7 MB
1. Overview.vtt	1.8 KB
2. Explore Enron Email Dataset.mp4	5.9 MB
2. Explore Enron Email Dataset.vtt	3.9 KB
3. Extract Plaintext Messages from Raw Email Data.mp4	9.1 MB
3. Extract Plaintext Messages from Raw Email Data.vtt	4.9 KB
4. Clean the Email Dataset.mp4	12.5 MB
4. Clean the Email Dataset.vtt	8.7 KB
5. Functions to Preprocess the Dataset.mp4	16.4 MB
5. Functions to Preprocess the Dataset.vtt	10.6 KB
6. Preprocess the Email Dataset.mp4	10.8 MB
6. Preprocess the Email Dataset.vtt	6.6 KB
7. Summary.mp4	1.1 MB
7. Summary.vtt	1.2 KB
/.../4. Implementation of Auto-completion for Assisted Smart Writing/
1. Overview.mp4	1.7 MB
1. Overview.vtt	1.5 KB
2. Network Architecture for Text Generation.mp4	8.5 MB
2. Network Architecture for Text Generation.vtt	7.4 KB
3. Tokenization, Vocabulary, and N-grams.mp4	13.5 MB
3. Tokenization, Vocabulary, and N-grams.vtt	10.4 KB
4. Handle Variable Sentence Lengths.mp4	8.7 MB
4. Handle Variable Sentence Lengths.vtt	6.3 KB
5. Predictors and Labels for Training.mp4	6.1 MB
5. Predictors and Labels for Training.vtt	4.1 KB
6. Build and Train the Model.mp4	10.2 MB
6. Build and Train the Model.vtt	6.5 KB
7. Generate Auto-complete Suggestions.mp4	10.3 MB
7. Generate Auto-complete Suggestions.vtt	7.2 KB
8. Summary.mp4	2.0 MB
8. Summary.vtt	1.9 KB
/B3. Implement Image Captioning with Recurrent Neural Networks (Abdul Rehman Yousaf, 2022)/
exercise.7z	27.3 MB
playlist.m3u	2.3 KB
~i.txt	1.7 KB
/1. Course Overview/
1. Course Overview.mp4	3.3 MB
1. Course Overview.vtt	2.0 KB
/.../2. Introducing Image Captioning/
1. Overview.mp4	6.8 MB
1. Overview.vtt	3.0 KB
2. What Is Image Captioning and Why Is It Important.mp4	6.2 MB
2. What Is Image Captioning and Why Is It Important.vtt	3.3 KB
3. Introducing the Business Case Study for Image-captioning.mp4	3.5 MB
3. Introducing the Business Case Study for Image-captioning.vtt	2.3 KB
4. Proposed Solutions for Image Captioning Case Study.mp4	10.1 MB
4. Proposed Solutions for Image Captioning Case Study.vtt	6.4 KB
5. Summary.mp4	1.4 MB
5. Summary.vtt	1.0 KB
/.../3. Preparing Data for Image Captioning Models/
1. Module and Project Overview.mp4	13.3 MB
1. Module and Project Overview.vtt	7.9 KB
2. Demo - Load and Explore the Dataset.mp4	26.3 MB
2. Demo - Load and Explore the Dataset.vtt	10.1 KB
3. Demo - Pre-processing the Images Data.mp4	22.7 MB
3. Demo - Pre-processing the Images Data.vtt	7.6 KB
4. Demo - Pre-processing the Captions Data.mp4	20.6 MB
4. Demo - Pre-processing the Captions Data.vtt	6.5 KB
5. Demo - Prepare Training Data Using Pre-processed Data.mp4	12.2 MB
5. Demo - Prepare Training Data Using Pre-processed Data.vtt	4.0 KB
6. Summary.mp4	1.1 MB
6. Summary.vtt	0.9 KB
/.../4. Building the Model for Image Captioning Using Tensorflow/
1. Overview.mp4	1.6 MB
1. Overview.vtt	1.2 KB
2. Demo - Build the Attention Model for Image-captioning Using TensorFlow.mp4	13.7 MB
2. Demo - Build the Attention Model for Image-captioning Using TensorFlow.vtt	5.7 KB
3. Demo - Implement CNN Encoder in TensorFlow.mp4	3.9 MB
3. Demo - Implement CNN Encoder in TensorFlow.vtt	1.4 KB
4. Demo - Implement RNN Decoder with Attention & Sentence Generator.mp4	7.1 MB
4. Demo - Implement RNN Decoder with Attention & Sentence Generator.vtt	2.4 KB
5. Demo - Define the Loss Function and Model Checkpoints.mp4	7.1 MB
5. Demo - Define the Loss Function and Model Checkpoints.vtt	2.7 KB
6. Demo - Perform Model Training.mp4	10.4 MB
6. Demo - Perform Model Training.vtt	3.5 KB
7. Demo - Making Predictions out of the Trained Model.mp4	4.7 MB
7. Demo - Making Predictions out of the Trained Model.vtt	1.7 KB
8. Summary.mp4	1.1 MB
8. Summary.vtt	0.9 KB
/.../5. Evaluating Deep Learning Models for Image Captioning/
1. Overview.mp4	1.4 MB
1. Overview.vtt	1.0 KB
2. Meshed Memory Transformer for Image Captioning.mp4	7.2 MB
2. Meshed Memory Transformer for Image Captioning.vtt	3.9 KB
3. Evaluation Metrics for Image Captioning.mp4	17.8 MB
3. Evaluation Metrics for Image Captioning.vtt	9.4 KB
4. Bottom-up and Top-down Attention for Image Captioning.mp4	4.3 MB
4. Bottom-up and Top-down Attention for Image Captioning.vtt	2.4 KB
5. Summary.mp4	4.4 MB
5. Summary.vtt	2.6 KB
/C1. Implement Natural Language Processing for Word Embedding (Axel Sirota, 2022)/
exercise.7z	73.1 MB
playlist.m3u	1.3 KB
~i.txt	1.4 KB
/1. Course Overview/
1. Course Overview.mp4	2.9 MB
1. Course Overview.vtt	1.8 KB
/.../2. Why Process Text/
1. Why Should We Process Text.mp4	5.6 MB
1. Why Should We Process Text.vtt	2.8 KB
2. Demo - Introducing Globomantics Case Study.mp4	1.6 MB
2. Demo - Introducing Globomantics Case Study.vtt	1.1 KB
3. Getting the Best out of This Course.mp4	4.0 MB
3. Getting the Best out of This Course.vtt	2.8 KB
4. Version Check.mp4	3.4 MB
4. Version Check.vtt	2.0 KB
5. Outline of the Course.mp4	2.2 MB
5. Outline of the Course.vtt	1.7 KB
/.../3. Training Word Representations/
1. How to Represent Words.mp4	5.0 MB
1. How to Represent Words.vtt	3.1 KB
2. First Embedding - One Hot Encoding.mp4	4.1 MB
2. First Embedding - One Hot Encoding.vtt	2.6 KB
3. Demo - Using OHE.mp4	33.5 MB
3. Demo - Using OHE.vtt	11.5 KB
4. Demo - Analyzing Sentiment with OHE.mp4	23.5 MB
4. Demo - Analyzing Sentiment with OHE.vtt	8.3 KB
5. Training Embeddings with Networks - CBOW and Skip-gram.mp4	8.3 MB
5. Training Embeddings with Networks - CBOW and Skip-gram.vtt	5.9 KB
6. Demo - Training a CBOW Embedding.mp4	42.4 MB
6. Demo - Training a CBOW Embedding.vtt	14.6 KB
7. Demo - Reanalyze Sentiment with a Network-based Embedding.mp4	27.7 MB
7. Demo - Reanalyze Sentiment with a Network-based Embedding.vtt	8.7 KB
8. What Comes Next.mp4	3.3 MB
8. What Comes Next.vtt	2.0 KB
/.../4. Fine-tuning Word Representations/
1. Why Would We Fine Tune Existing Models.mp4	3.8 MB
1. Why Would We Fine Tune Existing Models.vtt	2.5 KB
2. Demo - Fine Tuning Glove and FastText.mp4	67.6 MB
2. Demo - Fine Tuning Glove and FastText.vtt	13.3 KB
3. Demo - Making Word Clusters.mp4	21.4 MB
3. Demo - Making Word Clusters.vtt	7.8 KB
4. Demo - Debiase Word Embeddings.mp4	40.3 MB
4. Demo - Debiase Word Embeddings.vtt	9.6 KB
5. Key Takeaways and Tips.mp4	1.7 MB
5. Key Takeaways and Tips.vtt	1.1 KB
6. Where to Go Next.mp4	4.2 MB
6. Where to Go Next.vtt	1.8 KB
/C2. Build a Rating Recommendation Engine with Collaborative Filtering (Pratheerth Padman, 2022)/
exercise.7z	4.2 MB
playlist.m3u	1.5 KB
~i.txt	1.7 KB
/1. Course Overview/
1. Course Overview.mp4	3.3 MB
1. Course Overview.vtt	2.2 KB
/.../2. Getting Started with Recommender Systems/
1. Course and Module Overview.mp4	2.2 MB
1. Course and Module Overview.vtt	2.0 KB
2. What Are Recommendation Systems.mp4	5.3 MB
2. What Are Recommendation Systems.vtt	4.4 KB
3. Content-based Filtering.mp4	7.4 MB
3. Content-based Filtering.vtt	5.8 KB
4. Collaborative Filtering.mp4	3.3 MB
4. Collaborative Filtering.vtt	2.9 KB
5. Recommender Systems - Business Use Cases.mp4	10.0 MB
5. Recommender Systems - Business Use Cases.vtt	6.6 KB
6. Module Summary.mp4	1.4 MB
6. Module Summary.vtt	1.4 KB
/.../3. Memory-based Collaborative Filtering/
1. Module Overview.mp4	1.6 MB
1. Module Overview.vtt	1.6 KB
2. Understanding Memory-based Collaborative Filtering.mp4	6.5 MB
2. Understanding Memory-based Collaborative Filtering.vtt	5.4 KB
3. Demo - Setting up Your Environment.mp4	7.0 MB
3. Demo - Setting up Your Environment.vtt	3.8 KB
4. Demo - Dataset Introduction and Exploratory Data Analysis.mp4	18.8 MB
4. Demo - Dataset Introduction and Exploratory Data Analysis.vtt	11.1 KB
5. Demo - User-based Collaborative Filtering.mp4	23.0 MB
5. Demo - User-based Collaborative Filtering.vtt	10.8 KB
6. Demo - Item-based Collaborative Filtering.mp4	21.2 MB
6. Demo - Item-based Collaborative Filtering.vtt	11.2 KB
7. Module Summary.mp4	1.1 MB
7. Module Summary.vtt	1.0 KB
/.../4. Model-based Collaborative Filtering/
1. Module Overview.mp4	1.2 MB
1. Module Overview.vtt	1.3 KB
2. Understanding Model-based Collaborative Filtering.mp4	13.9 MB
2. Understanding Model-based Collaborative Filtering.vtt	11.2 KB
3. Demo - Movie Recommendations with SVD.mp4	13.9 MB
3. Demo - Movie Recommendations with SVD.vtt	8.0 KB
4. Demo - Book Recommendations with Deep Learning.mp4	18.5 MB
4. Demo - Book Recommendations with Deep Learning.vtt	9.7 KB
5. Module Summary and Feedback.mp4	1.5 MB
5. Module Summary and Feedback.vtt	1.5 KB
/C3. Build an Object Detection Model with Python (Gabrielle Davelaar, 2022)/
exercise.7z	8.0 MB
playlist.m3u	0.7 KB
~i.txt	1.3 KB
/1. Course Overview/
1. Course Overview.mp4	2.7 MB
1. Course Overview.vtt	2.0 KB
/.../2. Preparing Data for Model Training/
1. Building a Foundation.mp4	9.7 MB
1. Building a Foundation.vtt	7.6 KB
2. Building up Your Dataset.mp4	7.7 MB
2. Building up Your Dataset.vtt	7.1 KB
3. Demo - Bringing It into Practice.mp4	31.4 MB
3. Demo - Bringing It into Practice.vtt	11.6 KB
4. Finalizing Your Dataset.mp4	621.5 KB
4. Finalizing Your Dataset.vtt	0.7 KB
/3. Training/
1. Detector Models, Frameworks, and Libraries.mp4	13.0 MB
1. Detector Models, Frameworks, and Libraries.vtt	11.0 KB
2. DataFormats and Computing.mp4	6.3 MB
2. DataFormats and Computing.vtt	5.7 KB
3. General Object Detection Script Setup.mp4	6.6 MB
3. General Object Detection Script Setup.vtt	5.6 KB
4. Training a Model - Scripted and PaaS.mp4	24.4 MB
4. Training a Model - Scripted and PaaS.vtt	14.8 KB
5. Evaluating a Detector.mp4	3.3 MB
5. Evaluating a Detector.vtt	3.0 KB
/4. Deployment/
1. Deployment and MLOps.mp4	14.9 MB
1. Deployment and MLOps.vtt	15.0 KB
2. Drift, Retaining, and Model Store.mp4	20.1 MB
2. Drift, Retaining, and Model Store.vtt	18.2 KB
3. Inferencing.mp4	2.5 MB
3. Inferencing.vtt	1.5 KB
4. Summary.mp4	696.2 KB
4. Summary.vtt	0.8 KB
/C4. Build a Model for Anomaly Detection in Time Series Data (Pratheerth Padman, 2022)/
exercise.7z	7.0 MB
playlist.m3u	2.0 KB
~i.txt	1.6 KB
/1. Course Overview/
1. Course Overview.mp4	3.4 MB
1. Course Overview.vtt	2.2 KB
/.../2. Introduction to Time Series Data and Anomaly Detection/
1. Course and Module Summary.mp4	3.9 MB
1. Course and Module Summary.vtt	3.2 KB
2. What Is Time Series Data.mp4	8.9 MB
2. What Is Time Series Data.vtt	6.5 KB
3. Analysing Time Series Data.mp4	7.0 MB
3. Analysing Time Series Data.vtt	5.3 KB
4. Stationarity and Autocorrelation.mp4	7.1 MB
4. Stationarity and Autocorrelation.vtt	6.5 KB
5. Introduction to Anomaly Detection.mp4	5.9 MB
5. Introduction to Anomaly Detection.vtt	4.8 KB
6. Demo - Setting up Your Environment.mp4	8.0 MB
6. Demo - Setting up Your Environment.vtt	4.2 KB
7. Module Summary.mp4	1.6 MB
7. Module Summary.vtt	1.5 KB
/.../3. Building a Model to Automate Anomaly Detection/
01. Module Overview.mp4	1.6 MB
01. Module Overview.vtt	1.3 KB
02. STL Decomposition.mp4	3.0 MB
02. STL Decomposition.vtt	2.4 KB
03. Classification and Regression Trees (CART).mp4	6.8 MB
03. Classification and Regression Trees (CART).vtt	5.4 KB
04. Clustering-based Anomaly Detection.mp4	5.9 MB
04. Clustering-based Anomaly Detection.vtt	4.5 KB
05. Anomaly Detection Using Autoencoders.mp4	5.2 MB
05. Anomaly Detection Using Autoencoders.vtt	3.6 KB
06. Demo - Introduction to the Problem and Dataset.mp4	5.4 MB
06. Demo - Introduction to the Problem and Dataset.vtt	3.4 KB
07. Demo - Exploratory Data Analysis and Data Cleaning.mp4	30.6 MB
07. Demo - Exploratory Data Analysis and Data Cleaning.vtt	10.6 KB
08. Demo - Data Preprocessing and Dimensionality Reduction.mp4	19.2 MB
08. Demo - Data Preprocessing and Dimensionality Reduction.vtt	7.5 KB
09. Demo - Building a Model for Anomaly Detection.mp4	23.5 MB
09. Demo - Building a Model for Anomaly Detection.vtt	7.9 KB
10. Module Summary.mp4	2.0 MB
10. Module Summary.vtt	1.8 KB
/.../4. Model Evaluation and Dealing with Anomalies/
1. Module Overview.mp4	1.1 MB
1. Module Overview.vtt	1.0 KB
2. Demo - Evaluating the Anomaly Detection Models.mp4	16.3 MB
2. Demo - Evaluating the Anomaly Detection Models.vtt	8.3 KB
3. How to Deal with Anomalies.mp4	2.7 MB
3. How to Deal with Anomalies.vtt	3.0 KB
4. Module Summary and Feedback.mp4	1.4 MB
4. Module Summary and Feedback.vtt	1.4 KB
Total files 486