CBTNuggets Introduction to Machine Learning 2024 | 998AC336DD5AE4BD3A9E469B4F946F74D7F5AB24

CBTNuggets Introduction to Machine Learning 2024

Name	CBTNuggets - Introduction to Machine Learning 2024-3	DOWNLOAD Copy Link
Total Size	31.0 GB
Total Files	423
Last Seen	2025-01-05 02:00
Hash	998AC336DD5AE4BD3A9E469B4F946F74D7F5AB24

/55. Design Effective Prompts for Large Language Models/
2. Iterative Prompt Engineering.mp4	216.9 MB
1. Introduction.mp4	50.7 MB
3. Build a Summarizer for Interesting Topics.mp4	140.3 MB
4. Implement Supervised Learning Through Inference.mp4	67.9 MB
5. Challenge Build The AutoBot ChatBot To Manage Orders.mp4	178.6 MB
/1. Explore How AI Agents Navigate Driving Directions/
1. Introduction.mp4	37.4 MB
2. What is Artificial Intelligence.mp4	171.0 MB
3. Grand Search Auto.mp4	147.3 MB
4. Explore the Frontier.mp4	64.6 MB
5. Depth-First Search.mp4	73.0 MB
6. Breadth-First Search.mp4	103.6 MB
7. Breadth-First Search.mp4	77.5 MB
/2. Apply Probability to Real-World AI Problems/
1. Introduction.mp4	31.3 MB
2. Probability of Rolling One 6-sided Die.mp4	109.0 MB
3. Probability of Rolling Two 6-sided Dice.mp4	73.8 MB
4. Conditional Probability.mp4	61.6 MB
5. Bayesian Networks.mp4	76.6 MB
6. Recapitulation.mp4	21.7 MB
/3. Define What is Machine Learning/
1. Introduction.mp4	194.3 MB
2. What is Machine Learning.mp4	168.4 MB
3. Supervised.mp4	162.5 MB
4. Unsupervised.mp4	62.5 MB
5. Build an Image Classifier.mp4	149.5 MB
6. Predicting Lumber Prices with Linear Regression.mp4	134.4 MB
lumber_prediction_line.ipynb.txt	0.1 KB
/4. Setup a Machine Learning Development Environment/
1. Introduction.mp4	108.9 MB
2. Anaconda, Conda and Jupyter - Locally.mp4	172.7 MB
3. Anaconda, Conda and Jupyter - Starting and Ending a Session .mp4	77.8 MB
4. Google Colab.mp4	150.9 MB
5. Cloud Services AWS, GCP, and Azure.mp4	153.3 MB
6. Vast.ai the market leader in low-cost cloud GPU rental.mp4	88.3 MB
conda-cheatsheet.pdf	299.1 KB
/5. Explore How AI Agents Navigate Driving Directions/
1. Introduction.mp4	37.4 MB
2. What is Artificial Intelligence.mp4	171.0 MB
3. Grand Search Auto.mp4	147.3 MB
4. Explore the Frontier.mp4	64.6 MB
5. Depth-First Search.mp4	73.0 MB
6. Breadth-First Search.mp4	103.6 MB
7. Greedy-Best First and A Search.mp4	77.5 MB
/6. Apply Probability to Real-World AI Problems/
1. Introduction.mp4	31.3 MB
2. Probability of Rolling One 6-sided Die.mp4	109.0 MB
3. Probability of Rolling Two 6-sided Dice.mp4	73.8 MB
4. Conditional Probability.mp4	61.6 MB
5. Bayesian Networks.mp4	76.6 MB
6. Recapitulation.mp4	21.7 MB
/7. Define What is Machine Learning/
1. Introduction.mp4	194.3 MB
2. What is Machine Learning.mp4	168.4 MB
3. Supervised.mp4	162.5 MB
4. Unsupervised.mp4	62.5 MB
5. Build an Image Classifier.mp4	149.5 MB
6. Predicting Lumber Prices with Linear Regression.mp4	134.4 MB
/8. Setup a Machine Learning Development Environment/
1. Introduction.mp4	108.9 MB
2. Locally.mp4	172.7 MB
3. Starting and Ending a Session.mp4	77.8 MB
4. Google Colab.mp4	150.9 MB
5. Cloud Services AWS, GCP, and Azure.mp4	153.3 MB
6. Vast.ai the market leader in low-cost cloud GPU rental.mp4	88.3 MB
/9. Explore Data Pipelines and Linear Regression/
1. Introduction.mp4	88.9 MB
2. What is a Machine Learning Model.mp4	125.9 MB
3. Predicting Lumber Prices Data Collection.mp4	100.2 MB
4. Predicting Lumber Prices Data Cleaning & Preprocessing.mp4	56.1 MB
5. Predicting Lumber Prices Feature Extraction.mp4	177.9 MB
/10. Apply Regression Concepts for Supervised Learning/
1. Introduction.mp4	96.0 MB
2. A Brief and Bizarre History of Linear Regression.mp4	86.2 MB
3. Explore Linear Relationships Ordinary Least Squares.mp4	198.9 MB
4. Seaborn Line of Best Fit.mp4	69.6 MB
5. Ordinary Least Squares with Matlab's PolyFit.mp4	128.7 MB
6. Challenge.mp4	75.3 MB
/11. Examine Cost Functions and Parameter Tuning/
1. Introduction.mp4	75.8 MB
2. Mean Absolute Error.mp4	41.5 MB
3. Mean Squared Error.mp4	35.8 MB
4. Root Mean Squared Error.mp4	62.9 MB
5. Cost Functions.mp4	65.1 MB
6. Calculate Your Model's Performance.mp4	207.5 MB
/12. Implement Gradient Descent for Linear Regression/
1. Introduction.mp4	72.3 MB
2. Exploring Gradient Descent Concepts.mp4	76.3 MB
3. Exploring The Gradient Descent Algorithm.mp4	76.6 MB
4. Gradient Descent Behind the Scenes.mp4	89.6 MB
5. Implementing The Gradient Descent Algorithm.mp4	129.2 MB
/13. Vectorize Operations for Multiple Regression/
1. Introduction.mp4	99.5 MB
2. Multiple Linear Regression.mp4	74.4 MB
3. Vectorization.mp4	69.1 MB
4. Non-Vectorized Operations.mp4	92.4 MB
5. Interpreting the Weights.mp4	106.2 MB
6. Vectorized Operations.mp4	38.5 MB
/14. Explore Feature Engineering and Data Preparation/
1. Introduction.mp4	105.1 MB
2. What is Feature Engineering.mp4	82.8 MB
3. Handling Missing Data.mp4	109.9 MB
4. Handling Outliers.mp4	74.0 MB
5. One Hot Encoding.mp4	64.0 MB
6. Define, Split and Scale Features.mp4	92.4 MB
7. Measuring Survival Accuracy .mp4	34.0 MB
/15. Identify Key Classification Algorithms/
1. Introduction.mp4	110.8 MB
2. From Regression to Classification.mp4	89.7 MB
3. Logistic Regression.mp4	68.6 MB
4. Decision Trees.mp4	58.8 MB
5. Random Forests.mp4	71.8 MB
6. Support Vector Machines.mp4	45.8 MB
7. Perceptrons.mp4	53.7 MB
/16. Implement Logistic Regression with Python/
1. Introduction.mp4	158.1 MB
2. What is Logistic Regression.mp4	67.5 MB
3. The Sigmoid Formula and Function.mp4	51.8 MB
4. Logistic Regression in 4 lines of Code.mp4	85.9 MB
5. Implement Logistic Regression Part 1 Data Preprocessing, Cleaning, and Encoding .mp4	168.1 MB
6. Implement Logistic Regression Part 2 Implement Logistic Regression and Measure Performance.mp4	87.4 MB
/17. Build a Python Decision Tree Classification Model/
1. Introduction.mp4	90.0 MB
2. Introduction.mp4	138.6 MB
3. Introduction.mp4	66.3 MB
4. Introduction.mp4	163.3 MB
5. Introduction.mp4	127.0 MB
/18. Build a Python Random Forest Classification Model/
1. Introduction.mp4	72.3 MB
2. What is a Random Forest.mp4	61.1 MB
3. Random Forest Concepts.mp4	77.4 MB
4. Import Libraries, Feature Engineering and One-Hot Encoding.mp4	109.3 MB
5. Train, Test, Predict, and Measure Model Performance.mp4	83.1 MB
6. Bonus Hyperparameter Tuning Video.mp4	31.4 MB
/19. Apply Regularization to Overcome Overfitting/
1. Introduction.mp4	94.6 MB
2. What is Overfitting.mp4	82.7 MB
3. Three Options for Handling Overfitting.mp4	78.7 MB
4. Overfitting for Classification.mp4	63.3 MB
5. Comparing Cost Functions.mp4	71.7 MB
6. Perform Logistic Regression with Regularization.mp4	74.2 MB
/20. Build a Support Vector Machine Classifier/
1. Introduction.mp4	82.3 MB
2. What is a Support Vector Machine.mp4	76.8 MB
3. Optimal Hyperplanes and the Margin.mp4	70.5 MB
4. Data Loading and PreProcessing.mp4	158.7 MB
5. Build and Evaluate the Model.mp4	77.0 MB
6. Breast Cancer Wisconsin (Diagnostic) Dataset.mp4	44.7 MB
/21. Build a K-Nearest Neighbors Classifier/
1. Introduction.mp4	187.1 MB
2. What is K-Nearest Neighbors.mp4	75.1 MB
3. KNN vs. Other Classifiers.mp4	71.5 MB
4. What is Imbalanced Data.mp4	54.5 MB
5. Data Loading and EDA.mp4	53.4 MB
6. Data PreProcessing.mp4	85.0 MB
7. Build and Evaluate the Model.mp4	84.3 MB
/22. Explore Neural Network Basics With The Perceptron/
1. Introduction.mp4	85.7 MB
2. Neurons as the building blocks of neural networks.mp4	35.6 MB
3. Perceptrons As Artificial Neurons.mp4	70.6 MB
4. How Activation Functions Work.mp4	55.6 MB
5. Why Linearly Separable Data Is Key.mp4	79.1 MB
6. Build A Simple Binary Perceptron Classifier.mp4	117.4 MB
7. Challenge Complete The Perceptron Function 🍩.mp4	66.5 MB
8. Solution Video.mp4	79.1 MB
/23. Implement a Perceptron for Classification/
1. Introduction.mp4	88.1 MB
2. What is a Perceptron.mp4	36.8 MB
3. The Perceptron Rule and Neurons.mp4	117.3 MB
4. Implement a Perceptron from Scratch.mp4	148.8 MB
5. The Perceptron Challenge.mp4	41.8 MB
6. Solution Video.mp4	75.0 MB
7. Bonus Resources.mp4	95.6 MB
/24. Explore PyTorch Fundamentals for Machine Learning/
1. Introduction.mp4	69.7 MB
2. What Is PyTorch and Why It Is Useful.mp4	66.7 MB
3. Set up a PyTorch Development Environment.mp4	46.5 MB
4. Leverage Tensors Concepts.mp4	54.5 MB
5. Leverage Tensors Programmatically.mp4	61.4 MB
6. Challenge.mp4	48.6 MB
/25. Leverage PyTorch Tensor Attributes and Operators/
1. Introduction.mp4	70.0 MB
2. Tensor attributes.mp4	72.9 MB
3. Tensor Math Operators.mp4	52.6 MB
4. Matrix Multiplication.mp4	67.2 MB
5. The PyTorch Double Challenge.mp4	75.3 MB
/26. Explore Fundamental PyTorch Tensor Operations/
1. Introduction.mp4	40.7 MB
2. Review Matrix Multiplication Errors.mp4	101.9 MB
3. Min, Max, Mean, and Sum (Tensor Aggregation).mp4	57.2 MB
4. Navigating Positional Min Max Values.mp4	43.3 MB
5. The Challenge.mp4	77.6 MB
6. Solution Video.mp4	53.5 MB
7. Bonus Resources.mp4	38.6 MB
/27. Apply PyTorch Tensor Manipulation and Indexing/
1, Introduction.mp4	38.7 MB
2. Reshape, View, and Stack Tensors.mp4	111.0 MB
3. Squeeze and Unsqueeze Tensors.mp4	72.0 MB
4. Permute Tensors.mp4	49.3 MB
5. Index Tensors.mp4	62.1 MB
6. Challenge Tensor Transformer.mp4	61.6 MB
7. Solution Video.mp4	42.0 MB
/28. Explore Gradient Descent & Back Propagation/
1. Introduction.mp4	116.2 MB
2. Gradient Descent.mp4	17.2 MB
3. Forward Propagation.mp4	56.6 MB
4. Back Propagation.mp4	78.3 MB
5. Training, Validation, and Test Datasets.mp4	43.5 MB
6. Split The Train Test Datasets.mp4	170.2 MB
7. Build a Linear Regression Model.mp4	111.9 MB
/29. Predict Ice Cream Sales with PyTorch Regression/
1. Introduction.mp4	49.0 MB
2. Device Agnostic Conditions & Load Data.mp4	43.8 MB
3. Pre-Processing.mp4	37.9 MB
4. Model Building.mp4	42.7 MB
5. Mini-Challenge Model Training & Model Evaluation.mp4	69.7 MB
6. Saving and Loading PyTorch Models.mp4	66.8 MB
7. Challenge.mp4	56.0 MB
/30. Implement a Logistic Regression Model with PyTorch/
1. Introduction.mp4	62.8 MB
2. Review Sklearn Titanic Classification.mp4	38.3 MB
3. Part1 Import Libraries, Define Model. and Load the data.mp4	47.9 MB
4. Part2 Build model.mp4	48.7 MB
5. Part 3 Fit model.mp4	37.3 MB
6. Challenge Part 1 Evaluate the Model .mp4	37.6 MB
7. Challenge Part 3 Bonus Self-Graded Take-Home Challenge.mp4	89.4 MB
/31. Explore Neural Network Classification with PyTorch/
1. Introduction.mp4	61.5 MB
2. Review Logistic Regression PyTorch Workflow.mp4	61.6 MB
3. Load Make Moons Dataset & Pre-processing.mp4	68.0 MB
4. Define Neural Network Architecture.mp4	68.6 MB
5. Train and Evaluate Model.mp4	80.7 MB
6. Visualize Decision Boundary with Probability.mp4	13.8 MB
7. Challenge PyTorch Workflow.mp4	42.7 MB
/32. Build a PyTorch Classifier with Non-Linearity/
1. Introduction.mp4	23.1 MB
2. Review Neural Network Classification Without Non-Linearity.mp4	84.3 MB
3. Build a Neural Network Classification With Non-Linearity Step 1 Load Dataset, Pre-processing, and Make Circles.mp4	62.8 MB
4. Build a Neural Network Classification With Non-Linearity Step 2 Define Neural Network Architecture.mp4	56.6 MB
5. Build a Neural Network Classification With Non-Linearity Step 3 Add Non-Linear Activation Function ReLu.mp4	56.4 MB
6. Build a Neural Network Classification With Non-Linearity Step 4 Train Model .mp4	80.7 MB
7. Build a Neural Network Classification With Non-Linearity Step 5 Evaluate Model.mp4	33.7 MB
8. Challenge PyTorch Workflows.mp4	59.1 MB
/33. Explore Multi-class Classification with PyTorch/
1. Introduction.mp4	15.9 MB
2. Review of Binary Classification with PyTorch.mp4	111.0 MB
3. Step 1 Setup and Prepare Data.mp4	55.6 MB
4. Step 2 Visualize Data (EDA).mp4	40.2 MB
5. Step 3 Define Neural Network Architecture.mp4	41.9 MB
6. Challenge .mp4	45.5 MB
7. Solution Videos Training Loop.mp4	46.2 MB
8. Solution Videos Evaluation and Decision Boundary.mp4	39.8 MB
/34. Tune Hyperparameters and Analyze Fit with PyTorch/
1. Introduction.mp4	11.8 MB
2. Review Explore Multi-class Classification with PyTorch.mp4	57.4 MB
3. Create, Preprocess, and Visualize the Spiral Dataset.mp4	57.0 MB
4. Define Neural Network Architecture.mp4	26.9 MB
5. Explore Hyperparameter Tuning.mp4	78.1 MB
6. Explore Underfitting and Overfitting.mp4	46.1 MB
7. Challenge .mp4	40.5 MB
8. Solution Video.mp4	56.6 MB
/35. Discover What's New with PyTorch 2.0/
1. Introduction.mp4	82.7 MB
2. Universal Device Setup in PyTorch 2.0.mp4	37.7 MB
3. Key Features of PyTorch 2.0.mp4	71.3 MB
4. Traditional PyTorch 1.0 Vs PyTorch 2.0 torch.compile( ).mp4	74.8 MB
5. Challenge .mp4	46.5 MB
6. Challenge 2.mp4	23.7 MB
/36. Explore TensorFlow Machine Learning Foundations/
1. Introduction.mp4	51.0 MB
2. Introduction to TensorFlow Tensors.mp4	50.2 MB
3. Introduction to TensorFlow Tensors Part 2.mp4	22.4 MB
4. Create Tensors with TensorFlow.mp4	20.9 MB
5. Create Random Tensors with Numpy.mp4	58.2 MB
6. Challenge.mp4	65.5 MB
/37. Explore TensorFlow Aggregation and Manipulation/
1. Introduction.mp4	46.2 MB
2. Why Shuffle Tensors.mp4	28.1 MB
3. TensorFlow Seeds.mp4	23.7 MB
4. Tensor Attributes.mp4	24.5 MB
5. Tensor Indexing.mp4	15.2 MB
6. Changing Tensor Data Types & Tensor Aggregation.mp4	34.2 MB
7. Tensor Positional Methods.mp4	35.0 MB
8. Challenge .mp4	25.0 MB
9. Challenge 2.mp4	30.1 MB
/38. Implement Matrix Multiplication with TensorFlow/
1. Introduction.mp4	18.3 MB
2. Basic Tensor Operation.mp4	17.7 MB
3. TensorFlow Math Functions.mp4	28.1 MB
4. Matrix Multiplication Foundations.mp4	61.1 MB
5. Perform Matrix Multiplication.mp4	67.9 MB
6. Challenge.mp4	61.6 MB
/39. Reshape, Transpose, and Alter TensorFlow Tensors/
1. Introduction.mp4	11.9 MB
2. Review Matrix Multiplication.mp4	52.8 MB
3. Altering Tensors.mp4	39.0 MB
4. Transpose & Reshape Tensors.mp4	29.4 MB
5. Tensor Expansion.mp4	50.2 MB
6. Challenge.mp4	79.7 MB
7. Solution Part 1.mp4	66.8 MB
8. Solution Part 2.mp4	24.1 MB
/40. Squeeze, Encode, and Optimize TensorFlow Tensors/
1. Introduction.mp4	28.0 MB
2. Squeezing Tensors.mp4	77.7 MB
3. One-Hot Encoding.mp4	40.7 MB
4. Numpy = Friend ❤️.mp4	55.4 MB
5. GPU & TPU Tensor Optimization.mp4	55.0 MB
6. Challenge.mp4	23.6 MB
7. Challenge Part 2.mp4	66.9 MB
/41. Explore Neural Network Regression with TensorFlow/
1. Introduction.mp4	10.8 MB
2. What is Regression Analysis.mp4	67.0 MB
3. Neural Network Architecture.mp4	113.5 MB
4. Build a Model.mp4	109.1 MB
5. Challenge.mp4	61.1 MB
6. Solution Video.mp4	99.4 MB
/42. Build a Simple Regression Model with TensorFlow/
1. Introduction.mp4	80.8 MB
2. Build a Small Regression Model from Memory .mp4	62.0 MB
3. Build Model From Scratch.mp4	113.9 MB
4. Challenge Improve Model.mp4	70.0 MB
5. Solution Part 1.mp4	62.0 MB
6. Solution Part 1.mp4	113.2 MB
/43. Evaluate Regression Models with TensorFlow/
1. Introduction.mp4	67.2 MB
2. Regression Challenge.mp4	58.5 MB
3. Preprocess Data.mp4	73.8 MB
4. Challenge Build Model.mp4	52.1 MB
5. Challenge Solution.mp4	120.1 MB
/44. Visualize and Evaluate Performance with TensorFlow/
1. Introduction.mp4	35.9 MB
2. Generate Linear Transformation Data.mp4	75.3 MB
3. Common Evaluation Metrics MAE, MSE, & Huber.mp4	81.9 MB
4. Split Data for Train and Test Datasets.mp4	108.4 MB
5. Define Basic Model Architecture.mp4	35.3 MB
6. Make Predictions and Evaluate Model.mp4	59.1 MB
7. Challenge.mp4	47.4 MB
8. Solution Video.mp4	67.1 MB
/45. Normalize and Feature Scale Data with TensorFlow/
1. Introduction.mp4	77.0 MB
2. Handle Imports & Load Dataset.mp4	37.7 MB
3. One-hot Encode & Separate Features and Target.mp4	32.9 MB
4. Perform TrainTest Split.mp4	25.2 MB
5. Define Model Architecture.mp4	36.3 MB
6. Evaluate Model and Visualize Loss.mp4	32.8 MB
7. What is Normalization and Standardization.mp4	12.0 MB
8. Challenge.mp4	66.6 MB
9. Solution Video.mp4	49.9 MB
/46. Explore TensorFlow Neural Network Classification/
1. Introduction.mp4	89.6 MB
2. What is Classification.mp4	101.2 MB
3. What is Binary Classification.mp4	56.8 MB
4. What is Multi-Class Classification.mp4	40.2 MB
5. What is Multi-Label Classification.mp4	63.5 MB
6. Classification Code Example.mp4	65.7 MB
7. Challenge.mp4	91.0 MB
8. Solution.mp4	38.8 MB
/47. Build a Neural Network Classifier with TensorFlow/
1. Introduction.mp4	43.9 MB
2. Pseudocode Image Classification.mp4	26.8 MB
3. Create Circles Dataset & EDA.mp4	64.1 MB
4. Build, Compile, and Train Model.mp4	36.2 MB
5. Visualize and Evaluate Model.mp4	68.9 MB
6. Challenge.mp4	37.3 MB
7. Solution Video.mp4	49.8 MB
8. Bonus Video.mp4	41.4 MB
/48. Build a TensorFlow Classifier with Non-Linearity/
1. Review Non-Linearly Separable Data.mp4	77.8 MB
2. Create Circles DataSet.mp4	43.2 MB
3. Create Second Model.mp4	74.2 MB
4. Create Third Model.mp4	48.1 MB
5. Create Fourth Model.mp4	94.5 MB
6. Challenge.mp4	13.3 MB
7. Solution.mp4	66.6 MB
/49. Evaluate TensorFlow Classification Models/
1. Review Learning Rates.mp4	67.5 MB
2. Adaptive Learning Rates part 1.mp4	42.2 MB
3. Adaptive Learning Rates part 2.mp4	29.5 MB
4. Adaptive Learning Rates part 3.mp4	102.7 MB
5. Big Five Evaluation Metrics.mp4	29.7 MB
6. Solution Video.mp4	32.2 MB
/50. Explore Multi-Class Classification with TensorFlow/
1. Compare Binary and Multi-Class Classification.mp4	65.2 MB
2. Create a Teachable Machine Multi-Class Classifier.mp4	131.1 MB
3. Review Model Building Steps.mp4	21.5 MB
4. Load and Explore MNIST Fashion Dataset.mp4	104.6 MB
5. Challenge.mp4	32.4 MB
6. Solution Video.mp4	73.0 MB
/51. Tune Multi-Class Classification TensorFlow Models/
1. Introduction.mp4	58.2 MB
2. Review MNIST Fashion Multi-Class Classifier.mp4	83.3 MB
3. Load and Visualize Dataset.mp4	57.9 MB
4. One-Hot Encode Features and Build Model.mp4	114.8 MB
5. Softmax and Validation Exploration.mp4	52.8 MB
6. Challenge.mp4	73.5 MB
7. Solution Video.mp4	52.4 MB
/52. Explore Multi-Label Classification with TensorFlow/
1. Introduction.mp4	31.4 MB
2. Binary, Multi-Class, and Multi-Label Classification.mp4	205.3 MB
3. Start Building a Multi-Label Classifier.mp4	56.9 MB
4. Build a Sequential Multi-Label Model.mp4	48.7 MB
5. Evaluate Model.mp4	54.1 MB
6. Challenge.mp4	41.1 MB
7. Solution Video.mp4	36.4 MB
/53. Explore The Fundamentals of Large Language Models/
1. Introduction.mp4	62.0 MB
2. What is a Large Language Model (LLM).mp4	103.4 MB
3. How do LLMs work.mp4	36.4 MB
4. Two Kinds of LLMs Base and Instruction Tuned.mp4	54.4 MB
5. System Messages and Tokens.mp4	39.6 MB
6. System Messages and Tokens Part 2.mp4	32.8 MB
7. Challenge Connect Google Colab to ChatGPT via OpenAI's API.mp4	76.8 MB
/54. Build LLM Apps with ChatGPT and the OpenAI API/
1. Introduction.mp4	47.4 MB
2. Web Chat Interfaces Vs. Programmatic Notebooks.mp4	85.4 MB
3. Route Queries Using Classification for Different Cases.mp4	137.5 MB
4. Evaluate Inputs to Prevent Prompt Injections.mp4	22.6 MB
5. Implement The OpenAI Moderation API.mp4	123.2 MB
6. Sanitize and Validate Inputs Injection Attacks.mp4	100.6 MB
7. Challenge Filter Inputs with a Chain of Thought Prompt Filter.mp4	136.4 MB
/
Readme.txt	0.1 KB
/56. Implement LangChain in Language Model Workflows/
1. Introduction.mp4	62.6 MB
2. Compare Direct API Calls Vs. API Calls Through LangChain - LangChain API Call.mp4	101.3 MB
3. Leverage LangChain Templating for Complex Prompts.mp4	186.7 MB
4. Leverage Power of Templating for DRY Code.mp4	80.3 MB
5. Challenge.mp4	27.4 MB
6. Video Solution.mp4	93.5 MB
/57. Implement LangChain Memory for Autonomous Tasks/
1. Introduction.mp4	55.3 MB
2. ConversationBufferMemory.mp4	132.3 MB
3. ConversationBufferWindowMemory.mp4	63.3 MB
4. ConversationTokenBufferMemory.mp4	36.0 MB
5. ConversationSummaryBufferMemory.mp4	80.6 MB
6. The Power of Chaining LangChain Components.mp4	138.9 MB
7. Challenge Implement LangChain Memory.mp4	150.4 MB
/58. Combine LangChain Components for Coherent Apps/
1. Introduction.mp4	89.5 MB
2. Chaining in LangChain.mp4	44.7 MB
3. LLMChain.mp4	74.0 MB
4. SimpleSequentialChain.mp4	55.9 MB
5. SequentialChain.mp4	68.5 MB
6. RouterChain.mp4	137.2 MB
7. Challenge.mp4	83.1 MB
/59. Build Task-Driven Autonomous Agents with LangChain/
1. Introduction.mp4	89.5 MB
2. Leverage LangChain Agents.mp4	54.5 MB
3. Perform math calculation using an Math LLM.mp4	68.5 MB
4. Use Wikipedia to Find General Information.mp4	64.6 MB
5. Program using a Python REPL tool.mp4	22.3 MB
6. Create new custom agents and tooling (BabyAGI).mp4	33.1 MB
7. Debugging with LangChain.mp4	102.6 MB
8. Challenge.mp4	77.2 MB
/60. Use LangChain to Interact with PDFs and Documents/
1. Introduction.mp4	72.9 MB
2. Retrieval Augmented Generation (RAG) over 2 Skills.mp4	49.0 MB
3. Document Loaders.mp4	48.5 MB
4. Document Separation.mp4	75.1 MB
5. Embeddings.mp4	74.2 MB
6. Vector Stores.mp4	102.4 MB
/61. Use LangChain to Chat with PDFs and Documents/
1. Introduction.mp4	43.3 MB
2. Similarity Search.mp4	54.4 MB
3. Maximum Margin Relevance.mp4	80.9 MB
4. ContextualCompressionRetriever + MMR.mp4	59.7 MB
5. Chat Q&A Part 1.mp4	63.7 MB
6. Chat Q&A Part 2.mp4	73.5 MB
7. Challenge.mp4	136.6 MB
/62. Explore Transformer Encoders and Decoders/
1. Introduction.mp4	86.2 MB
2. What are Transformers.mp4	41.3 MB
3. Attention Is All You Need (Optional).mp4	165.4 MB
4. Encoders.mp4	25.2 MB
5. Decoders.mp4	31.0 MB
6. Encoder-Decoders.mp4	17.2 MB
7. What is HuggingFace Again.mp4	56.1 MB
8. Solution Video.mp4	39.5 MB
/63. Examine the Fundamentals of HuggingFace/
1. Introduction.mp4	113.8 MB
2. What is HuggingFace 🤗.mp4	46.9 MB
3. Models.mp4	140.3 MB
4. Datasets.mp4	74.5 MB
5. Spaces.mp4	154.2 MB
6. ChatGPT Competitor HuggingChat 🦾🤗.mp4	17.0 MB
7. Challenge.mp4	118.4 MB
Total files 423