Udemy - AI Foundations For Decision Makers - From Zero To LLMs

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1 - Introduction
• 1 - Introduction.mp4 (90.9 MB)
• 2 - Who is this course for.mp4 (30.4 MB)
• 3 - Course Outline.mp4 (90.5 MB)
2 - Fundamentals of Machine Learning and AI Introduction to AI and Machine Learning
• 10 - Datasets Data as the foundation Features Labels and Datasets.mp4 (18.8 MB)
• 11 - Training vs Inference How do AI models learn and make predictions.mp4 (30.4 MB)
• 12 - Common challenges Overfitting.mp4 (24.3 MB)
• 13 - Common challenges Bias.mp4 (7.4 MB)
• 14 - Common challenges Generalization.mp4 (13.4 MB)
• 15 - Key AIML Topics Feature Engineering.mp4 (45.4 MB)
• 16 - Google dataset search.txt (0.0 KB)
16 - Internet archive.txt
• 16 - Kaggle.txt (0.0 KB)
• 16 - Key AIML Topics Key Data Sources.mp4 (15.9 MB)
• 16 - Open Data on AWS.txt (0.1 KB)
• 16 - OpenDataMonitor.txt (0.0 KB)
• 16 - Quandl.txt (0.0 KB)
• 16 - UC Irvine Machine Learning Repository.txt (0.0 KB)
• 17 - Key AIML Topics Model Selection Different types of ML Models.mp4 (31.8 MB)
• 18 - Key AIML Topics Model Selection How to select a suitable model.mp4 (20.4 MB)
• 19 - Key AIML Topics Model Evaluation Validation and CrossValidation.mp4 (38.4 MB)
• 20 - A-REVIEW-ON-EVALUATION-METRICS-FOR-DATA-CLASSIFICATION-EVALUATIONS.pdf (160.5 KB)
• 20 - Key AIML Topics Evaluating Model Performance.mp4 (29.6 MB)
• 20 - Model-Evaluation-Model-Selection-and-Algorithm-Selection-in-Machine-Learning.pdf (1.9 MB)
• 20 - The-Relationship-Between-Precision-Recall-and-ROC-Curves.pdf (138.2 KB)
• 21 - Key AIML Topics Hyperparameters.mp4 (24.8 MB)
• 22 - Dropout-A-Simple-Way-to-Prevent-Neural-Networks-from-Overfitting.pdf (2.7 MB)
• 22 - Key AIML Topics Model Regularization.mp4 (13.4 MB)
• 23 - Awesome Deep Learning Related Survey Papers.txt (0.1 KB)
• 23 - Deep Learning and Artificial Neural Networks What are Artificial Neural Network.mp4 (67.2 MB)
• 24 - Forward Propagation Backpropagation Gradient Descent.mp4 (79.3 MB)
• 25 - A-survey-on-modern-trainable-activation-functions.pdf (1.0 MB)
• 25 - Activation Functions.mp4 (71.4 MB)
• 25 - Activation-Functions-in-Deep-Learning-A-Comprehensive-Survey-and-Benchmark.pdf (836.5 KB)
• 26 - A-Survey-of-Optimization-Methods-from-a-Machine-Learning-Perspective.pdf (564.1 KB)
• 26 - Optimization Algorithms.mp4 (68.1 MB)
• 27 - Intro to NLP Text Processing in NLP.mp4 (29.4 MB)
• 28 - Applications of NLP.mp4 (7.8 MB)
• 29 - Core NLP Tasks.mp4 (25.1 MB)
• 30 - NLP Approaches.mp4 (22.4 MB)
• 31 - Traditional Language Models.mp4 (26.1 MB)
• 32 - Challenges in NLP.mp4 (19.4 MB)
• 33 - A-Neural-Probabilistic-Language-Model.pdf (136.8 KB)
• 33 - A-STATISTICAL-APPROACH-TO-MACHINE-TRANSLATION.pdf (658.2 KB)
• 33 - Perplexity.mp4 (24.6 MB)
• 34 - EncodingDecoding Architecture.mp4 (22.4 MB)
• 4 - What is AI.mp4 (65.0 MB)
• 5 - Ebook-SamGhosh-AI-Foundations-for-Decision-Makers.pdf (4.9 MB)
• 5 - Narrow AI vs Broad AI.mp4 (21.7 MB)
• 6 - How does ML differ from traditional software.mp4 (20.2 MB)
• 7 - Must Read Papers for Data Science ML and DL.txt (0.0 KB)
• 7 - Types of Machine Learning.mp4 (55.7 MB)
• 8 - Features Data as the foundation Features Labels and Datasets.mp4 (19.4 MB)
• 9 - Labels Data as the foundation Features Labels and Datasets.mp4 (24.4 MB)
3 - Fundamentals of Large Language Models
• 35 - Review.mp4 (36.8 MB)
• 36 - What is Attention.mp4 (18.4 MB)
• 37 - Understanding Transformers The shift from RNNs CNNs to Transformers.mp4 (53.1 MB)
• 38 - Attention Is All You Need How selfattention enables LLMs.mp4 (65.6 MB)
• 38 - attentionisall.pdf (2.1 MB)
• 39 - SelfAttention and CrossAttention.mp4 (59.1 MB)
• 40 - Scaling Laws.mp4 (49.0 MB)
• 40 - Scaling-Laws-for-Neural-Language-Models.pdf (2.4 MB)
• 40 - Training-Compute-Optimal-Large-Language-Models.pdf (5.7 MB)
• 41 - How LLMs learn and adapt.mp4 (28.5 MB)
• 41 - Large-Language-Models-A-Survey.pdf (4.7 MB)
• 42 - Types of Pretraining.mp4 (17.7 MB)
• 43 - Selfsupervised Learning.mp4 (43.2 MB)
• 44 - LLM Model Architectures.mp4 (80.4 MB)
• 45 - Model Size and Capabilities.mp4 (21.4 MB)
• 46 - Finetuning Fundamentals.mp4 (12.8 MB)
• 46 - Instruction-Tuning-with-GPT-4.pdf (1.5 MB)
• 46 - Prefix-Tuning-Optimizing-Continuous-Prompts-for-Generation.pdf (1.5 MB)
• 47 - Adapters-A-Unified-Library-for-Parameter-Efficient-and-Modular-Transfer-Learning.pdf (1.4 MB)
• 47 - LoRA-Low-Rank-Adaptation-of-Large-Language-Models.pdf (1.5 MB)
• 47 - ParameterEfficient FineTuning PEFT.mp4 (28.5 MB)
• 47 - QLoRA-Efficient-Finetuning-of-Quantized-LLMs.pdf (1.0 MB)
• 48 - PostTraining Fundamentals.mp4 (11.4 MB)
• 48 - RLAIF-vs.RLHF-Scaling-Reinforcement-Learning-from-Human-Feedback-with-AI-Feedback.pdf (2.4 MB)
• 49 - Pre-train-Prompt-and-Predict-A-Systematic-Survey-of-Prompting-Methods-in-Natural-Language-Processing.pdf (11.8 MB)
• 49 - Ways to interact with LLMs.mp4 (63.2 MB)
• 50 - Zeroshot Prompting.mp4 (6.2 MB)
• 51 - Chain-of-Thought-Prompting-Elicits-Reasoning-in-Large-Language-Models.pdf (870.9 KB)
• 51 - ChainofThought CoT Reasoning.mp4 (5.6 MB)
• 52 - Zeroshot ChainofThought CoT.mp4 (2.6 MB)
• 53 - Fewshot Prompting.mp4 (3.8 MB)
• 53 - Language-Models-are-Few-Shot-Learners.pdf (6.5 MB)
• 54 - Fewshot Prompting CoT.mp4 (2.8 MB)
• 55 - DemonstrateSearchPredict.mp4 (4.4 MB)
• 56 - Interleaved Retrieval guided by ChainofThought IRCoT.mp4 (4.0 MB)
• 57 - SelfConsistency and Tree of Thoughts ToT.mp4 (23.3 MB)
• 58 - Retrieval-Augmented-Generation-for-Knowledge-Intensive-NLP-Tasks.pdf (864.6 KB)
• 58 - Retrieval-Augmented-Generation-for-Large-Language-Models-A-Survey.pdf (1.6 MB)
• 58 - RetrievalAugmented Generation RAG.mp4 (28.9 MB)
• 59 - AI Agents Autonomous Reasoning.mp4 (39.9 MB)
• 60 - OpenSource vs Proprietary LLMs.mp4 (26.1 MB)
• 61 - GPT Series OpenAI.mp4 (18.3 MB)
• 62 - BERT RoBERTa Google Meta.mp4 (14.2 MB)
• 63 - T5 UL2 Google