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| 10 - Datasets Data as the foundation Features Labels and Datasets.mp4 |
18.77Мб |
| 11 - Training vs Inference How do AI models learn and make predictions.mp4 |
30.37Мб |
| 12 - Common challenges Overfitting.mp4 |
24.29Мб |
| 13 - Common challenges Bias.mp4 |
7.43Мб |
| 14 - Common challenges Generalization.mp4 |
13.36Мб |
| 15 - Key AIML Topics Feature Engineering.mp4 |
45.45Мб |
| 16 - Google dataset search.txt |
42б |
| 16 - Internet archive.txt |
20б |
| 16 - Kaggle.txt |
31б |
| 16 - Key AIML Topics Key Data Sources.mp4 |
15.87Мб |
| 16 - OpenDataMonitor.txt |
35б |
| 16 - Open Data on AWS.txt |
109б |
| 16 - Quandl.txt |
38б |
| 16 - UC Irvine Machine Learning Repository.txt |
28б |
| 17 - Key AIML Topics Model Selection Different types of ML Models.mp4 |
31.78Мб |
| 18 - Key AIML Topics Model Selection How to select a suitable model.mp4 |
20.37Мб |
| 19 - Key AIML Topics Model Evaluation Validation and CrossValidation.mp4 |
38.42Мб |
| 1 - Introduction.mp4 |
90.93Мб |
| 20 - A-REVIEW-ON-EVALUATION-METRICS-FOR-DATA-CLASSIFICATION-EVALUATIONS.pdf |
160.47Кб |
| 20 - Key AIML Topics Evaluating Model Performance.mp4 |
29.64Мб |
| 20 - Model-Evaluation-Model-Selection-and-Algorithm-Selection-in-Machine-Learning.pdf |
1.85Мб |
| 20 - The-Relationship-Between-Precision-Recall-and-ROC-Curves.pdf |
138.21Кб |
| 21 - Key AIML Topics Hyperparameters.mp4 |
24.83Мб |
| 22 - Dropout-A-Simple-Way-to-Prevent-Neural-Networks-from-Overfitting.pdf |
2.74Мб |
| 22 - Key AIML Topics Model Regularization.mp4 |
13.41Мб |
| 23 - Awesome Deep Learning Related Survey Papers.txt |
55б |
| 23 - Deep Learning and Artificial Neural Networks What are Artificial Neural Network.mp4 |
67.17Мб |
| 24 - Forward Propagation Backpropagation Gradient Descent.mp4 |
79.34Мб |
| 25 - Activation Functions.mp4 |
71.41Мб |
| 25 - Activation-Functions-in-Deep-Learning-A-Comprehensive-Survey-and-Benchmark.pdf |
836.49Кб |
| 25 - A-survey-on-modern-trainable-activation-functions.pdf |
1.02Мб |
| 26 - A-Survey-of-Optimization-Methods-from-a-Machine-Learning-Perspective.pdf |
564.07Кб |
| 26 - Optimization Algorithms.mp4 |
68.13Мб |
| 27 - Intro to NLP Text Processing in NLP.mp4 |
29.44Мб |
| 28 - Applications of NLP.mp4 |
7.81Мб |
| 29 - Core NLP Tasks.mp4 |
25.05Мб |
| 2 - Who is this course for.mp4 |
30.40Мб |
| 30 - NLP Approaches.mp4 |
22.43Мб |
| 31 - Traditional Language Models.mp4 |
26.15Мб |
| 32 - Challenges in NLP.mp4 |
19.37Мб |
| 33 - A-Neural-Probabilistic-Language-Model.pdf |
136.81Кб |
| 33 - A-STATISTICAL-APPROACH-TO-MACHINE-TRANSLATION.pdf |
658.17Кб |
| 33 - Perplexity.mp4 |
24.63Мб |
| 34 - EncodingDecoding Architecture.mp4 |
22.41Мб |
| 35 - Review.mp4 |
36.79Мб |
| 36 - What is Attention.mp4 |
18.37Мб |
| 37 - Understanding Transformers The shift from RNNs CNNs to Transformers.mp4 |
53.12Мб |
| 38 - attentionisall.pdf |
2.11Мб |
| 38 - Attention Is All You Need How selfattention enables LLMs.mp4 |
65.60Мб |
| 39 - SelfAttention and CrossAttention.mp4 |
59.14Мб |
| 3 - Course Outline.mp4 |
90.50Мб |
| 40 - Scaling Laws.mp4 |
48.98Мб |
| 40 - Scaling-Laws-for-Neural-Language-Models.pdf |
2.38Мб |
| 40 - Training-Compute-Optimal-Large-Language-Models.pdf |
5.73Мб |
| 41 - How LLMs learn and adapt.mp4 |
28.51Мб |
| 41 - Large-Language-Models-A-Survey.pdf |
4.66Мб |
| 42 - Types of Pretraining.mp4 |
17.72Мб |
| 43 - Selfsupervised Learning.mp4 |
43.19Мб |
| 44 - LLM Model Architectures.mp4 |
80.42Мб |
| 45 - Model Size and Capabilities.mp4 |
21.39Мб |
| 46 - Finetuning Fundamentals.mp4 |
12.82Мб |
| 46 - Instruction-Tuning-with-GPT-4.pdf |
1.50Мб |
| 46 - Prefix-Tuning-Optimizing-Continuous-Prompts-for-Generation.pdf |
1.50Мб |
| 47 - Adapters-A-Unified-Library-for-Parameter-Efficient-and-Modular-Transfer-Learning.pdf |
1.41Мб |
| 47 - LoRA-Low-Rank-Adaptation-of-Large-Language-Models.pdf |
1.53Мб |
| 47 - ParameterEfficient FineTuning PEFT.mp4 |
28.48Мб |
| 47 - QLoRA-Efficient-Finetuning-of-Quantized-LLMs.pdf |
1.02Мб |
| 48 - PostTraining Fundamentals.mp4 |
11.41Мб |
| 48 - RLAIF-vs.RLHF-Scaling-Reinforcement-Learning-from-Human-Feedback-with-AI-Feedback.pdf |
2.41Мб |
| 49 - Pre-train-Prompt-and-Predict-A-Systematic-Survey-of-Prompting-Methods-in-Natural-Language-Processing.pdf |
11.76Мб |
| 49 - Ways to interact with LLMs.mp4 |
63.16Мб |
| 4 - What is AI.mp4 |
65.02Мб |
| 50 - Zeroshot Prompting.mp4 |
6.21Мб |
| 51 - ChainofThought CoT Reasoning.mp4 |
5.58Мб |
| 51 - Chain-of-Thought-Prompting-Elicits-Reasoning-in-Large-Language-Models.pdf |
870.87Кб |
| 52 - Zeroshot ChainofThought CoT.mp4 |
2.61Мб |
| 53 - Fewshot Prompting.mp4 |
3.77Мб |
| 53 - Language-Models-are-Few-Shot-Learners.pdf |
6.45Мб |
| 54 - Fewshot Prompting CoT.mp4 |
2.76Мб |
| 55 - DemonstrateSearchPredict.mp4 |
4.37Мб |
| 56 - Interleaved Retrieval guided by ChainofThought IRCoT.mp4 |
3.97Мб |
| 57 - SelfConsistency and Tree of Thoughts ToT.mp4 |
23.31Мб |
| 58 - Retrieval-Augmented-Generation-for-Knowledge-Intensive-NLP-Tasks.pdf |
864.57Кб |
| 58 - Retrieval-Augmented-Generation-for-Large-Language-Models-A-Survey.pdf |
1.59Мб |
| 58 - RetrievalAugmented Generation RAG.mp4 |
28.88Мб |
| 59 - AI Agents Autonomous Reasoning.mp4 |
39.85Мб |
| 5 - Ebook-SamGhosh-AI-Foundations-for-Decision-Makers.pdf |
4.88Мб |
| 5 - Narrow AI vs Broad AI.mp4 |
21.68Мб |
| 60 - OpenSource vs Proprietary LLMs.mp4 |
26.13Мб |
| 61 - GPT Series OpenAI.mp4 |
18.27Мб |
| 62 - BERT RoBERTa Google Meta.mp4 |
14.19Мб |
| 63 - T5 UL2 Google.mp4 |
22.79Мб |
| 64 - Claude Anthropic.mp4 |
15.74Мб |
| 65 - LLaMA Meta.mp4 |
11.78Мб |
| 66 - Mistral Mixtral.mp4 |
13.16Мб |
| 67 - Gemini Google DeepMind.mp4 |
20.47Мб |
| 68 - DeepSeek r1.mp4 |
6.90Мб |
| 69 - Grok xAI.mp4 |
11.72Мб |
| 6 - How does ML differ from traditional software.mp4 |
20.20Мб |
| 70 - Command R Cohere Other Enterprise LLMs.mp4 |
21.87Мб |
| 71 - Key Selection Factors for LLMs.mp4 |
20.28Мб |
| 72 - BIGbench.txt |
35б |
| 72 - BIGBenchHard.txt |
45б |
| 72 - GLUE.txt |
26б |
| 72 - GSM8K.txt |
40б |
| 72 - HumanEval.txt |
36б |
| 72 - LLM Performance Benchmarks.mp4 |
19.22Мб |
| 72 - MATH.txt |
45б |
| 72 - MMLU.txt |
39б |
| 72 - SQuAD.txt |
43б |
| 72 - SQuAD V2.txt |
50б |
| 72 - superGLUE.txt |
32б |
| 73 - AGIEval.txt |
38б |
| 73 - Chatbot Arena.txt |
19б |
| 73 - Codebench.txt |
32б |
| 73 - HELM.txt |
31б |
| 73 - LLM Leaderboards.mp4 |
19.99Мб |
| 73 - MTBench.txt |
44б |
| 73 - Open LLM Leaderboard.txt |
73б |
| 73 - TruthfulQA.txt |
37б |
| 74 - Accenture AI.txt |
48б |
| 74 - AiTuning.txt |
24б |
| 74 - IntelligentMind.txt |
27б |
| 74 - LLM Deployment Strategies.mp4 |
35.64Мб |
| 74 - Vaidik.txt |
18б |
| 75 - A-Comprehensive-Analysis-of-Memorization-in-Large-Language-Models.pdf |
1.34Мб |
| 75 - A-Review-of-Current-Trends-Techniques-and-Challenges-in-Large-Language-Models-LLMs.pdf |
533.38Кб |
| 75 - A-Survey-on-Hallucination-in-Large-Language-Models-Principles-Taxonomy-Challenges-and-Open-Questions.pdf |
1.53Мб |
| 75 - Demystifying-Verbatim-Memorization-in-Large-Language-Models.pdf |
3.35Мб |
| 75 - Large-Language-Models-A-Comprehensive-Survey-of-its-Applications-Challenges-Limitations-and-Future-Prospects.pdf |
6.19Мб |
| 75 - Technical Challenges in LLMs.mp4 |
28.58Мб |
| 76 - Review.mp4 |
31.04Мб |
| 77 - What are AI Agents.mp4 |
13.17Мб |
| 78 - Traditional Automation to Autonomous Agents.mp4 |
4.90Мб |
| 79 - Why do Agents Matter in the AIDriven Future.mp4 |
10.85Мб |
| 7 - Must Read Papers for Data Science ML and DL.txt |
50б |
| 7 - Types of Machine Learning.mp4 |
55.66Мб |
| 80 - AutoGPT.txt |
16б |
| 80 - CodeAgent.pdf |
1.15Мб |
| 80 - LangChain.txt |
26б |
| 80 - Major Examples of AI Agents.mp4 |
16.66Мб |
| 80 - microsoftUFO.txt |
32б |
| 80 - MusicLM-Generating-Music-From-Text.pdf |
835.58Кб |
| 80 - OpenDevin.txt |
45б |
| 80 - The AI Scientist.txt |
31б |
| 80 - Voyager.txt |
29б |
| 81 - The-AI-Scientist-Towards-Fully-Automated-Open-Ended-Scientific-Discovery.pdf |
11.19Мб |
| 81 - Types of AI Agents.mp4 |
17.71Мб |
| 82 - How do AI Agents Work.mp4 |
51.24Мб |
| 83 - AutoGen.txt |
53б |
| 83 - The Agent Economy.mp4 |
28.28Мб |
| 84 - AgentOps.mp4 |
24.75Мб |
| 84 - AIOS-LLM-Agent-Operating-System.pdf |
1.28Мб |
| 84 - A-Taxonomy-of-AgentOps-for-Enabling-Observability-of-Foundation-Model-based-Agents.pdf |
509.99Кб |
| 85 - Use Cases of AI Agents.mp4 |
12.34Мб |
| 86 - Ethical and Practical Challenges of AI Agents.mp4 |
52.48Мб |
| 87 - The Convergence of LLMs MultiModal AI and Automation.mp4 |
33.97Мб |
| 88 - Thanks.mp4 |
18.34Мб |
| 8 - Features Data as the foundation Features Labels and Datasets.mp4 |
19.41Мб |
| 9 - Labels Data as the foundation Features Labels and Datasets.mp4 |
24.39Мб |
| Bonus Resources.txt |
70б |
| Get Bonus Downloads Here.url |
180б |