18:12

GenAI Hyperpersonalization with Machine Learning and LLM for Insights | Recommendation Engine

08:54

GenAI Income Tax Optimization [India 2024 Tax Slabs] | LLM + RAG ChromaDB | Zypher 7B LLM

08:45

GenAI Financial Portfolio Optimization [Mutual Funds & Stocks] | LLM + RAG | Zypher 7B LLM [Mistral]

07:13

GenAI Financial Synthetic Data Generator [Mimic Your Data] | LLM + RAG [ Zypher 7B LLM ] Mistral LLM

03:01

GenAI 75 Hard Completed [Consolidated] | Theory and Code | Open Source LLMs and Langchain Projects

10:33

GenAI Financial Recommendation Engine | LLM + RAG ChromaDB [ Zypher 7B LLM ] Mistral LLM

09:14

GenAI Financial Chatbot | Building LLM + RAG Framework [ Zypher 7B LLM ] Mistral LLM | Hugging Face

09:57

GenAI Financial Default Prediction | Building RAG Framework [ Zypher 7B LLM ] and Hugging Face

10:21

GenAI Financial Fraud Detection | Building RAG Framework using Hugging Face [ Zypher 7B LLM ]

07:33

GenAI Financial Advisor | Building Investment Strategies using GenAI [ LLM + RAG ] Python Langchain

05:41

GenAI Financial News Summarizer to Prediction Stock Market Sentiment [ LLM + RAG ] Python Langchain

09:53

GenAI Financial Report Building [ LLM + RAG ] using API Data with Python and Langchain

23:02

Day 75/75 Holistic Evaluation of Language Models [HELM] Framework | Evaluation Metrics Python GenAI

17:42

Day 72/75 Hugging Face LLM Evaluation Framework [Explained] | BLEU ROUGE HELM | Python GenAI

07:04

Day 71/75 GenAI LLM Evaluation Framework [Explained] LLM Evaluation Metrics | Holistic Evaluation

07:45

Day 68/75 Meta LLAMA 3 Fine Tuning [ Explained ] ORPO Fine Tuning + LORA and QLORA | Python GenAi

11:11

Day 65/75 LLM Quantization Techniques [GPTQ - AWQ - BitsandBytes NF4] Python | Hugging Face GenAI

09:11

Day 64/75 Era of 1Bit LLM and 1.58Bit LLM [Most Efficient Quantization Technique] GenAI

10:57

Day 63/75 GenAI LLM Quantization [Quantize and De-Quantize] Affine and Scale Quantization

03:54

Day 62/75 Why [INT1 INT4] not used in LLM Quantization | What are Accumulation Data Types? GenAI

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Day 61/75 LLM Quantization | How Accuracy is maintained? | How FP32 and INT8 calculations same?

08:49

Day 60/75 LLM Quantization to Convert Float32 to Int8 | LLM Evaluation Framework | Scalable LLM

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Day 51/75 Chat with Images or Graphs with [Google Vision Pro LLM] Build Streamlit App using Python

06:39

Day 50/75 How to Fine Tune Google Gemma LLM on [Punjabi Language] for Question Answering or ChatBot

06:45

Day 49/75 Build Chatbot with Google Gemma LLMs on Kaggle in 15 lines using Python | Hugging Face

06:58

Day 48/75 Google Gemini Gemma LLM Fine Tuning LORA | Kaggle Gemma Models | Build Chatbots with LLMs

18:43

Day 41/75 Building Retrieval Augmented Generation [RAG] App + Pinecone Vector DB + Hugging Face LLM

05:56

Day 40/75 Train LLM (OpenAI) with API Data using Python and Kaggle Notebook | Vector DB + RAG

15:24

Day 36/75 Build Text-to-Image [Stable Diffusion] Application with Python Hugging Face | LLM Kaggle

11:30

Day 29/75 Build Text-to-Video AI with LLM [Explained] OpenAI SORA Stable Diffusion VideoPoet Runway

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Day 24/75 Microsoft Phi-2 SLM Fine Tuning using QLORA on Financial News Sentiment Prediction | LLM

11:55

Day 22/75 Fine Tune Meta LLaMA2 7B LLM on Guanaco using QLORA PEFT | Kaggle GPU + Notebook | Python

12:27

Day 21/75 Fine Tune Databricks Dolly V2 LLM using LORA Fine Tuning | Kaggle GPU + Notebook | Python

04:05

Don't Build a Zoo of LLMs: Fine-Tune LLM with LORA & QLORA Efficiently | Meta LLaMA 2 GPT4 Gemini

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Day 20/75 LORA and QLORA LLM Fine Tuning Techniques [Explained] Python Code Meta LLaMA2 Fine Tuning

13:43

Day 19/75 How to Fine Tune LLM with Maske Language Modelling | Hugging Face | PyTorch and Tensorflow

05:20

Day 16/75 How to Build Streamlit App with 3 Hugging Face Transformers | Python Code + Project

06:56

Day 15/75 Top 7 NLP Projects with Hugging Face Transformers [Explained] Question Answering Chatbot

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Day 12/75 Attention Mechanism [Explained] Transformers Architecture in GPT4 [Encoder Decoder] Python

11:25

Day 11/75 Vanshing Gradient and Exploding Gradient Problem in NLP [Explained]

17:51

Day 9/75 Text Representation with Negative Sampling or GLOVE to get Word Embeddings NLP [Explained]

15:34

Day 8/75 Text Representation | Word Embeddings in NLP [Explained] Word2Vec CBOW N-Gram Bi-Gram

14:50

Day 7/75 Next Word Prediction [Explained] LSTM and Bidirectional LSTM Python Model Implemented

20:36

Day 6/75 NLP 7 Data Preprocessing Techniques [Explained] Tokenization to Named Entity Recognition

15:20

Day 5/75 Natural Language Processing [Explained] Types of NLP Algorithms and Why NLP for LLMs

11:42

Day 4/75 Large Language Models Top 2 Optimizers [Explained] Why Softmax is used in Transformers

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ML Model Optimization Top 6 Techniques [ Explained ] Machine Learning, Data Science, NLP Projects

08:31

Day 3/75 Generative AI Top 4 Activation Functions [Explained] Large Language Models Transformers

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Day 2/75 Why Neural Networks [Transformers] used in Generative AI Algorithms

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Day 1/75 Generative AI Top 2 Algorithms[ Explained ] GenAI 2024 PyTorch Lightening Tensorflow LLM

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SQL PySpark Spark SQL Interview Questions [ Explained ] | Crack Interview in 2024 | Data Scientist

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Prompting Techniques[ Explained ] to Increase LLM Performance | 5-Shot Prompting VS Chain-Of-Thought

03:48

Google Gemini AI [Explained] Multimodel Large Language Model Surpassed OpenAI GPT4

17:19

7 Step Journey to Master Large Language Models [ Secrets Revealed ] with Pre Trained Models and RAG

05:22

Multinomial Logistic Regression[ Explained ] Animal Species and Glass Prediction Project with Python

09:49

Building Your Best Data Science Portfolio [Beginner to Advanced Projects] Interview Questions Added

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Statistics Interview Questions for Data Scientist | Most Asked in Google and Microsoft

08:32

Logistic Regression Project [ Explained ] Binary Classification | Data Science Project | Python

07:20

What is Logistic Regression [ Non Linear Model ] Decision Boundary and Sigmoid Function

20:36

Machine Learning [Interview Questions and Answers] | Feature Engineering and Selection with Python