Abhishek Thakur - PokeTube

Abhishek Thakur @UCBPRJjIWfyNG4X-CRbnv78A@youtube.com

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I make videos about applied machine learning, deep learning,

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BARK: Free Text to Speech & Voice Cloning

Build your own Stable Doodle: Sketch to Image

Stable Diffusion XL (SDXL) DreamBooth: Easy, Fast & Free | Beginner Friendly

The FASTEST way to build CHAT UI for LLAMA-v2

The EASIEST way to finetune LLAMA-v2 on local machine!

Run LLAMA-v2 chat locally

1-Click LLM Deployment!

100% Private & Local PDF ChatBot (without langchain)

Deploy FULLY PRIVATE & FAST LLM Chatbots! (Local + Production)

Train LLMs in just 50 lines of code!

Content Based Image Search: InstructBLIP + Sentence Transformers + FAISS

Building a summarizer using XGen-7b: Fully open source LLM by Salesforce

How to: AI Generated QR Codes (Using Python)

Finetune LLMs (llama, vicuna, gptneo, pythia) without any code!

🤗 AutoTrain: Train state-of-the-art image classification models (no code)

Custom object detection in Python using YOLOv8

Segment Anything + ControlNet + Stable Diffusion = 💥

Stable Diffusion Inpainting with Segment Anything Model (SAM)

How to create GPT-powered conversational bot for any website

How to become a data scientist in 30 days?

Data representations for neural networks

My First Neural Network using Keras

What is deep learning?

Kaggle's 30 Days Of ML (Competition Part-7): What are public and private leaderboard?

Kaggle's 30 Days Of ML (Competition Part-6): Model Stacking

Kaggle's 30 Days Of ML (Competition Part-5): Model Blending 101

Kaggle's 30 Days Of ML (Competition Part-4): Hyperparameter tuning using Optuna

Kaggle's 30 Days Of ML (Competition Part-3): What is Target Encoding and how does it work?

Kaggle's 30 Days Of ML (Competition Part-2): Feature Engineering (Categorical & Numerical Variables)

Kaggle's 30 Days Of ML (Competition Part-1): Cross Validation & First Submission on Kaggle

Kaggle's 30 Days Of ML (Day-14 Part-2): What is Data Leakage?

Kaggle's 30 Days Of ML (Day-14 Part-1): Intro to XGBoost

Kaggle's 30 Days Of ML (Day-13 Part-2): Cross-validation

Kaggle's 30 Days Of ML (Day-13 Part-1): Scikit-Learn Pipelines

Kaggle's 30 Days Of ML (Day-12 Part-2): Handling Categorical Variables

Kaggle's 30 Days Of ML (Day-12 Part-1): Handling Missing Values in Datasets (imputing missing value)

Kaggle's 30 Days Of ML (Day-11): Machine Learning Model to Predict House Prices (Intro to ML Ends)

Kaggle's 30 Days Of ML (Day-10): Underfitting, Overfitting & Random Forests

How do random forests work?

Kaggle's 30 Days Of ML (Day-9): First Machine Learning Model and Validation

What Are Decision Trees And How Do They Work? (From Scratch)

Kaggle's 30 Days Of ML (Day-8): What is a machine learning model and what is pandas all about?

Kaggle's 30 Days Of ML (Day-7): Working with external libraries in Python (final python day)

Kaggle's 30 Days Of ML (Day-6): Python Strings and Dictionaries

How to train & deploy transformer models (BERT, RoBERTa, XLNet, etc.) without writing any code!

Kaggle's 30 Days Of ML (Day-5, Part-2): Python Loops and List Comprehension

Kaggle's 30 Days Of ML (Day-5, Part-1): Python Lists and Tuples

Kaggle's 30 Days Of ML (Day-4): Python Booleans and Conditionals

Kaggle's 30 Days Of ML (Day-3): Python Functions

Kaggle's 30 Days Of ML (Day-2): Say Hello to Python: variables, types and arithmetic operations

Kaggle's 30 Days Of ML (Day-1): Getting Started With Kaggle

BERT Model for Toxic Comment Classification (with GitHub Copilot's help)

9. Understanding torch.nn

8. Training and validation loops in PyTorch

7. Linear regression model in PyTorch

6. Dataloader in PyTorch

5. Dataset class for simple image / vision problems

4. Dataset class for simple NLP problems

3. The dataset class in PyTorch

2. PyTorch Autograd