jadhav-aditya Goto Github PK

basics

📚 Learn ML with clean code, simplified math and illustrative visuals. As you learn, work on interesting projects and share them on https://madewithml.com for the community to discover and learn from!

bert-sentiment

deep_learning_notebooks

A collection of some of my deep learning experiments on NLP. Fork it, improve it :)

dlaicourse

Notebooks for learning deep learning

fastbook

Draft of the fastai book

hackathons

This repo contains my hackathon solutions

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning

is_that_a_duplicate_quora_question

learn-keras-for-deep-neural-networks

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.

masterportfolio

The complete portfolio website hosted using github pages (gh-pages). Visit the website here :

spark-py-notebooks

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

tensorflow-examples

TensorFlow Tutorial and Examples for Beginners with Latest APIs

transformers

Implementations of transformer models in pytorch