Intermediate PyTorch

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Illustrated Guide to Transformers Neural Network: A step by step explanation

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Neural Networks and Deep Learning: Crash Course AI 3

This IBM course explores transformers and key model frameworks like Hugging Face and PyTorch. It covers optimizing LL Ms and advances to fine-tuning generative AI models using techniques like PEFT, LoRA, and QLoRA.

This course focuses on the foundational concepts of LL Ms, specifically tokenization and word embedding models. It includes practical, hands-on exercises for building and training these models using PyTorch.

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PyTorch, H2O, KERAS & TensorFlow IN PYTHON!It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & TensorFlow.                         HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the PyTorch, H2O, Keras and TensorFlow framework in Python. This means, this course covers the important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures.  In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, TensorFlow is revolutionizing Deep Learning... By gaining proficiency in PyTorch, H2O, Keras and TensorFlow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.&nbsp

A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina).Também dentro do contexto da Aprendizagem de Máquina existe a área de Aprendizagem por Reforço, que é um tipo de aprendizagem usado em sistemas multi-agente no qual os agentes devem interagir no ambiente e aprenderem por conta própria, ganhando recompensas positivas quando executam ações corretas e recompensas negativas quando executam ações que não levem para o objetivo. O interessante dessa técnica é que a inteligência artificial aprende sem nenhum conhecimento prévio, adaptando-se ao ambiente e encontrando as soluções sozinho!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre a construção de um carro autônomo virtual utilizando aprendizagem por reforço! Vamos trabalhar com técnicas modernas de Deep Learning com a biblioteca PyTorch e a linguagem Python! Ao final você terá todas as ferramentas necessárias para solucionar outros tipos de problemas com aprendizagem por reforço. O conteúdo do curso está dividido em três partes:Teoria sobre aprendizagem por reforço com o algoritmo Q-Learning Teoria da aprendizagem por reforço

In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques.&nbsp; This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examples Course Structure:Creating a Classification Model using Scikit-Learn Saving the Model and the standard Scaler Exporting the Model to another environment - Local and Google Colab Creating a REST API using Python Flask and using it locally Creating a Machine Learning REST API on a Cloud virtual server Creating a Serverless Machine Learning REST API using Cloud Functions Building and Deploying TensorFlow and Keras models using TensorFlow Serving Building and Deploying&nbsp; PyTorch Models Converting a PyTorch model to TensorFlow format using ONNX Creating REST API for PyTorch and TensorFlow Models Deploying tf-idf and text classifier models for Twitter sentiment analysis Deploying models using TensorFlow.js and Java Script Tracking Model training experiments and deployment with MLF Low Running ML Flow on Colab and Databricks Appendix - Generative AI - Miscellaneous Topics.OpenAI and the history of GPT models Creating an OpenAI account and invoking a text-to-speech model from Python code Invoking OpenAI Chat Completion, Text Generation, Image Generation models from Python code Creating a Chatbot with OpenAI API and ChatGPT Model using Python on Google Colab ChatGPT, Large Language Models (LLM) and prompt engineering New Section : Agent-Mode Model Building and Deployment with Git Hub Copilot Vibe Coding: Model Development with Git Hub Copilot Using a Single Prompt<li

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.We cover several key NLP frameworks including:Hugging Face's Transformers TensorFlow 2Py Torchspa CyNLTK Flair And learn how to apply transformers to some of the most popular NLP use-cases:Language classification/sentiment analysis Named entity recognition (NER)Question and Answering Similarity/comparative learning Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:History of NLP and where transformers come from Common preprocessing techniques for NLP The theory behind transformers How to fine-tune transformers We cover all this and more, I look forward to seeing you in the course!

Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?Then this is the right course for you!Welcome to one of the most comprehensive courses on&nbsp; Deep Learning in medical imaging!This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges.You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more.The following topics are covered:Num PyMachine Learning Theory Test/Train/Validation Data Splits Model Evaluation - Regression and Classification Tasks Tensors with PyTorch Convolutional Neural Networks Medical Imaging Interpretability of a network's decision - Why does the network do what it does?A state of the art high level PyTorch library: PyTorch-lightning Tumor Segmentation Three-dimensional dataand many more Why choose this specific Deep Learning with PyTorch for Medical Image Analysis course ?This course provides unique knowledge on the application of deep learning to highly complex and&nbsp; non-standard (medical) problems (in 2D and 3D) All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step. Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.You will learn skills and techniques that the vast majority of AI engineers do not have!</ul

Pada kursus ini, teman-teman akan belajar mengenai pengolahan citra dengan menggunakan Bahasa Python. Materi pada kursus ini didesain sesederhana mungkin agar teman-teman dapat lebih mudah dalam memahami materi yang disampaikan. Selain materi yang mudah dipahami dan dipelajari, materi pada kursus ini akan dikembangkan dan ditambahkan secara terus menerus seiring berkembangnya bidang computer vision atau pengolahan citra. Materi yang disajikan berawal dari materi paling sederhana yaitu pre-processing citra dan dilanjutkan dengan deep learning.Pada pre-processing citra, teman-teman akan belajar mengenai rotasi, shifting(pergeseran pixel), flipping, ruang warna dan masih banyak lagi. Pada materi ruang warna, teman-teman akan belajar juga mengenai perhitungan matematika secara manual sebelum implementasi dengan menggunakan python. Pada materi deep learning, teman-teman akan belajar mengenai Neural Network atau NN dan Convolutional Neural Network (CNNs). Materi yang akan dipelajari pada Neural Network berupa perhitungan matematika dari forward pass dan backward pass. Selain perhitungan manual, teman-teman juga akan belajar bagaimana cara mengimplementasikan Neural Network dengan menggunakan Bahasa Python dengan library PyTorch. Pada materi Convolutional Neural Network, teman-teman akan mempelajari bagaimana sebuah mesin mempelajar sebuah data dan membuat sebuah sistem Artificial Intelligence (AI) secara sederhana. Materi Convolutional Neural Network yang disajikan antara lain, bagaimana penerapan dengan menggunakan Bahasa Python dengan library PyTorch dan bagaimana contoh-contoh penggunaan Convolutional Neural Network dalam kehidupan sehari-hari.

Selamat datang di program Pelatihan Data Science dengan Deep Learning dan Python Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dengan titik fokus pemanfaatan Deep Learning untuk model machine learning dan data science.Peserta diharapkan sudah menguasai pemrograman Python dasar implementasi machine learning dan data science dengan menggunakan Python. Kami juga menyediakan konten mengenai Pelatihan Data Science dan Machine Learning Dengan Python yang ada di Udemy ini.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Konsep dan teori mengenai Deep Learning Pengenalan TensorFlow dan Keras Dasar Tensor TensorFlow Pemanfaatan GPU dan TPU pada komputasi TensorFlow dan Keras Pembuat Model dan Layer Untuk TensorFlow Training dan evaluasi Deep Learning pada TensorFlow Pengenalan dan instalasi PyTorch Pemanfaatan GPU dan TPU pada komputasi PyTorch Membangun model Deep Learning dengan PyTorch Training dan evaluasi Deep Learning pada PyTorch Penggunaan Tensor Board untuk visualisasi model pada TensorFlow dan PyTorch Penerapan Hyperparameter Tuning pada TensorFlow dan Keras Penerapan Hyperparameter Tuning pada PyTorch Penggunaan Tensor Board untuk implementasi Hyperparameter Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada we

A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina).A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando a biblioteca PyTorch o Python! Este curso apresenta desde os conceitos mais básicos sobre as redes neurais até técnicas mais modernas e avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Para isso, o conteúdo está dividido em sete partes: redes neurais artificiais, redes neurais convolucionais, autoencoders, redes adversariais generativas (GANs)</strong

Course Description This tutorial course is a practical, project driven introduction to Machine Learning and Deep Learning using PyTorch. Each concept is taught through real world examples, allowing professionals to quickly understand, how models work and how they are used in real applications. You will build complete end to end projects such as LSTMs based sentiment analysis, RNNs based spam detection, CNNs models for image classification, MLPs networks for video quality prediction, and regression models using real datasets from sales, finance, and home loan scenarios. This tutorial course also covers how to convert Jupyter Notebook experiments into a clean, modular Python project structure suitable for production use.By combining NLP, computer vision, and predictive analytics use cases, this tutorial course helps you gain solid practical experience in PyTorch while learning how to preprocess data, design model architectures, train models, evaluate results, and prepare solutions for real-world implementation.This Tutorial Course Primarily Focuses On:Building ML &amp; DL models end to end in PyTorch Performing data preprocessing and feature engineering Training, evaluating, and deploying models with real datasets Understanding architectures like LSTMs, CNNs, DNNs, Decision Trees, Random Forest &amp; MLPs Converting research notebooks into production ready Python modules By the end of this course, You will be able to Build machine learning regression &amp; classification models Develop CNNs, RNNs, MLPs, and LSTMs architectures in PyTorch Perform NLP tasks like sentiment analysis &amp; spam detection Implement image classification models for handwritten alphabets &amp; traffic signs Convert notebooks into modular Python project structures Work with real time data for prediction and quality assessment You will learn in this tutorial course Dec