TensorFlow Binary and Multi-class Classification Tutorial
Learn TensorFlow for binary and multi-class classification, covering data handling, model building, optimization, and evaluation techniques for effective machine learning implementations.
TensorFlow Tutorial 2022
Comprehensive TensorFlow tutorial covering tensors, data handling, neural networks, and model building. Learn to create, compile, and evaluate machine learning models using practical examples.
Implementing a Neural Network for Music Genre Classification
Implement a music genre classifier using TensorFlow and MFCC features. Learn key deep learning concepts like binary/multiclass classification, ReLUs, batching, and overfitting while building the neural network.
How to Implement a Simple Neural Network With TensorFlow 2
Learn to implement a simple neural network using TensorFlow 2 and Keras for arithmetic sum, covering model building, training, testing, evaluation, and prediction.
Machine Learning for Continuous Integration - Andrea Frittoli & Kyra Wulffert - ODSC Europe 2019
Explore machine learning techniques for analyzing CI/CD pipeline data, extracting insights, and automating failure identification using open-source tools like TensorFlow and Pandas with real-life OpenStack community data.
CI-CD for Machine Learning
Automate ML model training and evaluation using GitLab CI/CD. Learn to allocate cloud instances, generate performance reports, transfer data, and customize workflows for efficient machine learning development.
Introduction to Neural Networks in Python - Tensorflow-Keras
Learn neural network basics, architecture, hyperparameters, and implementation in Python using TensorFlow/Keras. Includes hands-on coding examples for various classification tasks, from simple to complex datasets.
Solving Real World Data Science Problems With Python - Computer Vision Edition
Learn to build and improve convolutional neural networks for computer vision tasks using Python, TensorFlow, and Keras. Explore data augmentation, preprocessing, and network architecture optimization techniques.
How to Implement Autoencoders in Python and Keras - The Encoder
Learn to implement autoencoders in Python and Keras, focusing on building the encoder component. Covers autoencoder class construction, convolutional layers, bottleneck creation, and practical implementation.
How to Implement a CNN for Music Genre Classification
Implement a CNN for music genre classification using TensorFlow. Learn cross-validation, inference, and practical application with code examples and step-by-step guidance.
Building and Training an Autoencoder in Keras - TensorFlow - Python
Learn to build and train autoencoders using Python, TensorFlow, and Keras. Explore encoder-decoder architectures, implement training with the MNIST dataset, and gain practical insights into autoencoder development.
Generative Adversarial Networks for Image Synthesis and Translation - Dr. Jan Kautz
Explore recent GAN advancements for image synthesis and translation, including conditional GANs, supervised vs unsupervised methods, and multimodal approaches. Learn to design, modify, and understand GAN architectures.
Deep Learning for Real Time Streaming Data with Kafka and TensorFlow - ODSC East 2019
Learn to process real-time streaming data using Apache Kafka and TensorFlow's KafkaDataset module for efficient machine learning applications in mission-critical scenarios.
Fine-Tuning Alpaca- Train Alpaca LoRa for Sentiment Analysis on a Custom Dataset
Learn to fine-tune Llama 7B with Alpaca LoRa on a custom bitcoin sentiment dataset. Covers data preprocessing, model training, and performance analysis for sentiment detection in cryptocurrency tweets.
Deep Learning with Neural Networks and TensorFlow
Learn to train neural networks using TensorFlow, covering session setup, cost function implementation, and result analysis for deep learning models.
Deep Learning with Neural Networks and TensorFlow
Learn to build a deep neural network using TensorFlow and the MNIST dataset. Explore feature extraction, model creation, and digit prediction techniques in this hands-on tutorial.
Deep Learning with Neural Networks and TensorFlow Introduction
Introduction to deep learning with neural networks, covering theory, models, optimization, and TensorFlow. Explore biologically-inspired machine learning techniques and their evolution in the field of artificial intelligence.
Machine Learning Tutorial for Beginners
Comprehensive TensorFlow tutorial covering data handling, neural networks, and model building. Learn to clean, analyze, and process data for machine learning applications.
MATS Stack for Cross-System Orchestration of Machine Learning Pipelines
Explore the MATS stack for orchestrating ML pipelines across systems, featuring MLFlow, Airflow, Tensorflow, and Spark. Learn how Avast leverages this approach for efficient phishing detection and streamlined ML workflows.
Productionizing Machine Learning Pipelines with Databricks and Azure ML
Jumpstart ML projects with Databricks and Azure ML. Learn to build, track, and deploy production-ready pipelines using tools like MLflow, TensorFlow, and PyTorch for efficient, reproducible data science workflows.
Productionizing Deep Reinforcement Learning with Spark and MLflow
Aprenda a aplicar Deep Reinforcement Learning em escala usando Spark e MLflow para personalização de jogos e aumento de engajamento, com dicas práticas para superar desafios de produção.