Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning.
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. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. 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. The first section prepares you with all the necessary basics to get started in deep learning. The overall book comprises three sections with two chapters in each section. 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. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. Learn-Keras-for-Deep-Neural-Networks * Jupyter Notebook 0 Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning) Mastering TensorFlow 1x, published by Packt Mastering-TensorFlow-1x * Jupyter Notebook 0 TensorFlow-From-Zero-To-One * Jupyter Notebook 0 R, Python, Machine Learning, Data Analysis, Data Visualization,Data Manipulation,Data Cleaning Mastering OpenCV 4 with Python, published by Packt Mastering-OpenCV-4-with-Python * Python 0 Mastering OpenCV 4, Third Edition, published by Packt publishing Mastering-OpenCV-4-Third-Edition * Assembly 0 Machine Learning Algorithms implementationsĪ Keras implementation of YOLOv3 (Tensorflow backend) Keras implementation of yolo v3 object detection.Īwesome Object Detection based on handong1587 github: Ĭode repository for Hands On OpenCV 4 with Python, published by PacktĪutomated-feature-engineering * Jupyter Notebook 0Īutomated feature engineering in Python with Featuretools Pure tensorflow Implement of YOLOv3 with support to train your own dataset Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow Simple Online Realtime Tracking with a Deep Association Metric 计算机视觉实践和探索/Practice and explorations in computer vision.Ī toolkit for making real world machine learning and data analysis applications in C++ Notebooks for my "Deep Learning with TensorFlow 2 and Keras" courseĮasyIPCamera-RTSPServer：An elegant, simple, high performance RTSP Server for smart-devices/desktop-application,such as Android-Camera/Windows-Desktop-LiveStreaming/ARM-IPCamera,also can run in Windows/Linux/ARM Platform,with flexible interface,u can use lots of video & audio source,very easy to use. Tensorflow2_tutorials_chinese * Jupyter Notebook 0
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JSONLab: a native JSON/UBJSON/MassagePack encoder/decoder for MATLAB/Octave Yolov3 implemented with brand new TensorFlow 2.0 API (both train and prediction) So it can be used directly after installed.Ĭonvolutional neural networks with Python 3 and Keras Tesseract Open Source OCR Engine (main repository)Ī python package for Chinese OCR with the available pre-trained model. Python-based tools for document analysis and OCR
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OCRmyPDF adds an OCR text layer to scanned PDF files, allowing them to be searched 💥 Tensorflow2.x Examples from basic to hard The Kalibr visual-inertial calibration toolboxĪ tensorflow_2.0 implementation of SSD (Single Shot MultiBox Detector).
#Jhelioviewer java software
License++ is a cross platform software licensing library that uses digital signatures to implement secure licensing into your application.ĭrench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!Ī PyTorch implementation of the YOLO v3 object detection algorithmĪccompanying code for Paperspace tutorial series "How to Implement YOLO v3 Object Detector from Scratch" Introduction to Machine Learning - UFES - 2016/2 - Course SlidesĪ curated list of deep learning resources for computer vision Openpose from CMU implemented using Tensorflow with Custom Architecture for fast inference. 简单粗暴 TensorFlow 2.0 | A Concise Handbook of TensorFlow 2.0
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