The Complete Deep Learning & Computer Vision Course in 2020

Category: Tutorial


Posted on 2020-11-12, by perica123.

Description

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The Complete Deep Learning & Computer Vision Course in 2020
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | Size: 5.89 GB | Duration: 15h 52m



What you'll learn
Using Latest Tools & Techniques in Deep Learning & Computer Vision
Learning how to used the latest Tensorflow 2.0
How to apply Transfer Learning, Ensemble Learning, using GPUs & TPUs
How to work & win Kaggle Competitions
Learning to use FastAI
How to use Generative Adversarial Networks
How to use Weights & Biases for recording Experiments
Learning to use Detectron2 for Object Detection
Making Machine Learning Web Application from Scratch
Learn how to use OpenCV for Computer Vision
How to make Real World Applications & Deploy into Cloud
Learning Techniques like Object Detection, Classification & Generation
Learning how to use Heroku for deploying ML models
Working on Kaggle Competitions & Kaggle Kernels
Exploring & Visualizing Datasets using popular libraries like Matplotlib & Plotly.
Learinng how to use libraries like Pandas, Sklearn, Numpy
Creating Advance Data Pipelines using Tensorflow for training Deep Learning Models
Setting up Environment & Project for Deep Learning & Computer Vision

Requirements
Basic Python programming knowledge
A Computer with Internet Connection
All tools used in this course are free to use

Description
This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert.

Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple,

Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world.

If We Want Machines to Think, We Need to Teach Them to See.-Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab

Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision.

All the tools, techniques & technologies used in this course -

Learning Computer Vision & Deep Learning Fundamentals

Setting up Anaconda, Installing Libraries & Jupyter Notebook

Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks

Advanced OpenCV - Image Preprocessing, Geometrical transformations ,Perspective transformations & affine transformations ,image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours

Working with videos in OpenCV - Using webcam, Haar Cascades & Object Detection, Lane Detection

Deep Learning & How Neural Network Works ? - Artificial neural networks, Convolution Neural Networks & Transfer Learning

Image Classification - Plant leaf Classification

Working on very recent Kaggle Competitions

Using Google Colab & Kaggle Kernels

Using latest Tensorflow 2.0 & Keras

Using Keras Data Generators & Data Argumentation

Using Transfer Learning & Ensemble learning

Using State of The Art Deep Learning Models

Using GPU & TPU for Model Training

Hyperparameter Tuning

Using Weights & Biases for recording Deep Learning experimentations

Saving & Loading Models

Creating a Weights & Biases Report & Showcasing the Project!

Object Detection - Wheat heads Detection

Working on Kaggle Competitions, again!

Using Facebook's Detectron2 for Object Detection

Creating COCO Dataset from scratch

Training Faster RCNN Model and Custom Weights & Biases callback

Using Retinanet

Saving & Loading Detectron2 models

Generative Adversarial Networks - Creating Fake Leaf Images

Learning How Generative Adversarial Networks works

Using FastAI

Creating & Training Generative Adversarial Networks

Making Fake Images using GAN

Making ML Web Application

Getting started with Streamlit

Creating an ML Web Application from scratch using Streamlit

making a React Web Application

Deploying ML Applications

Learning how to use Cloud Services to Deploy Models & Applications

Using Heroku

Learning how to Open Source Projects on GitHub

How to showcase your projects to impress boss & employes & Get Hired!

A lot of bonus lectures!

This is what included in the package

All lecture codes are available for downloadable for free

110+ HD video lectures ( over 50 more to come very soon! )

Free support in course Q/A

All videos with English captions available

This course is for you if..

... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision

... you want to get more experience to Win Kaggle Competitions

... you want to get started with Computer Vision to become a Computer Vision Engineer

.. you are interested in learning Image Classification, Object Detection, Generative Adversarial Networks, Making & Deploying Machine Learning Applications


Who this course is for:
You want to become a Computer Vision Engineer & Get Hired
Anyone who want to learn latest tools & techniques used in Computer Vision
You are already a Programmer and what to extend your skills by learning Computer Vision
Who want to learn new Tools & Techniques used in Computer Vision
You want to get more experience for winning Kaggle Competitions


Homepage
https://anonymz.com/?https://www.udemy.com/course/dlcourse/


1.courseoutlinesoektz.jpg



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