This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to learn the fundamentals of computer vision.
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 the 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 MLApplications
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 & employees & 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
