Ever since I started learning Deep Learning & Computer Vision, I always dreamed of this course!
This is the course I wish I had when I was getting started with Deep Learning & Computer Vision
What’s In this Course?
As the name suggests, this course is on learning the latest tools & technologies using in Deep Learning & Computer Vision. This course teaches a lot of topics like,
- Good old Image Classification!
- Object Detection
- Generative Adversarial Networks
- Making Machine Learning Web Application
- Deploying ML Web Applications & Open Sourcing ML Projects
I am using a lot of different tools like Anaconda, OpenCV, Tensorflow 2.0, Detectron2, FastAI, Streamlit, Heroku, and much much more.
What’s Unique in this course?
YES, Good Question!
- This course uses very recent Kaggle Competitions to work on & submitting predictions & analyzing winner solutions.
- This course uses Weights & Biases for saving every experiment we do and then making a report which is really wonderful to show off to potential employers.
- This course makes an actual Machine learning Web Application & Deploy it into a cloud, showcasing it to employers will automatically make you stand out from the rest.
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
- More things below!
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 & Open Sourcing ML Application
- Learning how to use Cloud Services to Deploy Models & Applications
- Using Heroku
- Learning how to Open Source ML Projects on GitHub
- Learn to build a good Readme focused on ML Projects
- How to showcase your projects to impress boss & employees & Get Hired!
And seriously, there are more projects that we will make in the course!
Feel free to message me with any questions!