Master Deep Learning for Computer Vision in TensorFlow[2025]

Use ConvNets & Vision Transformers to build projects in Image classification,generation,segmentation & Object detection

Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of fields in which deep learning has the most influence today is Computer Vision.Object detection, Image Segmentation, Image Classification, Image Generation & People Counting

To understand why Deep Learning based Computer Vision is so popular; it suffices to take a look at the different domains where giving a computer the power to understand its surroundings via a camera has changed our lives.

Some applications of Computer Vision are:

• Helping doctors more efficiently carry out medical diagnostics

• enabling farmers to harvest their products with robots, with  the need for very little human intervention,

• Enable self-driving cars

• Helping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brain

• Creation of art  with GANs, VAEs, and Diffusion Models

• Data analytics in sports, where players' movements are monitored automatically using sophisticated computer vision algorithms.

The demand for Computer Vision engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(

In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression model for car price prediction and binary classifier for malaria prediction) using Tensorflow to much more advanced models (like object detection model with YOLO and Image generation with GANs).

After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep learning for computer vision solutions that big tech companies encounter.

You will learn: 

• The Basics of TensorFlow (Tensors, Model building, training, and evaluation)

• Deep Learning algorithms like Convolutional neural networks and Vision Transformers

• Evaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)

• Mitigating overfitting with Data augmentation

• Advanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, Tensorboard

• Machine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)

• Binary Classification with Malaria detection

• Multi-class Classification with Human Emotions Detection

• Transfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)

• Object Detection with YOLO (You Only Look Once)

• Image Segmentation with UNet

• People Counting with Csrnet

• Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)

• Digit generation with Variational Autoencoders

• Face generation with Generative Adversarial Neural Networks

  
  

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!

This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Enjoy!!!