Introduction
Welcome
General Introduction
Applications of Deep Learning in Computer Vision
Course Structure
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Deep Learning for Computer Vision
Learn the basics of Machine Learning. Dive deep into Deep Learning for Computer Vision Using TensorFlow 2. Master how to build, train, evaluate, test and Deploy Deep Learning Models. Understanding Key MLOps concepts. Going from beginner to solving real world problems efficiently!
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This Course Includes
Practice based lessons
More than 120 lessons amounting to more than 50 hours + of video
Access to exclusive Neuralearn community
Full Lifetime access to all courses and resources
Access on mobile and TVPrerequiscites
Basic MathA computer with an internet connection 
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Learning Path
Tensors and Variables
Basics
Initialization and Casting
Indexing
Maths Operations
Linear Algebra Operations
Common TensorFlow Functions
Ragged Tensors
Sparse Tensors
String Tensors
Variables
Loading and Saving Models
Building Neural Networks with TensorFlow [Car Price Prediction]
Task Understanding
Data Preparation
Linear Regression Model
Error Sanctioning
Training and Optimization
Performance Measurement
Validation and Testing
Corrective Measures
Building Convolutional Neural Networks with TensorFlow [Malaria Diagnosis]
Task Understanding
Data Preparation
Data Visualization
Data Processing
How and Why Convolutional Neural Networks Work
Building ConvNets with Tensorflow
Binary Crossentropy Loss
Training ConvNets
Model Evaluation and Testing
Loading and Saving tensorflow models to gdrive
Building more Advanced Models with TensorFlow [Malaria Diagnosis]
Task Understanding
Data Preparation
Data Visualization
Evaluating Classification Models [Malaria Diagnosis]
Precision, Recall, Accuracy
Confusion Matrix
ROC Curve
Improving Model Performance [Malaria Diagnosis]
Tensorflow Callbacks
Learning Rate Scheduling
Model Checkpointing
Understanding BLEU score
Mitigating Overfitting and Underfitting
Data Augmentation [Malaria Diagnosis]
Data augmentation with tf.image
Data augmentation with Keras Layers
Mixup Augmentation
Cutmix Augmentation
Albumentations
Advanced Tensorflow [Malaria Diagnosis]
Custom Loss and Metrics
Eager and Graph Modes
Custom Training Loops
Tensorboard Integration [Malaria Diagnosis]
Data Logging
View Model Graphs
Hyperparameter Tuning
Profiling and Visualizations
MLOps with Weights and Biases [Malaria Diagnosis]
Experiment Tracking
Hyperparameter Tuning
Dataset Versioning
Model Versioning
Human Emotions Detection
Data Preparation
Modeling and Training
Data Augmentation
Tensorflow records
Modern Convolutional Neural Networks [Human Emotions Detection]
AlexNet
VGGNet
ResNet
Coding ResNets from Scratch
MobileNet
How APIs work
Building API with Fastapi
EfficientNet
Transfer Learning [Human Emotions Detection]
Feature Extraction
Finetuning
Understanding the Blackbox [Human Emotions Detection]
Visualizing Intermediate Layers
Grad-Cam Method
Class Imbalance and Ensembling [Human Emotions Detection]
Ensembling
Class Imbalance
Transformers in Vision [Human Emotions Detection]
Understanding VITs
Building VITs from Scratch
Finetuning Huggingface VITs
Model Evaluation with Wandb
Data Efficient Transformers
Swin Transformers
Model Deployment [Human Emotions Detection]
Understanding Quantization
Conversion from to Onnx Model
Quantization of Onnx Model
Quantization Aware Training
Conversion to TensorflowLite model
How APIs work
Building API with Fastapi
Deploying API to the Cloud
Load Testing API
Object Detection with YOLO
Basics of object detection
Understanding YOLO algorithm
Dataset Preparation
Building and Training YOLO from scratch
Testing
Performance Measurement with Mean Average Precision
Image Segmentation with UNet
Understanding Image Segmentation
Training and Optimization
Class weighting and reduced network
People Counting
Understanding People Counting
Dataset Preparation
Building CSRNET
Data Augmentation
Image Generation
Variational Autoencoders
Digits generation with VAEs
How GANs work
The GAN loss
Improving GAN training
Face Generation with GANs
Learning Never Ends!!!
This Course Will Constantly be Updated
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