Certificate in Deep Learning

Mathematics for Deep Learning :

➥ Linear Algebra

    o Vectors & Matrices

    o Matrix Multiplication

    o Eigenvalues & Eigenvectors

➥ Calculus

    o Derivatives

    o Partial Derivatives

    o Chain Rule

➥ Probability & Statistics

    o Mean & Variance

    o Probability Distributions

    o Bayes Theorem

Python for Deep Learning :

➥ NumPy (Arrays, Broadcasting, Matrix Operations)

➥ Pandas (Data Handling & Cleaning)

➥ Matplotlib & Seaborn (Data Visualization)

Introduction to Machine Learning :

➥ Machine Learning vs Deep Learning

➥ Types of Learning :

    o Supervised Learning

    o Unsupervised Learning

➥ Overfitting & Underfitting

➥ Bias-Variance Tradeoff

Introduction to Neural Networks :

➥ Biological Neuron vs Artificial Neuron

➥ Perceptron Model

Activation Functions :

    o Sigmoid

    o ReLU

    o Tanh

    o Softmax

ANN Concepts :

➥ Forward Propagation

➥ Backpropagation Algorithm

Loss Functions :

    o MSE

    o Cross Entropy

Optimization Techniques :

    o Gradient Descent

    o Batch

    o Mini-Batch

    o Stochastic

Implementation with Frameworks :

➥ Introduction to TensorFlow

➥ Introduction to PyTorch

➥ Building First ANN Model

➥ Model Training & Evaluation

Mini Projects :

➥ Student Performance Prediction

➥ House Price Prediction

Convolutional Neural Networks (CNN) :

➥ Convolution Operation

➥ Filters & Kernels

➥ Pooling Layers

➥ Padding & Stride

➥ CNN Architecture Design

➥ Image Classification Project

Recurrent Neural Networks (RNN) :

➥ Sequential Data Processing

➥ Vanishing Gradient Problem

➥ LSTM

➥ GRU

Natural Language Processing (Basics) :

➥ Text Preprocessing

➥ Word Embeddings

➥ Sentiment Analysis Project

Transfer Learning :

➥ Pretrained Models

➥ Fine-Tuning Models

Advanced Architectures :

➥ Autoencoders

➥ GAN (Generative Adversarial Networks)

➥ Introduction to Transformers

Model Optimization :

➥ Dropout

➥ Regularization

➥ Batch Normalization

➥ Hyperparameter Tuning

➥ Saving & Loading Models

➥ Model Serialization Techniques

➥ Creating REST API using Flask

➥ Integrating Model with Frontend

➥ Basic Cloud Deployment

➥ Model Monitoring & Performance Tracking

➥ Introduction to MLOps Concepts

➥ End-to-End Deep Learning Project

➥ Dataset Collection & Preprocessing

➥ Model Building & Optimization

➥ Deployment as Web Application

➥ Documentation & Technical Report Writing

➥ Presentation & Viva