Ai Masterclass: Deep Learning Fundamentals To Advanced Tech

Master deep learning from mathematical foundations to hands-on model deployment—everything in one unique course!

What you'll learn

Gain hands-on experience with neural networks, optimization algorithms, and deep learning frameworks like TensorFlow and PyTorch to build and deploy models.

Learn to design, train, and deploy deep learning models for real-world applications such as image recognition and NLP with practical, project-based learning

Master key concepts in linear algebra, calculus, and probability to deepen your understanding of how deep learning models work and improve them.

Understand the real industry projects to gain practical experience in model building, evaluation, and optimization

Requirements

To take the Deep Learning Bootcamp, a basic understanding of Python programming is required, as it is the primary language used in deep learning frameworks like TensorFlow and PyTorch. While not mandatory, having a basic grasp of high school-level mathematics, including algebra, calculus, and probability, will help you understand the core mathematical principles behind deep learning models better. Familiarity with machine learning concepts such as supervised/unsupervised learning and classification is also helpful but not essential. Above all, a strong enthusiasm to learn, experiment, and solve real-world AI problems is key, as the course focuses on hands-on experience and practical application of deep learning techniques. With these prerequisites, you'll be well-prepared to embark on your deep learning journey.

Description

Unlock the power of artificial intelligence with our Deep Learning Bootcamp! Dive into the world of neural networks, reinforcement learning, and cutting-edge AI applications through hands-on projects and real-world examples. Whether you're a beginner or looking to deepen your understanding, this course provides you with a comprehensive, practical approach to mastering deep learning. From building models in TensorFlow and PyTorch to learning the mathematical foundations behind each algorithm, you'll gain the skills needed to tackle complex AI challenges. The course uniquely integrates both mathematical theory and practical application—a rare combination not found in other courses. You'll not only understand the math behind optimization, backpropagation, and model evaluation, but also gain valuable experience in deploying deep learning models into real-world environments. Learn to transform theoretical knowledge into tangible results by building intelligent systems, optimizing them, and making them production-ready. Prepare to experiment, optimize, and deploy your own deep learning solutions, while staying ahead in the rapidly evolving field of AI. With hands-on labs, real-world case studies, and practical deployment experience, this course equips you with everything you need to succeed in the industry. Through expert mentorship and collaborative projects, you’ll gain the confidence to apply deep learning techniques to solve real-world problems. Start building intelligent systems today and transform your career with deep learning expertise!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Introduction to Deep Learning

Lecture 2 Overview of Machine Learning vs. Deep Learning

Lecture 3 Applications of Deep Learning (Vision, NLP, Robotics)

Lecture 4 Introduction to Neural Networks

Lecture 5 Tools & Frameworks: TensorFlow, PyTorch

Lecture 6 Setting up the environment (Colab, local setup)

Section 3: Math Foundations for Deep Learning (optional)

Lecture 7 Linear Algebra (Matrices, Vectors, Dot Products)

Lecture 8 Calculus (Derivatives, Chain Rule)

Lecture 9 Probability and Statistics Basics

Lecture 10 Optimization Fundamentals (Gradient Descent)

Section 4: Neural Networks Architecture

Lecture 11 Structure of a Neural Network (Layers, Neurons, Weights)

Lecture 12 Activation Functions (ReLU, Sigmoid, Tanh)

Lecture 13 Forward and Backward Propagation

Lecture 14 Loss Functions (MSE, Cross-Entropy)

Section 5: Training Deep Neural Networks

Lecture 15 Overfitting and Underfitting

Lecture 16 Regularization Techniques (Dropout, L1/L2 Regularization)

Lecture 17 Optimizers: SGD, Adam, RMSProp

Lecture 18 Learning Rate Schedules

Lecture 19 Model Evaluation Metrics

Section 6: Convolutional Neural Networks (CNNs)

Lecture 20 Basics of Image Data

Lecture 21 Convolutional Neural Networks (CNNs) architecture

Lecture 22 Convolutional Layers, Pooling Layers and other key components

Lecture 23 Architectures: LeNet, AlexNet, VGG, ResNet, EfficientNetV2

Lecture 24 Applications: Image Classification, Object Detection

Section 7: Recurrent Neural Networks (RNNs) & Sequence Models

Lecture 25 Basics of Sequence Data

Lecture 26 RNN, LSTM, GRU Architectures

Lecture 27 Applications: Text Generation, Time Series Forecasting

Lecture 28 Attention Mechanisms

Section 8: Transformer Models and NLP

Lecture 29 Introduction to Transformers

Lecture 30 BERT, GPT, and other popular architectures

Lecture 31 Hands on Applications: Text Classification using BERT

Lecture 32 Hands-on GPT for Text Generation with Hugging Face

Section 9: Generative Models

Lecture 33 Variational Autoencoders (VAEs)

Lecture 34 Generative Adversarial Networks (GANs)

Lecture 35 Hands on Applications: Image Generation

Lecture 36 Real Life applications and Ethical Implications of Generative Models

Section 10: Deep reinforcement Learning

Lecture 37 Basics of Reinforcement Learning (RL)

Lecture 38 Deep Q-Learning

Lecture 39 Policy Gradient Methods

Section 11: Scaling and Deploying Deep Learning Models

Lecture 40 Model Deployment: TensorFlow Serving, TorchServe

Lecture 41 Scaling with GPUs and TPUs

Lecture 42 Model Optimization (Quantization, Pruning)

Lecture 43 Real-world deployment examples

Section 12: Ethical AI and Model Interpretability

Lecture 44 Bias and Fairness in AI

Lecture 45 Explainability Techniques (SHAP, LIME)

Lecture 46 Privacy-Preserving AI

Lecture 47 Case Studies on Ethical AI Challenges

This Deep Learning Bootcamp is designed for: Aspiring Data Scientists & AI Enthusiasts: Those looking to build a strong foundation in deep learning and develop hands-on experience with real-world AI projects. Machine Learning Practitioners: Individuals with basic machine learning knowledge who want to dive deeper into deep learning techniques and frameworks like TensorFlow and PyTorch. Software Engineers: Developers interested in learning how to integrate deep learning models into applications, from data preprocessing to model deployment. Researchers & Academics: Those seeking to expand their knowledge of advanced AI methods and explore deep learning for scientific research or innovation. Tech Entrepreneurs & Innovators: Individuals looking to apply deep learning to create AI-powered products, solutions, or services that can transform industries. This course is ideal for anyone passionate about AI who wants to gain practical, hands-on experience while understanding the theory and math behind deep learning models.