DEEP LEARNING

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dl
Why Deep Learning is Important
in Data Science and AI

Deep Learning (DL) enables machines to learn complex patterns from large amounts of data, outperforming traditional ML in many areas.

It powers state-of-the-art AI systems in computer vision, speech recognition, NLP, and generative AI.

  • DL models can automatically perform feature extraction, eliminating the need for manual feature engineering.

  • Architectures like CNNs and RNNs specialize in handling images, videos, time series, and sequential data.

  • DL is at the core of modern breakthroughs like ChatGPT, AlphaGo, autonomous driving, and DeepFake generation.

  • It enables transfer learning, where pre-trained models can solve new tasks with minimal data and compute.

  • Frameworks like TensorFlow and PyTorch make DL development accessible and scalable.

  • DL fuels innovation in healthcare, robotics, fintech, and education, pushing the frontiers of AI.

  • It supports the development of reinforcement learning agents in gaming, robotics, and simulations.

  • Deep Learning is essential for building end-to-end intelligent systems that can sense, understand, and act.

dl
01

Module 1: Introduction to Deep Learning

  1. What is Deep Learning?

  2. Deep Learning vs Machine Learning

  3. History and evolution of DL

  4. Applications of DL in AI

  5. DL frameworks: TensorFlow, PyTorch, Keras

02

Module 2: Math Foundations for Deep Learning

  1. Vectors, matrices, and tensors

  2. Linear transformations

  3. Derivatives and gradients

  4. Chain rule and backpropagation

  5. Activation functions: Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax

03

Module 3: Neural Networks Basics

  1. Perceptron and multi-layer perceptrons (MLPs)

  2. Forward propagation

  3. Loss functions: MSE, Cross-Entropy

  4. Backpropagation and weight updates

  5. Optimization algorithms:

    1. Gradient Descent

    2. SGD, Adam, RMSprop

04

Module 4: Building Neural Networks with Keras/PyTorch

  1. Model architecture using Sequential and functional API

  2. Compiling and training a model

  3. Evaluating performance

  4. Saving and loading models

  5. Custom training loops (PyTorch)

05

Module 5: Regularization and Optimization

  1. Overfitting vs underfitting

  2. Regularization techniques:

    1. L1, L2

    2. Dropout

    3. Early stopping

  3. Batch normalization

  4. Learning rate scheduling

06

Module 6: Convolutional Neural Networks (CNNs)

  1. Convolution operation and filters

  2. Padding, stride, pooling

  3. Building CNNs in Keras/PyTorch

  4. Image classification with CNN

  5. Transfer learning with pre-trained models (VGG, ResNet)

07

Module 7: Recurrent Neural Networks (RNNs)

  1. Introduction to sequence modeling

  2. RNN architecture and limitations

  3. Long Short-Term Memory (LSTM)

  4. Gated Recurrent Units (GRU)

  5. Applications in time series, speech, and text

08

Module 8: Natural Language Processing with DL

  1. Tokenization and word embeddings (Word2Vec, GloVe)

  2. Embedding layers

  3. Sentiment analysis with LSTM

  4. Attention mechanism basics

  5. Introduction to Transformers (BERT, GPT overview)

09

Module 9: Generative Models

  1. Autoencoders:

    1. Undercomplete and Denoising

  2. Variational Autoencoders (VAEs)

  3. Generative Adversarial Networks (GANs):

    1. Generator vs Discriminator

    2. Applications: Deepfakes, image generation

10

Module 10: Deployment and Inference

  1. Converting models for deployment

  2. TensorFlow Lite, ONNX, TorchScript

  3. Inference optimization for edge devices

  4. Model serving with Flask / FastAPI

  5. Using DL models in web apps or mobile apps

11

Module 11: Tools and Libraries

  1. TensorBoard for visualization

  2. Weights & Biases or MLflow for tracking experiments

  3. GPU acceleration with CUDA and cuDNN

  4. Hugging Face Transformers library (intro)

12

Module 12: Advanced Topics

  1. Attention and Transformers in detail

  2. BERT, GPT, Vision Transformers

  3. Reinforcement Learning introduction

  4. Neural Architecture Search

  5. Large Language Models (LLMs) overview

13

Module 13: Real-world Projects

  1. Image classification on CIFAR-10 / MNIST

  2. Sentiment classification with IMDB/Yelp dataset

  3. Object detection (YOLO or SSD intro)

  4. Time series forecasting (stock prices, sensors)

  5. Build a chatbot with deep learning

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