Prerequisites (Before Deep Learning)

Make sure you're comfortable with:

1. Mathematics

  • Linear Algebra: Vectors, matrices, dot product, eigenvalues, SVD

  • Calculus: Partial derivatives, chain rule, gradients

  • Probability & Statistics: Bayes theorem, distributions, expectation, variance

2. Programming (Python)

  • NumPy, Pandas, Matplotlib

  • Jupyter Notebooks

  • Understanding how to build simple projects

3. Machine Learning (Basics)

  • Supervised vs. Unsupervised Learning

  • Regression, Classification, Clustering

  • Overfitting/Underfitting, bias-variance tradeoff

  • Evaluation metrics: accuracy, precision, recall, F1-score

  • Scikit-learn basics

🧠 Core Deep Learning Topics

1. Introduction to Neural Networks

  • Perceptron and Multi-layer Perceptrons (MLP)

  • Activation Functions: Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax

  • Forward and Backward Propagation

  • Loss Functions (MSE, Cross-Entropy)

  • Gradient Descent and Variants: SGD, Momentum, RMSProp, Adam

  • Weight Initialization Techniques (Xavier, He Initialization)

  • Regularization: L1, L2, Dropout, Early stopping

2. Deep Neural Networks (DNNs)

  • Architecture design (layers, units, depth)

  • Vanishing and Exploding Gradients

  • Batch Normalization

  • Hyperparameter Tuning

  • Data Preprocessing and Augmentation

3. Convolutional Neural Networks (CNNs)

  • Convolution, Padding, Stride

  • Filters/Kernels

  • Pooling (Max, Average)

  • Architectures: LeNet, AlexNet, VGG, GoogLeNet (Inception), ResNet

  • Transfer Learning

  • Feature Maps and Visualization

4. Recurrent Neural Networks (RNNs)

  • RNN architecture and unfolding

  • Backpropagation Through Time (BPTT)

  • Exploding/Vanishing Gradients

  • Long Short-Term Memory (LSTM)

  • Gated Recurrent Units (GRU)

  • Sequence Modeling (text, time series)

  • Applications: sentiment analysis, language modeling, translation

5. Natural Language Processing (NLP) with Deep Learning

  • Text Preprocessing: Tokenization, Lemmatization, Stopwords

  • Word Embeddings: Word2Vec, GloVe, FastText

  • Sequence-to-Sequence Models

  • Attention Mechanism

  • Transformers (Basic architecture)

  • BERT, GPT, T5 (basic understanding)

6. Generative Models

  • Autoencoders

    • Basic Autoencoders

    • Variational Autoencoders (VAE)

  • Generative Adversarial Networks (GANs)

    • Generator & Discriminator

    • Loss functions

    • DCGAN, Conditional GAN

    • Applications (image generation, deepfakes)

7. Advanced Deep Learning Topics

  • Transformers in depth

    • Multi-head attention

    • Positional encoding

    • Encoder-Decoder structure

  • Large Language Models (LLMs): BERT, GPT (architecture, training, fine-tuning)

  • Reinforcement Learning (Basics + Deep RL)

    • Q-learning, Deep Q-Networks (DQN)

  • Self-Supervised Learning

  • Contrastive Learning

  • Meta Learning and Few-Shot Learning

🧰 Frameworks and Tools

  • TensorFlow / Keras

  • PyTorch (preferred for research and flexibility)

  • Hugging Face Transformers (for NLP)

  • OpenCV (for image processing)

  • MLflow / Weights & Biases (for experiment tracking)

🧪 Projects to Practice

  • Image classification (CIFAR-10, MNIST)

  • Object detection (YOLO, SSD)

  • Face recognition

  • Sentiment analysis

  • Chatbot with Seq2Seq

  • Text summarization

  • Image captioning

  • Style transfer

  • GAN for face generation

  • Custom LLM fine-tuning

📚 Suggested Learning Path

  1. Deep Learning Specialization – Andrew Ng (Coursera)

  2. Fast.ai Deep Learning Courses

  3. CS231n (Stanford): Convolutional Neural Networks

  4. CS224n (Stanford): NLP with Deep Learning

  5. Dive into Deep Learning (Free interactive book: d2l.ai)

  6. Practical Deep Learning for Coders (Fast.ai)