1. About Deep Learning & Neural Networks
Deep Learning involves training artificial neural networks with multiple layers (hence “deep”) to learn hierarchical representations of data. It is widely used in industries like healthcare, finance, autonomous vehicles, and entertainment. Neural Networks mimic the human brain’s structure, with layers of interconnected nodes (neurons) that process input data and produce output predictions.
Key Applications:
- Computer Vision : Image classification, object detection, facial recognition.
- Natural Language Processing (NLP) : Sentiment analysis, machine translation, chatbots.
- Speech Recognition : Voice assistants like Siri, Alexa.
- Reinforcement Learning : Autonomous driving, game-playing AI.
2. Why Learn Deep Learning & Neural Networks?
- Cutting-Edge Technology : DL powers state-of-the-art AI systems like GPT, AlphaGo, and self-driving cars.
- High Demand : Deep Learning engineers are highly sought after in industries like tech, healthcare, and robotics.
- Versatility : Used in diverse fields like healthcare (medical imaging), finance (fraud detection), and entertainment (content recommendation).
- Automation : Automate complex tasks like image recognition, speech synthesis, and decision-making.
- Research Opportunities : Contribute to groundbreaking research in AI and ML.
3. Full Syllabus
Phase 1: Basics (Weeks 1–4)
- Introduction to Deep Learning
- What is Deep Learning?
- Difference between Machine Learning and Deep Learning.
- Key Terminology: Neurons, Layers, Weights, Bias, Activation Functions.
- Mathematics for Deep Learning
- Linear Algebra: Vectors, Matrices, Dot Products.
- Calculus: Gradients, Partial Derivatives, Chain Rule.
- Probability & Statistics: Distributions, Expectation, Variance.
- Programming Basics
- Learn Python (the most popular language for DL).
- Libraries: NumPy (for numerical computations), Pandas (for data manipulation).
- Neural Network Fundamentals
- Perceptron: The simplest neural network.
- Activation Functions: Sigmoid, ReLU, Softmax.
- Loss Functions: Mean Squared Error (MSE), Cross-Entropy Loss.
Phase 2: Intermediate (Weeks 5–8)
- Building Neural Networks
- Feedforward Neural Networks (FNN): Input, Hidden, Output Layers.
- Backpropagation: How neural networks learn by minimizing loss.
- Optimization Algorithms: Gradient Descent, Stochastic Gradient Descent (SGD), Adam.
- Deep Learning Frameworks
- TensorFlow: Google’s open-source library for building neural networks.
- PyTorch: Facebook’s flexible library for deep learning.
- Keras: High-level API for TensorFlow.
- Convolutional Neural Networks (CNNs)
- Architecture: Convolutional Layers, Pooling Layers, Fully Connected Layers.
- Applications: Image Classification, Object Detection.
- Popular Models: LeNet, AlexNet, VGG, ResNet.
- Recurrent Neural Networks (RNNs)
- Architecture: Sequential Data Processing.
- Variants: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU).
- Applications: Time Series Prediction, NLP.
Phase 3: Advanced (Weeks 9–12)
- Advanced Architectures
- Transformers: Attention Mechanism, Self-Attention, Multi-Head Attention.
- Generative Adversarial Networks (GANs): Generator vs Discriminator.
- Autoencoders: Dimensionality Reduction, Denoising.
- Transfer Learning
- Use pre-trained models (e.g., ResNet, BERT) for your own tasks.
- Fine-tuning pre-trained models for specific datasets.
- Natural Language Processing (NLP)
- Word Embeddings: Word2Vec, GloVe, FastText.
- Transformer-Based Models: BERT, GPT, T5.
- Applications: Text Generation, Sentiment Analysis, Translation.
- Hyperparameter Tuning & Regularization
- Techniques: Dropout, Batch Normalization, Early Stopping.
- Tools: Grid Search, Random Search, Bayesian Optimization.
Phase 4: Real-World Applications (Weeks 13–16)
- Deploying Deep Learning Models
- Save and load models using TensorFlow SavedModel or PyTorch TorchScript.
- Deploy models using Flask/Django (for APIs) or cloud platforms like AWS, GCP, or Azure.
- Big Data & Scalability
- Work with large datasets using tools like Apache Spark.
- Use distributed training frameworks like Horovod or TensorFlow Distributed.
- Reinforcement Learning
- Understand Markov Decision Processes (MDPs).
- Algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods.
- Ethics in AI
- Bias and Fairness in Deep Learning Models.
- Privacy Concerns: Differential Privacy, Federated Learning.
4. Projects to Do
Beginner Projects
- Handwritten Digit Recognition :
- Classify handwritten digits using a simple CNN.
- Dataset: MNIST.
- Framework: TensorFlow/Keras or PyTorch.
- Image Classification :
- Build a model to classify images of cats and dogs.
- Dataset: CIFAR-10 or Kaggle Cats vs Dogs.
- Framework: TensorFlow/Keras or PyTorch.
- Sentiment Analysis :
- Analyze the sentiment of movie reviews using RNNs or LSTMs.
- Dataset: IMDb Movie Reviews.
- Framework: TensorFlow/Keras or PyTorch.
Intermediate Projects
- Object Detection :
- Detect objects in images using YOLO (You Only Look Once) or Faster R-CNN.
- Dataset: COCO or Pascal VOC.
- Framework: TensorFlow Object Detection API or PyTorch.
- Text Generation :
- Generate text using an LSTM or Transformer-based model.
- Dataset: Shakespeare Text or Wikipedia Articles.
- Framework: TensorFlow/Keras or PyTorch.
- Facial Recognition :
- Build a system to recognize faces using Siamese Networks or FaceNet.
- Dataset: LFW (Labeled Faces in the Wild).
- Framework: TensorFlow/Keras or PyTorch.
Advanced Projects
- Generative Adversarial Networks (GANs) :
- Generate realistic images using GANs.
- Dataset: CelebA or CIFAR-10.
- Framework: TensorFlow/Keras or PyTorch.
- Autonomous Driving Simulation :
- Train a reinforcement learning agent to drive in a simulated environment.
- Framework: OpenAI Gym or CARLA Simulator.
- Chatbot Development :
- Build a conversational AI using Transformer-based models like GPT or BERT.
- Framework: Hugging Face Transformers.
5. YouTube Links for Learning Deep Learning
English Resources
- DeepLearning.AI (Andrew Ng) :
- Sentdex :
- freeCodeCamp :
- 3Blue1Brown :
Hindi Resources
- CodeWithHarry :
- Thapa Technical :
- Hitesh Choudhary :
6. Final Tips
- Start Small : Begin with simple projects like MNIST digit classification to understand the basics of neural networks.
- Practice Daily : Spend at least 1 hour coding every day.
- Focus on Math : A strong foundation in linear algebra, calculus, and probability is crucial for understanding DL concepts.
- Stay Updated : Follow blogs like Towards Data Science , Medium , or Analytics Vidhya for the latest updates.
- Join Communities : Engage with forums like Reddit’s r/MachineLearning or Discord groups for support.
100-Day Master Plan
| 1 | Introduction to Deep Learning & Setting Up Environment | Deep Learning Basics |
| 2 | Python Basics for Deep Learning (NumPy, Pandas) | Python Official Docs |
| 3 | Linear Algebra for Deep Learning | Linear Algebra |
| 4 | Probability & Statistics for Deep Learning | Probability & Stats |
| 5 | Calculus for Deep Learning (Gradients, Derivatives) | Calculus for DL |
| 6 | Introduction to Neural Networks | Neural Networks |
| 7 | Perceptron Model | Perceptron |
| 8 | Activation Functions (ReLU, Sigmoid, Tanh, Softmax) | Activation Functions |
| 9 | Loss Functions (MSE, Cross-Entropy) | Loss Functions |
| 10 | Gradient Descent Optimization | Gradient Descent |
| 11 | Backpropagation | Backpropagation |
| 12 | TensorFlow Basics | TensorFlow Docs |
| 13 | Keras Basics | Keras Docs |
| 14 | Building Your First Neural Network | Neural Network Example |
| 15 | Convolutional Neural Networks (CNNs) | CNN Tutorial |
| 16 | CNN Architectures (LeNet, AlexNet, VGG, ResNet) | CNN Architectures |
| 17 | Transfer Learning with Pretrained Models | Transfer Learning |
| 18 | Recurrent Neural Networks (RNNs) | RNN Tutorial |
| 19 | Long Short-Term Memory (LSTM) Networks | LSTM Tutorial |
| 20 | Gated Recurrent Units (GRUs) | GRU Tutorial |
| 21 | Sequence-to-Sequence Models (Seq2Seq) | Seq2Seq |
| 22 | Attention Mechanisms | Attention Mechanism |
| 23 | Transformers | Transformers |
| 24 | Generative Adversarial Networks (GANs) | GAN Tutorial |
| 25 | Variational Autoencoders (VAEs) | VAE Tutorial |
| 26 | Autoencoders | Autoencoder Tutorial |
| 27 | Reinforcement Learning Basics | Reinforcement Learning |
| 28 | Q-Learning | Q-Learning |
| 29 | Deep Q-Networks (DQN) | DQN Tutorial |
| 30 | Policy Gradient Methods | Policy Gradient |
| 31 | Actor-Critic Methods | Actor-Critic |
| 32 | Proximal Policy Optimization (PPO) | PPO Tutorial |
| 33 | Natural Language Processing (NLP) Basics | NLP Tutorial |
| 34 | Word Embeddings (Word2Vec, GloVe) | Word Embeddings |
| 35 | Text Preprocessing (Tokenization, Stemming, Lemmatization) | Text Preprocessing |
| 36 | Bag of Words & TF-IDF | Bag of Words |
| 37 | Sentiment Analysis | Sentiment Analysis |
| 38 | Topic Modeling (Latent Dirichlet Allocation – LDA) | LDA Tutorial |
| 39 | Named Entity Recognition (NER) | NER Tutorial |
| 40 | Machine Translation (Seq2Seq + Attention) | Machine Translation |
| 41 | Text Generation (RNNs + LSTMs) | Text Generation |
| 42 | Vision Transformers (ViTs) | Vision Transformers |
| 43 | Multimodal Models (Image + Text) | Multimodal Models |
| 44 | Self-Supervised Learning | Self-Supervised Learning |
| 45 | Federated Learning Basics | Federated Learning |
| 46 | Explainable AI (SHAP, LIME) | Explainable AI |
| 47 | Hyperparameter Tuning for Deep Learning | Hyperparameter Tuning |
| 48 | Deployment of Deep Learning Models (Flask API) | Deploy DL Models |
| 49 | MLOps for Deep Learning | MLOps Guide |
| 50 | Finalize and Document Your Projects | Documentation Best Practices |
| 51 | Build a Handwritten Digit Recognizer (MNIST Dataset) | MNIST Dataset |
| 52 | Image Classification (CIFAR-10 Dataset) | CIFAR-10 Dataset |
| 53 | Object Detection (YOLO) | YOLO Tutorial |
| 54 | Face Recognition (OpenCV + CNN) | Face Recognition |
| 55 | Chatbot Development (Seq2Seq Models) | Chatbot Tutorial |
| 56 | Music Genre Classification (Audio Features) | GTZAN Dataset |
| 57 | Stock Price Prediction (LSTM) | Stock Price Prediction |
| 58 | Sentiment Analysis on Twitter Data | Twitter Sentiment |
| 59 | Fake News Detection (NLP + ML) | Fake News Dataset |
| 60 | Traffic Sign Recognition (CNN) | Traffic Signs Dataset |
| 61 | Credit Card Fraud Detection (Anomaly Detection) | Fraud Detection |
| 62 | Sales Forecasting (Time Series) | Sales Forecasting |
| 63 | Disease Prediction (Healthcare Dataset) | Disease Prediction |
| 64 | Wine Quality Prediction (Regression) | Wine Quality Dataset |
| 65 | Loan Default Prediction (Classification) | Loan Dataset |
| 66 | Energy Consumption Prediction (Regression) | Energy Dataset |
| 67 | Human Activity Recognition (Multiclass Classification) | Activity Recognition |
| 68 | Weather Forecasting (Time Series) | Weather Dataset |
| 69 | Product Recommendation System (E-commerce Dataset) | E-commerce Dataset |
| 70 | Customer Churn Prediction (Classification) | Churn Dataset |
| 71 | Medical Image Classification (X-Ray Images) | X-Ray Dataset |
| 72 | Speech Emotion Recognition (Audio Features) | Speech Emotion Dataset |
| 73 | Autonomous Driving Simulation (Deep Reinforcement Learning) | Self-Driving Cars |
| 74 | Build a REST API for DL Models (FastAPI) | FastAPI Docs |
| 75 | Optimize DL Models (Hyperparameter Tuning) | Optimization Techniques |
| 76 | Build a Custom Transformer for NLP | Custom Transformers |
| 77 | Multi-Label Classification (Toxic Comment Classification) | Toxic Comments |
| 78 | Build a GAN for Image Generation | GAN Tutorial |
| 79 | Build a Reinforcement Learning Agent (CartPole) | CartPole |
| 80 | Build a Multi-Agent RL System | Multi-Agent RL |
| 81 | Build a Recommendation System with Graph Neural Networks (GNNs) | GNNs |
| 82 | Build a Vision Transformer (ViT) | Vision Transformers |
| 83 | Build a Multimodal Model (Image + Text) | Multimodal Models |
| 84 | Build a Self-Supervised Learning Model | Self-Supervised Learning |
| 85 | Deploy a Flask App for DL Model (Heroku) | Heroku Deployment Guide |
| 86 | Build a Custom Object Detector (YOLOv5) | YOLOv5 |
| 87 | Build a Style Transfer Model | Style Transfer |
| 88 | Build a Super-Resolution Model | Super-Resolution |
| 89 | Build a Multilingual Translation Model | Translation Model |
| 90 | Build a Question-Answering System (BERT) | BERT Tutorial |
| 91 | Build a Text Summarization Model | Text Summarization |
| 92 | Build a Speech-to-Text Model | Speech-to-Text |
| 93 | Build a Time Series Forecasting Model (LSTM) | Time Series |
| 94 | Build a Federated Learning Model | Federated Learning |
| 95 | Build an Explainable AI Model (SHAP, LIME) | Explainable AI |
| 96 | Build a Reinforcement Learning Game Agent (Atari Games) | Atari Games |
| 97 | Finalize and Document Your Projects | Documentation Best Practices |
| 98 | Reflect and Plan Next Steps | DL Career Paths |
| 99 | Review and Revise Concepts | DL Review |
| 100 | Complete a Capstone Project | Capstone Ideas |
