8 Sections
44 Lessons
8 Hours
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Module 1: Introduction to Deep Learning (45 min)
6
1.1
What is Deep Learning
1.2
DL vs ML vs AI
1.3
Real-World Applications of DL (Healthcare, Finance, Image Recognition, NLP, etc.)
1.4
Basics of Neural Networks
1.5
Understanding Weights, Biases, Activation Functions (ReLU, Sigmoid, Softmax)
1.6
Overview of Popular DL Frameworks: TensorFlow & PyTorch
Module 2: Setting Up the DL Environment (30 min)
4
2.1
Installing TensorFlow & Keras
2.2
Introduction to Google Colab for Free GPU Access
2.3
Basics of NumPy, Pandas, and Matplotlib for DL
2.4
Hands-on: Creating and Running a Simple Neural Network
Module 3: Fundamentals of Neural Networks (1 Hour)
6
3.1
Understanding Perceptrons & Multi-Layer Perceptrons (MLP)
3.2
Forward Propagation & Backpropagation Explained
3.3
Gradient Descent & Optimization Algorithms (SGD, Adam, RMSprop)
3.4
Loss Functions: MSE, Cross-Entropy Loss
3.5
Batch Size, Epochs, and Learning Rate Explained
3.6
Hands-on: Building a Neural Network from Scratch in TensorFlow
Module 4: Convolutional Neural Networks (CNNs) (1 Hour 15 min)
6
4.1
Why CNNs? Understanding Image Processing
4.2
Convolution, Pooling, Padding, and Stride
4.3
Architecture of CNNs (LeNet, AlexNet, VGG, ResNet)
4.4
Transfer Learning: Using Pretrained Models
4.5
Data Augmentation for Image Processing
4.6
Hands-on: Image Classification with CNNs using TensorFlow
Module 5: Recurrent Neural Networks (RNNs) & LSTMs (1 Hour 15 min)
6
5.1
Understanding Sequential Data & Time Series
5.2
Basics of RNNs & Challenges (Vanishing Gradient Problem
5.3
Long Short-Term Memory (LSTM) Networks
5.4
Gated Recurrent Units (GRUs)
5.5
Applications: Sentiment Analysis, Text Generation, Stock Price Prediction
5.6
Hands-on: Building an LSTM Model for Text Prediction
Module 6: Generative AI & Autoencoders (1 Hour 15 min)
5
6.1
Introduction to Autoencoders
6.2
Variational Autoencoders (VAEs) vs. Generative Adversarial Networks (GANs)
6.3
Understanding GANs: Generator & Discriminator
6.4
Use Cases: DeepFake, Image Generation
6.5
Hands-on: Generating New Images using GANs
Module 7: Model Evaluation, Deployment & Optimization (1 Hour)
5
7.1
Performance Metrics for DL Models (Accuracy, Precision, Recall, ROC)
7.2
Hyperparameter Tuning in Deep Learning
7.3
Overfitting & Regularization (Dropout, L1/L2 Regularization)
7.4
Deployment of DL Models using Flask & Streamlit
7.5
Hands-on: Deploying a DL Model as a Web App
Module 8: End-to-End DL Project (1 Hour)
6
8.1
Selecting a Dataset (E.g., Image Classification, Sentiment Analysis, Anomaly Detection)
8.2
Data Preprocessing & Augmentation
8.3
Model Training & Evaluation
8.4
Fine-Tuning & Transfer Learning
8.5
Model Deployment Overview (TF Serving, FastAPI)
8.6
Final Thoughts & Next Steps in Deep Learning
Deep Learning Pro: Build & Deploy AI Models with TensorFlow
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