Practice Problems Machine Learning Deep Learning is a branch of Artificial Intelligence (AI) that enables machines to learn patterns from large amounts of data using multi-layered neural networks. It is widely used in image recognition, speech processing and natural language understanding. Neural networks are the basis of deep learning, using interconnected layers of neurons to learn patterns from data. Neural Networks Types of Neural Networks Artificial Neural Networks (ANNs) Deep Learning Framework Components of Neural Networks Neural networks have key components that control data flow and learning, helping the model adjust parameters and improve predictions. Weights and Biases Activation Functions Forward Propagation Backpropagation Optimization Algorithm Optimization algorithms update weights and biases to minimize loss, helping the model converge faster and perform better. Gradient Descent Stochastic Gradient Descent (SGD) Mini-batch Gradient Descent Momentum Optimizer Adagrad Optimizer RMSProp Optimizer Adam (Adaptive Moment Estimation) Regularization & Hyperparameter Tuning Regularization techniques reduce overfitting, while hyperparameter tuning improves model performance and training efficiency. Batch Normalization L1 & L2 Regularization Hyperparameter Tuning Convolutional Neural Networks Convolutional Neural Networks (CNNs) are deep learning models designed for image data that automatically detect features like edges, textures and shapes. Convolutional Layers Fully Connected Layers Backpropagation in CNNs Building CNN using PyTorch Building CNN using TensorFlow Image Classification CNN Architectures GoogLeNet/Inception ResNet (Residual Network) Recurrent Neural Networks RNNs are a class of neural networks that are used for modeling sequence data such as time series or natural language. How RNN Differs from Feedforward Neural Networks Backpropagation Through Time (BPTT) Vanishing Gradient Problem Training of RNN Model Bidirectional RNNs Long Short-Term Memory (LSTM) Bidirectional Long Short-Term Memory (Bi-LSTM) Gated Recurrent Units (GRU) Advanced Deep Learning Techniques Advanced deep learning techniques use specialized models and architectures to solve complex AI tasks beyond traditional neural networks. Types of Autoencoders Generative Adversarial Network (GAN)

Diffusion Models Encoder Decoder Models Attention Mechanism Transfer Learning Deep Reinforcement Learning Deep Reinforcement Learning combines deep learning with Reinforcement Learning to enable agents to learn optimal actions through interaction with an environment. Reinforcement Learning Deep Reinforcement Learning Markov Decision Processes Deep Q-Networks (DQN) Policy Gradient Actor-Critic Methods Proximal Policy Optimization (PPO) Practice deep learning concepts through projects and interview questions to strengthen your understanding. Deep Learning Projects Interview Questions Related Articles Machine Learning Tutorial AI, ML and Data Science Tutorial Introduction 5 min read AI vs ML vs DL 2 min read Applications 3 min read Challenges 7 min read Importance 5 min read Introduction 9 min read Types 5 min read Layers in ANN 4 min read Activation functions 6 min read Feedforward Neural Network 6 min read Backpropagation 7 min read RNN 10 min read LSTM 4 min read Transformers 5 min read Autoencoders 6 min read GAN 10 min read Gradient Descent 13 min read Momentum-based Gradient Optimizer 3 min read Adagrad 6 min read RMSProp 4 min read Adam Optimizer 5 min read TensorFlow 2 min read PyTorch 5 min read Caffe 8 min read Apache MXNet 4 min read Theano 3 min read Lung Cancer Detection 4 min read Cat & Dog Classification 5 min read Sentiment Analysis 3 min read Text Generation using LSTM 4 min read Machine Translation 5 min read Interview Questions 15+ min read Data Science 360 Course 2 min read AI Engg Course 2 min read