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deep learning course in jalandhar

Join the best Deep Learning course in Jalandhar, Punjab, at TechCadd. Master neural networks and AI techniques with practical, hands-on training.<br>VISIT NOW:https://techcadd.com/best-deep-learning-course-in-jalandhar.php

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deep learning course in jalandhar

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  1. DEEP LEARNING COURSE VISIT NOW:https://techcadd.com/

  2. WHAT IS DEEP LEARNING? • Subset of Machine Learning using neural networks with multiple layers • Learns complex patterns from large datasets • Ideal for tasks like image and speech recognition

  3. DEEP LEARNING VS TRADITIONAL ML • No need for manual feature extraction • Scales well with big data • Handles unstructured data (images, text, audio

  4. Inspired by the human brain Structure: Neurons, Layers, Weights, Biases Forward and backward propagation NEURAL NETWORK BASICS:

  5. TYPES OF NEURAL NETWORKS: • Feedforward Neural Networks (FNN) • Convolutional Neural Networks (CNN) • Recurrent Neural Networks (RNN) • Generative Adversarial Networks (GANs)

  6. ACTIVATION FUNCTION: • Introduce non-linearity • Common functions: • ReLU (Rectified Linear Unit) • Sigmoid • Tanh • Softmax

  7. Loss function: Measures error (e.g., MSE, Cross-Entropy) • Optimizers: Gradient Descent, Adam • Backpropagation: Updating weights using gradients TRAINING A NEURAL NETWORK:

  8. Convolutional Neural Networks (CNNs) • Recurrent Neural Networks (RNNs) • Generative Adversarial Networks (GANs) CNN, RNN, GAN • Designed for image and spatial data • Layers: Convolution, Pooling, Flatten, Fully Connected • Applications: Image classification, object detection • Designed for sequential data • Memory of previous inputs • Variants: LSTM, GRU • Applications: Time-series prediction, language modeling • Two networks: Generator vs. Discriminator • Used for generating realistic data (images, audio, text) • Applications: Deepfakes, image enhancement

  9. DEEP LEARNING FRAMEWOKS • TensorFlow • Keras • PyTorch • JAX • Tool comparison and use cases

  10. CHALLENGES IN DEEP LEARNING: • Requires large datasets and computing power • Overfitting • Interpretability • Ethical concerns (bias, misuse)

  11. APPLICATIONS OF DEEP LEARNING: • Computer Vision: Facial recognition, medical imaging • Natural Language Processing: Chatbots, translation • Speech: Voice assistants, speech-to-text • Autonomous systems: Drones, self-driving cars

  12. CONCLUSION: • Recap: What is DL, core networks, key use cases • Learn more through projects and practice • Suggested resources: Courses, books, datasets • Q&A

  13. THANKYOU..... VISIT NOW:https://techcadd.com/

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