## **Introduction to Deep Learning** ![[1_Da7wVx5j1KcSJ-I4DVFZyQ.png]] The simple machine learning algorithms work very well on a wide variety of important problems. However, they have not succeeded in solving the central problems in AI, such as recognizing speech or recognizing objects. Deep learning was designed to overcome these and other obstacles. ## 1.What is Deep Learning ? Deep learning **mimics the human brain** by using multiple **layers of artificial neurons** to process data in a hierarchical manner. Key Aspects: - Works with large-scale data and high-dimensional features. - Utilizes **multiple layers (deep architectures)** to learn representations automatically. - Excels in computer vision, NLP, healthcare, and robotics. ## 2. Why Deep Learning? Traditional ML methods perform well on structured, small datasets but they struggle with: - High-dimensional, unstructured data (e.g., images, text, audio). - Feature engineering → Requires domain expertise. - Scalability issues with large datasets. Deep Learning solves these problems by: - Automatically extracting features from raw data. - Handling large-scale datasets efficiently. - Leveraging GPU acceleration for faster computation. ## 3. How Deep Learning Works Deep Learning uses **layers of neurons** to transform input data step by step. 1. **Input Layer** → Raw data (images, text, numbers). 2. **Hidden Layers** → Feature extraction using weights and activation functions. 3. **Output Layer** → Final predictions. For more details, see: **[[Neural Networks]]** --- ## 4. Topics to Explore #### Fundamentals of Deep Learning - [[Neural Networks]] – Understanding perceptrons, backpropagation, and gradient descent. - [[Activation Function]] – ReLU, Sigmoid, Tanh, and Softmax. - [[Optimization Techniques]] – Adam, RMSprop, SGD, and learning rate schedules. #### Advanced Architectures - [[Convolutional Neural Networks (CNNs)]] – Used in image recognition. - [[Recurrent Neural Networks (RNNs)]] – For sequential data processing (e.g., NLP, time series). - [[Transformers and Attention Mechanism]] – State-of-the-art NLP models (BERT, GPT). - [[Generative Models]] – GANs, VAEs, Diffusion models for AI-generated content. #### Frameworks - [[Deep Learning Frameworks]] – Popular frameworks like TensorFlow, PyTorch, and Keras. --- ## 5. Resources for Learning Deep Learning #### Books 1. [_Deep Learning_ - Ian Goodfellow, Yoshua Bengio, Aaron Courville](https://www.deeplearningbook.org/) – A comprehensive textbook covering the fundamentals of deep learning. 2. [_Neural Networks and Deep Learning_ - Michael Nielsen](http://neuralnetworksanddeeplearning.com/) – A free online book explaining neural networks in an intuitive way. 3. [_Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow_ - Aurélien Géron](http://14.139.161.31/OddSem-0822-1122/Hands-On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf) – Practical deep learning using TensorFlow & Keras. #### Online Courses 1. [Coursera - Deep Learning Specialization (Andrew Ng)](https://www.coursera.org/specializations/deep-learning) – Covers fundamental deep learning concepts, CNNs, RNNs, and sequence models. 2. [Fast.ai - Practical Deep Learning for Coders](https://course.fast.ai/) – A hands-on deep learning course with PyTorch. 3. [MIT OpenCourseWare - Introduction to Deep Learning](http://introtodeeplearning.com/) – An in-depth course on deep learning from MIT. #### Video Tutorials 1. [Deep Learning Full Course](https://www.youtube.com/watch?v=aircAruvnKk) – A beginner-friendly deep learning crash course. 2. [Deep Learning Basics - Playlist](https://www.youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1) – Covers deep learning fundamentals with hands-on examples. 3. [Stanford CS231n - CNNs for Visual Recognition](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) – A deep dive into convolutional neural networks (CNNs). 4. [Deep Learning with Tensorflow2.0 , Keras ](https://www.youtube.com/watch?v=Mubj_fqiAv8&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO) - Covers deep learning fundamentals with hands-on examples using Tensorflow. --- ## 6. Applications - **[[Computer Vision]]** : Object detection, image classification. - **[[Natural Language Processing]]** :Chatbots, text summarization. - Healthcare : Disease detection, drug discovery. --- ## Next Steps Explore Deep Learning Topics: - Understand ANN structure & training. → [[Neural Networks]] - Used in image recognition. → [[Convolutional Neural Networks (CNNs)]] - Study attention-based NLP models. → [[Transformers and Attention Mechanism]] - Learn about GANs, VAEs, and Stable Diffusion. → [[Generative Models]] Start Building Models: - Implement Deep learning with [[Deep Learning Frameworks]]. - Train your own CNN for image Classification. - Experiment with Transformers for Text Generation.