Tutorial-2:Complete deep learning road map in telugu

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Deep Learning by Ian Goodfellow -https://www.amazon.in/Deep-Learning-Ian-Goodfellow/dp/0262035618

Deep Learning using Python - https://www.amazon.in/Learning-Python-Second-Fran%C3%A7ois-Chollet/dp/1617296864

Hands on ML- https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/9355421982

Deep Learning for Coders -https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527
Grokking Deep Learning -https://www.amazon.in/Grokking-Deep-Learning-Andrew-Trask/dp/1617293709

Neural Network and Deep Learning - https://jingyuexing.github.io/Ebook/Machine_Learning/Neural%20Networks%20and%20Deep%20Learning-eng.pdf

NLP with Transformers - https://www.amazon.in/Natural-Language-Processing-Transformers-Revised/dp/1098136799

1. Prerequisites: Mathematics and Programming Basics
Linear Algebra: Linear algebra is essential for understanding the operations behind neural networks. Topics to cover include vectors, matrices, matrix operations, and eigenvalues.
Calculus: Calculus helps you understand how deep learning models are trained. Focus on derivatives, gradients, and the chain rule, as these are crucial for backpropagation.
Probability and Statistics: Probability is important for managing uncertainty in models, while statistics are essential for understanding data distributions. Key concepts include probability distributions, Bayes’ theorem, expectation, and variance.
Python Programming: Python is the dominant language for deep learning. Ensure you’re comfortable with Python, and learn to use data manipulation and visualization libraries like NumPy, Pandas, and Matplotlib.
2. Core Machine Learning Concepts
Supervised Learning: Supervised learning forms the basis of many machine learning models. Study regression, classification, and important evaluation metrics like accuracy, precision, recall, F1-score, and AUC-ROC.
Unsupervised Learning: Techniques like clustering (k-means, hierarchical clustering) and dimensionality reduction (PCA, t-SNE) help in understanding data structures and are often used in pre-processing.
Feature Engineering: Learn to preprocess and manipulate data. Key steps include data cleaning, normalization, encoding categorical variables, and feature scaling.
Model Evaluation and Overfitting: Understand overfitting and underfitting, train/test splits, and cross-validation.
3. Introduction to Neural Networks
Neural Network Basics: Neural networks are made up of layers of neurons. Learn about perceptrons, how neurons connect, and the purpose of activation functions. Activation functions introduce non-linearity, allowing models to learn complex patterns.
Backpropagation and Loss Functions: Backpropagation is the core of neural network training.

4. Key Deep Learning Architectures
Feedforward Neural Networks (FNN): FNNs are simple neural networks where data flows in one direction (input to output). They’re the foundation for more complex architectures.
Convolutional Neural Networks (CNN): CNNs are tailored for image data, making them powerful for tasks like image recognition. Learn about convolution operations, pooling layers, and architectures like AlexNet, VGG, and ResNet, which improve model depth and accuracy.
Recurrent Neural Networks (RNN): RNNs are designed for sequence data, such as time series or text.
Transformers and Attention Mechanisms: Transformers use self-attention mechanisms and have revolutionized natural language processing.
5. Deep Learning Frameworks and Tools
TensorFlow and Keras: These frameworks by Google offer high-level and low-level APIs for deep learning. TensorFlow is widely used in both industry and research, while Keras provides a simpler interface that’s ideal for prototyping.
PyTorch: PyTorch by Facebook is known for its flexibility and is popular in research. It offers a dynamic computation graph, which provides greater control over the model-building process and is intuitive to use.
Data Handling with Libraries: Master libraries like OpenCV, PIL, and torchvision for data loading, transformation, and augmentation.
Data Preprocessing and Augmentation: Preprocessing data ensures the model performs well.
Transfer Learning and Fine-Tuning: Transfer learning allows you to use pre-trained models for related tasks, significantly reducing training time.
Hyperparameter Tuning: Hyperparameter tuning improves model performance.

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