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Deep Learning and AI Superhero

12 chapters and 63 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
12
Reading time
~ 19h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

12 chapters - 63 sections

Chapter 1: Introduction to Neural Networks and Deep Learning

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1.1 Perceptron and Multi-Layer Perceptron (MLP)24m1.2 Backpropagation, Gradient Descent, and Optimizers37m1.3 Overfitting, Underfitting, and Regularization Techniques49m1.4 Loss Functions in Deep Learning31mPractical Exercises Chapter 16mChapter 1 Summary3m

Chapter 2: Deep Learning with TensorFlow 2.x

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2.1 Introduction to TensorFlow 2.x45m2.2 Building, Training, and Fine-Tuning Neural Networks in TensorFlow44m2.3 Using TensorFlow Hub and Model Zoo for Pretrained Models32m2.4 Saving, Loading, and Deploying TensorFlow Models32mPractical Exercises Chapter 25mChapter 2 Summary3m

Chapter 3: Deep Learning with Keras

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3.1 Introduction to Keras API in TensorFlow 2.x27m3.2 Building Sequential and Functional Models with Keras31m3.3 Model Checkpointing, Early Stopping, and Callbacks in Keras26m3.4 Deploying Keras Models to Production33mPractical Exercises Chapter 36mChapter 3 Summary3m

Quiz Part 1: Neural Networks and Deep Learning Basics

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1. Introduction to Neural Networks and Deep Learning (Chapter 1)2m2. Deep Learning with TensorFlow 2.x (Chapter 2)2m3. Deep Learning with Keras (Chapter 3)3mAnswers to the Quiz2m

Chapter 4: Deep Learning with PyTorch

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4.1 Introduction to PyTorch and its Dynamic Computation Graph26m4.2 Building and Training Neural Networks with PyTorch29m4.3 Transfer Learning and Fine-Tuning Pretrained PyTorch Models33m4.4 Saving and Loading Models in PyTorch30m4.5 Deploying PyTorch Models with TorchServe27mPractical Exercises Chapter 47mChapter 4 Summary3m

Chapter 5: Convolutional Neural Networks (CNNs)

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5.1 Introduction to CNNs and Image Processing50m5.2 Implementing CNNs with TensorFlow, Keras, and PyTorch19m5.3 Advanced CNN Techniques (ResNet, Inception, DenseNet)33m5.4 Practical Applications of CNNs (Image Classification, Object Detection)23mPractical Exercises Chapter 58mChapter 5 Summary3m

Chapter 6: Recurrent Neural Networks (RNNs) and LSTMs

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6.1 Introduction to RNNs, LSTMs, and GRUs30m6.2 Implementing RNNs and LSTMs in TensorFlow, Keras, and PyTorch27m6.3 Applications of RNNs in Natural Language Processing22m6.4 Transformer Networks for Sequence Modeling31mPractical Exercises Chapter 67mChapter 6 Summary4m

Quiz Part 2: Advanced Deep Learning Frameworks

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Chapter 4: Deep Learning with PyTorch1mChapter 5: Convolutional Neural Networks (CNNs)1mChapter 6: Recurrent Neural Networks (RNNs) and LSTMs1mAnswers5m

Chapter 7: Advanced Deep Learning Concepts

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7.1 Autoencoders and Variational Autoencoders (VAEs)22m7.2 Generative Adversarial Networks (GANs) and Their Applications20m7.3 Transfer Learning and Fine-Tuning Pretrained Networks23m7.4 Self-Supervised Learning and Foundation Models15mPractical Exercises Chapter 78mSummary Chapter 74m

Chapter 8: Machine Learning in the Cloud and Edge Computing

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8.1 Running Machine Learning Models in the Cloud (AWS, Google Cloud, Azure)22m8.2 Introduction to TensorFlow Lite and ONNX for Edge Devices21m8.3 Deploying Models to Mobile and Edge Devices20mPractical Exercises Chapter 86mSummary Chapter 84m

Chapter 9: Practical Projects

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9.1 Project 1: Predicting House Prices with Regression20m9.2 Project 2: Sentiment Analysis Using Transformer-based Models21m9.3 Project 3: Image Classification with CNNs20m9.4 Project 4: Time Series Forecasting with LSTMs (Improved)18m9.5 Project 5: GAN-based Image Generation20m

Quiz Part 3: Cutting-Edge AI and Practical Applications

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Questions3mAnswers1m