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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
~ 13h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

12 chapters - 63 sections
Chapter 01

Chapter 1: Introduction to Neural Networks and Deep Learning

0/6
11.1 Perceptron and Multi-Layer Perceptron (MLP)12m21.2 Backpropagation, Gradient Descent, and Optimizers12m31.3 Overfitting, Underfitting, and Regularization Techniques12m41.4 Loss Functions in Deep Learning12m6Chapter 1 Summary12m5Practical Exercises Chapter 112m
Chapter 02

Chapter 2: Deep Learning with TensorFlow 2.x

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12.1 Introduction to TensorFlow 2.x12m22.2 Building, Training, and Fine-Tuning Neural Networks in TensorFlow12m32.3 Using TensorFlow Hub and Model Zoo for Pretrained Models12m42.4 Saving, Loading, and Deploying TensorFlow Models12m6Chapter 2 Summary12m5Practical Exercises Chapter 212m
Chapter 03

Chapter 3: Deep Learning with Keras

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13.1 Introduction to Keras API in TensorFlow 2.x12m23.2 Building Sequential and Functional Models with Keras12m33.3 Model Checkpointing, Early Stopping, and Callbacks in Keras12m43.4 Deploying Keras Models to Production12m6Chapter 3 Summary12m5Practical Exercises Chapter 312m
Chapter 04

Quiz Part 1: Neural Networks and Deep Learning Basics

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11. Introduction to Neural Networks and Deep Learning (Chapter 1)12m22. Deep Learning with TensorFlow 2.x (Chapter 2)12m33. Deep Learning with Keras (Chapter 3)12m4Answers to the Quiz12m
Chapter 05

Chapter 4: Deep Learning with PyTorch

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14.1 Introduction to PyTorch and its Dynamic Computation Graph12m24.2 Building and Training Neural Networks with PyTorch12m34.3 Transfer Learning and Fine-Tuning Pretrained PyTorch Models12m44.4 Saving and Loading Models in PyTorch12m54.5 Deploying PyTorch Models with TorchServe12m6Chapter 4 Summary12m7Practical Exercises Chapter 412m
Chapter 06

Chapter 5: Convolutional Neural Networks (CNNs)

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15.1 Introduction to CNNs and Image Processing12m25.2 Implementing CNNs with TensorFlow, Keras, and PyTorch12m35.3 Advanced CNN Techniques (ResNet, Inception, DenseNet)12m45.4 Practical Applications of CNNs (Image Classification, Object Detection)12m6Chapter 5 Summary12m5Practical Exercises Chapter 512m
Chapter 07

Chapter 6: Recurrent Neural Networks (RNNs) and LSTMs

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16.1 Introduction to RNNs, LSTMs, and GRUs12m26.2 Implementing RNNs and LSTMs in TensorFlow, Keras, and PyTorch12m36.3 Applications of RNNs in Natural Language Processing12m46.4 Transformer Networks for Sequence Modeling12m6Chapter 6 Summary12m5Practical Exercises Chapter 612m
Chapter 08

Quiz Part 2: Advanced Deep Learning Frameworks

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4Answers12m1Chapter 4: Deep Learning with PyTorch12m2Chapter 5: Convolutional Neural Networks (CNNs)12m3Chapter 6: Recurrent Neural Networks (RNNs) and LSTMs12m
Chapter 09

Chapter 7: Advanced Deep Learning Concepts

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17.1 Autoencoders and Variational Autoencoders (VAEs)12m27.2 Generative Adversarial Networks (GANs) and Their Applications12m37.3 Transfer Learning and Fine-Tuning Pretrained Networks12m47.4 Self-Supervised Learning and Foundation Models12m5Practical Exercises Chapter 712m6Summary Chapter 712m
Chapter 10

Chapter 8: Machine Learning in the Cloud and Edge Computing

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18.1 Running Machine Learning Models in the Cloud (AWS, Google Cloud, Azure)12m28.2 Introduction to TensorFlow Lite and ONNX for Edge Devices12m38.3 Deploying Models to Mobile and Edge Devices12m4Practical Exercises Chapter 812m5Summary Chapter 812m
Chapter 11

Chapter 9: Practical Projects

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19.1 Project 1: Predicting House Prices with Regression12m29.2 Project 2: Sentiment Analysis Using Transformer-based Models12m39.3 Project 3: Image Classification with CNNs12m49.4 Project 4: Time Series Forecasting with LSTMs (Improved)12m59.5 Project 5: GAN-based Image Generation12m
Chapter 12

Quiz Part 3: Cutting-Edge AI and Practical Applications

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2Answers12m1Questions12m