Month 23: Deep Learning Techniques with TensorFlow
Week 1: Introduction to Deep Learning and TensorFlow
- Day 1: Understanding Deep Learning
- Day 2: Introduction to Neural Networks
- Day 3: Setting up the TensorFlow Environment
- Day 4: Basics of TensorFlow: Tensors, Variables, and Operations
- Day 5: Building a Basic Neural Network with TensorFlow
Week 2: Convolutional Neural Networks (CNNs) with TensorFlow
- Day 1: Understanding Convolutional Neural Networks
- Day 2: Convolutional and Pooling Layers in TensorFlow
- Day 3: Building a Basic CNN with TensorFlow
- Day 4: Advanced CNN Architectures: LeNet, AlexNet, VGGNet, GoogLeNet, ResNet
- Day 5: CNNs for Image Classification and Object Detection
Week 3: Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) with TensorFlow
- Day 1: Understanding Recurrent Neural Networks
- Day 2: The Problem of Long-Term Dependencies in RNNs
- Day 3: Introduction to Long Short-Term Memory (LSTM)
- Day 4: Building a Basic LSTM with TensorFlow
- Day 5: RNNs and LSTMs for Sequence Prediction and Natural Language Processing
Week 4: Generative Adversarial Networks (GANs) with TensorFlow
- Day 1: Understanding Generative Adversarial Networks
- Day 2: The Components of a GAN: Generator and Discriminator
- Day 3: Building a Basic GAN with TensorFlow
- Day 4: Understanding the Training Process of GANs
- Day 5: Advanced GAN Architectures: DCGAN, StyleGAN, CycleGAN
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