All Courses Deep Learning with TensorFlow
Deep Learning with TensorFlow
This self-paced course introduces you to deep learning fundamentals using TensorFlow. You’ll learn to design, train, and deploy convolutional neural networks (CNNs) and Vision Transformers (ViT) to solve real-world problems. Practical hands-on projects and clear explanations make advanced AI concepts accessible for beginners and intermediate learners alike.
Instructors:
SJ Sushmitha J
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51 Lessons
  1. Understand core deep learning concepts and workflows
  2. Build and train CNN models using TensorFlow
  3. Implement Vision Transformer (ViT) architectures
  4. Apply TensorFlow for real-world image and sequence data
  5. Optimize and evaluate deep learning models effectively
Course Content
1 Introduction
2 Tensors and Variables
3 Linear Regression Neural Networks
4 Convolutional Neural Networks
5 Loading and Saving TensorFlow Model
6 TensorFlow Models
7 Confusion Matrix
8 Callbacks with TensorFlow
9 Mitigating Overfitting and Underfitting
10 Data augmentation
11 Mixup Data augmentation
12 Cutmix Data augmentation
13 Albumentations with TensorFlow
14 Custom Loss and Metrics in TensorFlow 2
15 Eager and Graph Modes in TensorFlow
16 Custom Training Loops in TensorFlow
17 Tensorboard integration with TensorFlow
18 Weights and Biases
19 Hyperparameter Tuning with Weights
20 Dataset Versioning with Weights and Biases
21 Model Versioning
22 Human Emotions Detection
23 Human Emotions Detection
24 Human Emotions Detection
25 Tensorflow Records
26 AlexNet Convolutional Neural Network
27 VGGNet
28 ResNet
29 Coding ResNet
30 Understanding MobileNet
31 Understanding EfficientNet
32 Understanding Transfer Learning
33 Visualizing intermediate Layers
34 Understanding Gradient Based Class Activation Maps
35 Creating Model Ensembles in Tensorflow
36 Tackling the problem of class imbalance
37 Vision Transformers (VIT)
38 Building a Vision Transformers
39 Finetuning Vision Transformers
40 Converting Tensorflow model
41 Understanding Quantization
42 Practical Post Training Quantization
43 Quantization Aware Training
44 Converting TensorFlow
45 Building an API
46 Build Human Emotions Detection API
47 Deploying Human Emotions Detection API
48 Understanding Variational Autoencoders
49 Practical Digit Generation
50 How GANs work
Skills Review on Deep Learning
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