1 - Introduction to Hands on Deeplearning
2 - What is Machine Learning
3 - Popular ML Methods
4 - What is Deep Learning
5 - Applications of Deeplearning
6 - Recommendations
7 - Basic Concept of Deeplearning
8 - Perception
9 - Neural Network
10 - Universal Approximations Theorem
11 - Deep Neural Network
12 - Deep Neural Network Continue
13 - Getting Started
14 - Where to write Code
15 - Jupiter Notebook
16 - Google Colab
17 - Pytorch
18 - Tensors
19 - Tensors Continue
20 - Gradients
21 - MNIST Example
22 - Check Sample
23 - Hidden Layer
24 - Interface on a Digit
25 - TransferLearningOverview
26 - What is Transfer Learning
27 - CS231n Convolutional Neural Networks
28 - Download Dataset
29 - Transform the Data
30 - Visualize the Data
31 - Define the Model
32 - Add a Few Final Layers
33 - Train the Model
34 - Test the Model
35 - What About CIFAR
36 - Image Classifier on Cifar 10 Dataset
37 - Download and Load Our Dataset
38 - Train and Test Dataset
39 - Define Our Neural Network
40 - Working on Image
41 - Input and Output
42 - Define Our Loss Function
43 - Train Data in Enumerate
44 - Train Data in Enumerate Continue
45 - Test the Neural Network on the Test Image
46 - Intro to Text Classifier
47 - Text Classification Using CNN
48 - Prepare the Data
49 - Build the Model
50 - Build the Model Coninue
51 - More on Build the Model
52 - Define a Loss Function
53 - Define a Loss Function Continue
54 - More on Define a Loss Function
55 - Evaluate or Test the Model
56 - Intro to Text Generation
57 - Text GenerationTransformers
58 - Text GenerationTransformers Continue
59 - TransformersArchitectures
60 - TransformersArchitectures Cintinue
61 - WordGeneration
62 - WordGeneration Continue
63 - TextGeneration
64 - Intro to Text Translation
65 - LoadingData
66 - PreparingData
67 - EncoderAttention Part 1
68 - EncoderAttention Part 2
69 - EncoderAttention Part 3
70 - Decoder
71 - TrainEvalFunctions
72 - TrainEvalFunctions Continue
73 - TrainingFixes
74 - TrainingEvaluation
75 - PredictionTabularData Part 1
76 - PredictionTabularData Part 2
77 - PredictionTabularData Part 3
78 - PredictionTabularData Part 4
79 - Collaborative Filtering
80 - Collaborative Filtering Continue
81 - Other Recommendation Approaches
