20.49 GB | 00:10:50 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Introduction (38.68 MB)
2 What is An Artificial Neuron (45.23 MB)
3 What is Deep Learning (48.02 MB)
4 Neural Network Basics (82.17 MB)
5 Convolutional Neural Networks (CNNs) (107.19 MB)
6 Natural Language Processing (NLP) (83.67 MB)
7 Challenge (26.75 MB)
1 Introduction (150.04 MB)
2 Binary to Multi-Class Classification EDA (85.44 MB)
3 Build, Compile, and Fit Model (54.25 MB)
4 Evaluate for Overfitting (plot curves) (106.46 MB)
5 Adjust HyperparametersData Augmentation (70.52 MB)
6 Repeat Until Happy with Results (159.24 MB)
1 Introduction (218.29 MB)
2 Kaggle Milestone Challenge Overview (176.68 MB)
3 Load Dataset with ImageDataGenerator (130.7 MB)
4 Challenge (33.57 MB)
5 Solution Video (107.86 MB)
1 Introduction (102.74 MB)
2 Explore Dataset and Clone Repo from CBTN GitHub (57.85 MB)
3 Prepare Train Dataset for Reduction (147.91 MB)
4 Reduce Train Dataset by 90% and Only Keep 10% (81.17 MB)
5 CHALLENGE (34.94 MB)
6 Solution (93.66 MB)
1 Introduction (144.92 MB)
10 Improved Learning Rate (9.28 MB)
2 Review Reduced Food 10 Dataset (10%) (41.27 MB)
3 Custom Callbacks (67.96 MB)
4 TensorBoard Callbacks (45.75 MB)
5 Checkpoint Callbacks (29.68 MB)
6 Early Stopping Callbacks (48.35 MB)
7 Callback Lists (63.39 MB)
8 Challenge Introduction (24.8 MB)
9 Challenge Solution (66.8 MB)
1 Introduction (107.53 MB)
2 Review Transfer Learning, Feature Extraction, and Fine-Tuning (74.74 MB)
3 Review Food 10 Dataset (59.85 MB)
4 Exploring Pre-Trained Models (184.27 MB)
5 Apply Transfer Learning, Feature Extraction, and Fine-tuning (64.17 MB)
6 Challenge (49.09 MB)
7 Solution video (65.45 MB)
1 Introduction (127.07 MB)
2 Clone and Create Reduced Food 10 Dataset (133.83 MB)
3 Apply Data Augmentation (91.92 MB)
4 Create a custom function to build Keras models simply using URLs 🎉 (65.12 MB)
5 Build, compile, and train resnet model (68.56 MB)
6 Challenge (19.54 MB)
7 Solution video (49.51 MB)
1 Introduction (117 MB)
2 Explore Reduced Food 10 Dataset for Transfer Learning (170.52 MB)
3 Create re-useable custom functions for rapid testing (62.35 MB)
4 Import custom functions from CBT Nuggets GitHub repo (65.37 MB)
5 Double Challenge 🚀🚀 (81.84 MB)
6 Build a tensorflowresnet model from scratch (94.77 MB)
7 Build a tensorflowefficientnet model from scratch (65.42 MB)
1 Introduction (141.26 MB)
2 Explore Fine-Tuning (63.13 MB)
3 Explore Food 10 Dataset (88.84 MB)
4 Import custom functions with !wget or !clone (42.85 MB)
5 Create ResNet50 Model (113.35 MB)
6 Train the Fine-Tuning layers of the model (111.45 MB)
7 Challenge (31.41 MB)
8 Solution Video (154.08 MB)
1 Introduction (76.93 MB)
2 Explore Fine-Tuning (37.76 MB)
3 Explore Food 10 Dataset in three sizes (70.49 MB)
4 Explore a new TensorFlow methods (129.34 MB)
5 Explore Keras Applications Vs TensorFlow Hub (127.32 MB)
6 Fine-Tune the Top 10 Unfrozen Layers (94.7 MB)
7 Challenge (26.97 MB)
8 Challenge Solution Video (87.49 MB)
9 Update Completed ResNet50 Training & EfficientNetB0 Code (159.02 MB)
1 Introduction (73.46 MB)
2 From Pixels and CNN to Characters with NLP (54.51 MB)
3 What is ASCII and Why Isn't Great for Encoding in NLP (43.45 MB)
4 Using Basic Sequences to Understand Basic Encoding Principles (49.56 MB)
5 What are Tokens and Tokenizers (62.81 MB)
6 CHALLENGE 🎉 (25.49 MB)
7 Challenge Solution Video (54.66 MB)
1 Introduction (103.26 MB)
2 Google Colab (121.55 MB)
3 Anaconda and Conda (178.39 MB)
4 Jupyter Notebook (84.31 MB)
5 PyCharm (65.41 MB)
6 Visual Studio Code (49.41 MB)
7 Challenge (31.86 MB)
8 Solution Video (39.96 MB)
1 Introduction (110.22 MB)
2 Explore Similarities in Reading for Humans and Machines (48.21 MB)
3 Apply Token Sequences Aiming for Coherent Outputs (53.45 MB)
4 Handling Out-of Vocabulary words with OOV Tokens (76.59 MB)
5 Adding Uniformity to Sentences with Padding (58.14 MB)
6 CHALLENGE (37.45 MB)
7 Challenge Solution (76.32 MB)
1 Introduction (116.64 MB)
2 Review Limitations of Song Lyrics Generator Bot (113.16 MB)
3 Explore News Category Dataset and Preprocessing (124.72 MB)
4 Apply Sequencing, OOV, and Padding (83.85 MB)
5 Test User Article Title Input on Our Vocabulary with OOV in Mind (83.1 MB)
6 CHALLENGE (17.64 MB)
7 Challenge Solution Video (103.04 MB)
1 Introduction (90.62 MB)
2 Review Previous NLP Neural Network Classifier (50.79 MB)
3 Explore TensorFlow Datasets and New Dataset (64.19 MB)
4 Load IMDB Dataset and Convert to DataFrames (49.8 MB)
5 Convert Data to Numpy Arrays and review sentences and labels (60.44 MB)
6 Tokenizer, Sequences, OOV, and Padding and Embeddings (137.65 MB)
7 CHALLENGE (37.44 MB)
8 Challenge Solution Video (46.98 MB)
1 Introduction (64.39 MB)
2 Use Numpy instead of Pandas to Load Dataset (65.72 MB)
3 Deep Dive Into Hyperparameters Tuning (89.57 MB)
4 Deep Dive Into Text Embeddings (101.95 MB)
5 Plot Loss and Accuracy Curves (159.03 MB)
6 Analyze Sentiment with Embedding Projector (63.06 MB)
7 CHALLENGE (45.3 MB)
8 Solution Video (88.22 MB)
1 Introduction (98.16 MB)
2 Review TensorFlow Datasets & Explore Subwords and BSE (96.4 MB)
3 CHALLENGE (50.8 MB)
4 Solution Video A (164.69 MB)
5, Solution Video B (109.42 MB)
6 Solution Video C (67.84 MB)
1 Introduction (80.98 MB)
2 From Token Semantics to Sequential Coherence (91.18 MB)
3 What is RNN and LSTM (125.07 MB)
4 The Heart of Sequence Models Sequence Problems (56.53 MB)
5 TensorFlow Modeling Action Steps (104.1 MB)
6 Delve Deeper Into RNN and LSTM (65.7 MB)
7 CHALLENGE (19.3 MB)
8 Solution Video (66.37 MB)
1 Introduction (104.63 MB)
2 Static Token Vs Dynamic Embeddings (40.93 MB)
3 NLP with Kaggle's Disaster Tweets Contest (78.25 MB)
4 Exploratory Data Analysis with Pandas (104.91 MB)
5 Data Visualization with Matplotlib and Seaborn (74.28 MB)
6 CHALLENGE (59.01 MB)
7 Solution Video (60.08 MB)
1 Introduction (53.97 MB)
2 Review Baseline TensorFlow Binary Disaster Classifier (134.66 MB)
3 Load and Preprocess Dataset (38.03 MB)
4 Clean Data Before Improving Model Architecture (52.39 MB)
5 Clean Data Part 2 (92.06 MB)
6 CHALLENGE (20.17 MB)
7 Solution Video (86.45 MB)
1 Introduction (134.23 MB)
2 Review Baseline Model Accuracy & Loss (291.14 MB)
3 Add Random Dataset Shuffle & Hyperparameters (61.81 MB)
4 Leverage Custom Functions (24.09 MB)
5 Prepare Competition Output File submission csv (25.34 MB)
6 CHALLENGE (110.24 MB)
7 Solution Video (68.19 MB)
1 Introduction (81.22 MB)
2 Explore RNNs Models and the Vanishing Gradients Problem (105.08 MB)
3 Explore Long Short-Term Memory (LSTM) Models (41.75 MB)
4 Build a Single Layer Bidirectional LSTM Model (62.89 MB)
5 Build a Multiple Layer Bidirectional LSTM Model (45.74 MB)
6 Add Convolutions to LSTM Models to Capture Sequences of Words (21.79 MB)
7 CHALLENGE (14.89 MB)
8 Solution Video & Code (96.93 MB)
1 Introduction (94.4 MB)
2 What is Computer Vision(CV) (121.29 MB)
3 Explore CV with the fashion MNIST dataset (88.43 MB)
4 How does SoftMax work (50.88 MB)
5 Normalizing and Standardization (44.75 MB)
6 Challenge (48 MB)
7 Solution Video (86.53 MB)
1 Introduction (70.48 MB)
2 Contrast LSTM, GRU, and Convolutional LSTM (86.95 MB)
3 Build LSTM model (101.64 MB)
4 Build GRU model (13.57 MB)
5 Build Convolutional LSTM model (51.15 MB)
6 CHALLENGE 🎉 (25.17 MB)
7 Solution Video (70.61 MB)
1 Introduction (59.91 MB)
2 Review Transfer Learning (125.25 MB)
3 Searching for Models on TensorFlow Hub (100.01 MB)
4 CHALLENGE (72.66 MB)
5 Challenge Solution Video Part 1 (57.64 MB)
6 Challenge Solution Video Part 2 (76.27 MB)
1 Introduction (126.71 MB)
2 Explore Generative Text Prediction (92.11 MB)
3 Initialize and Fit Tokenizer (21.83 MB)
4 Convert to Numerical Representation of the Corpus (36.8 MB)
5 Generate and Return N-Gram Sequences (43.15 MB)
6 Convert Padding Sequences to X and y (82.4 MB)
7 Explore Tokenized Word Index (44.25 MB)
8 CHALLENGE 🎉 (4.48 MB)
9 Solution Video (15.28 MB)
1 Introduction (98.66 MB)
2 Build, Compile and Train Model (65.17 MB)
3 Plot the Accuracy and Loss Curves (61.6 MB)
4 Add Bidirectional(LSTM) and Plot Curves (64.36 MB)
5 Create a Text Prediction Sequence Model (55.58 MB)
6 CHALLENGE 🎉 (50.65 MB)
7 Solution Video (56.43 MB)
1 Introduction (73.07 MB)
2 What is Univariate Time Series (79.95 MB)
3 What is Multivariate Time Series (44.34 MB)
4 Trends (20.68 MB)
5 Seasonality (41.45 MB)
6 Autocorrelation (26.08 MB)
7 Noise (43.09 MB)
1 Introduction (37.93 MB)
2 Explore Time Series Forecasting Basics (32.81 MB)
3 Create and Visualize Synthetic Dataset (15.07 MB)
4 Prepare Data for Training using a windowed dataset (75.49 MB)
5 Review Model Architecture (61.22 MB)
6 CHALLENGE 🎉 (52.95 MB)
7 Challenge Solution Video (91.85 MB)
1 Introduction (28.22 MB)
2 Review DNN Forecasting (19.01 MB)
3 CHALLENGE 1 🎉 (73.5 MB)
4 Complete code challenge (43.68 MB)
5 Build DNN model (66.49 MB)
6 Review Recurrent Neural Networks (25.4 MB)
7 CHALLENGE 2 🎉 (24.71 MB)
8 Complete code challenge (45.23 MB)
9 Build RNN model (96.35 MB)
1 Introduction (35.53 MB)
10 Solution Video (34.97 MB)
2 Review DNN Model Architecture (14.07 MB)
3 🎉 CHALLENGE 1 Build DNN Forecasting Model (33.23 MB)
4 Solution Video (94.46 MB)
5 Review RNN Model Architecture (13.88 MB)
6 🎉 CHALLENGE 2 Build RNN Forecasting Model (10.39 MB)
7 Solution Video (21.6 MB)
8 Review LSTM Model Architecture (9.11 MB)
9 🎉 CHALLENGE 3 Build LSTM Forecasting Model (9.01 MB)
1 Introduction (26.13 MB)
2 Review Kaggle Sunspot Dataset & CBT Nuggets GitHub Repo (66.63 MB)
3 Build Baseline DNN Forecasting Model Part 1 (67.75 MB)
4 Build Baseline DNN Forecasting Model Part 2 (67.42 MB)
5 Review LSTMCNN Model Architecture (35.98 MB)
6 🎉 CHALLENGE Build LSTMCNN Forecasting Model (15.35 MB)
7 Solution Video (99.53 MB)
1 Introduction (22.99 MB)
2 Load & EDA MNIST Dataset (93.23 MB)
3 Callbacks Part 1 (66.23 MB)
4 Callbacks Part 2 (93.96 MB)
5 Convolution & Pooling (142.31 MB)
6 Challenge (42.96 MB)
7 Solution Video (111.45 MB)
1 Introduction (70.97 MB)
2 Explore the Food 101 dataset on Kaggle (70.96 MB)
3 Explore the modified ramen sushi dataset (129.97 MB)
4 Load dataset using ImageDataGenerator (83.09 MB)
5 Visualize random images with the labels (82.38 MB)
6 Challenge (73.79 MB)
7 Solution Video Part 1 (91.6 MB)
8 Solution Video Part 2 (113.58 MB)
1 Introduction (72.07 MB)
2 Explore CNNs in a Browser (132.34 MB)
3 What is a baseline model (79.49 MB)
4 Deep Neural Networks (DNNs) (108.75 MB)
5 Convolutional Neural Networks (CNNs) (191.66 MB)
6 Challenge (60.07 MB)
7 Solution Video (128.44 MB)
1 Introduction (69.51 MB)
2 Real-world scenario Teachable Machine Proof of Concept (118.03 MB)
3 Real-world scenario Teachable Machine Proof of Concept Part 2 (103.88 MB)
4 Real-world scenario Acquire and Upload Images Part 1 (90.01 MB)
5 Real-world scenario Acquire and Upload Images Part 2 (109.96 MB)
6 Challenge (28.28 MB)
7 Solution Video (51.2 MB)
1 Introduction (130.31 MB)
2 Baseline Model (136.35 MB)
3 Part 2 (119.45 MB)
4 CNN Model (86.96 MB)
5 Improvements (127.94 MB)
6 Challenge (30.97 MB)
7 Challenge Solution (123.88 MB)
1 Introduction (77.21 MB)
2 Explore Overfitting (85.52 MB)
3 Load Dataset (107.13 MB)
4 Challenge 1 Build and Train a Baseline Model from Pseudocode (104.21 MB)
5 Plot Training Curves (37.01 MB)
6 Reducing Overfitting (28.55 MB)
7 Challenge (17.89 MB)
8 Solution Video (100.8 MB)