1:39:55

Thanks!

01:40:40 why did you made a function inside another one ( input_function inside make_input_fn ) ?

Hi

Thing I needed to update as I went thru the course (will update as I go)

1:13:12

– Package change to scikit-learn : !pip install -q scikit-learn

20:28

55:23

Can someone explain why it is necessary to have a function inside the function for training? The make_input_fn and input_fn part and the lambda both uses this. Why can't we just use the input_fn directly?

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Thank you a lot.Quite useful. Summary : I am not gonna talk about it.

00:05 Introduction to TensorFlow 2.0 course for beginners.
02:26 Introduction to Google Collaboratory for easy machine learning setup
07:07 AI encompasses machine learning and deep learning
09:35 Neural networks use layered representation of data in machine learning.
14:12 Data is crucial in machine learning and neural networks
16:37 Features are input information and labels are output information.
21:07 Supervised learning involves guiding the model to make accurate predictions by comparing them to the actual labels
23:21 Unsupervised machine learning involves clustering data points without specific output data.
27:57 Training reinforcement models to maximize rewards in an environment.
30:00 Introduction to TensorFlow and its importance
34:36 Understanding the relation between computations and sessions in TensorFlow
36:52 Google Collaboratory allows easy access to pre-installed modules and server connection.
41:11 Importing TensorFlow in Google Collaboratory for TensorFlow 2.0
43:17 Tensors are fundamental in TensorFlow 2.0
47:58 Explanation of tensors and ranks
50:12 Understanding TensorFlow tensor shapes and ranks
54:41 Reshaping Tensors in TensorFlow
56:47 Using TF session to evaluate tensor objects
1:01:16 Different categories of machine learning algorithms
1:03:07 Linear regression for data prediction
1:07:22 Calculating the slope of a line using a triangle and dividing distances
1:09:29 Predicting values using the line of best fit
1:13:31 Overview of important Python modules like NumPy, pandas, and matplotlib
1:15:43 Predicting survival on the Titanic using TensorFlow 2.0
1:19:40 Splitting data into training and testing sets is crucial for model accuracy.
1:21:48 Separating the data for classification
1:26:09 Exploring dataset statistics and shape attributes
1:28:12 Understanding the data insights from the analysis
1:32:21 Handling categorical and numeric data in TensorFlow
1:34:39 Creating feature columns for TensorFlow model training
1:38:42 Epochs are used to feed data multiple times for better model training
1:40:55 Creating an input function for TensorFlow data set objects
1:45:19 Creating an estimator and training the model in TensorFlow
1:47:21 Explanation on how to access and interpret statistical values from a neural network model.
1:51:46 Exploring survival probabilities based on indices
1:53:52 Introduction to classification in TensorFlow 2.0
1:58:01 Data frames in TensorFlow 2.0 contain encoded species already, simplifying data preprocessing.
2:00:08 Creating input function and feature columns in TensorFlow 2.0
2:04:26 Setting up the neural network and defining the number of nodes and classes.
2:06:35 Using lambda functions to create chained functions
2:10:44 Creating a prediction function for specific flowers
2:12:46 Explaining the process of predicting on a single value
2:17:25 Clustering helps find clusters of like data points
2:19:50 Data points are assigned to clusters based on distance to centroids.
2:24:02 Understanding K means clustering
2:26:09 Hidden Markov model uses states and observations with associated probabilities.
2:30:36 Defining transition and observation probabilities in two states
2:32:56 Hidden Markov Model predicts future events based on past events
2:37:22 Explanation of transition probabilities and observation distribution in a Hidden Markov Model
2:39:31 Mismatch between TensorFlow versions
2:43:45 Hidden Markov models are used for probability-based predictions.
2:45:35 Introduction to neural networks and their working principle.
2:50:00 Designing the output layer for neural networks
2:52:19 Neural networks make predictions based on probability distributions for each class.
2:56:39 Introduction to biases as trainable parameters in neural networks
2:58:53 Neural network nodes determine values using weighted sums of connected nodes.
3:03:21 Explanation of different activation functions in neural networks
3:05:38 Sigmoid function is chosen for output neuron activation
3:10:00 Loss function measures the deviation of the neural network output from the expected output.
3:12:25 Understanding the concept of cost function and gradient descent
3:17:01 Neural networks update weights and biases to make better predictions with more data.
3:19:17 Loading and exploring the fashion amnesty dataset for training and testing neural networks.
3:23:54 Data pre processing is crucial for neural networks
3:25:54 Pre-processing images is crucial for training and testing in neural networks
3:30:26 Selecting optimizer, loss, and metrics for model compilation
3:32:33 Training and testing a neural network model in TensorFlow 2.0
3:36:51 Training with less epochs can lead to better model performance
3:39:00 Understanding predictions and probability distribution
3:43:34 TensorFlow deep learning model used for computer vision and classification tasks.
3:45:42 Images are represented by three color channels: red, green, and blue
3:50:09 Convolutional neural networks analyze features and patterns in images.
3:52:19 Convolutional neural networks use filters to identify patterns in images
3:56:49 Quantifying presence of filters using dot product
3:58:52 Understanding filter similarity in TensorFlow 2.0
4:03:09 Padding, Stride, and Pooling Operations in Convolutional Neural Networks
4:05:17 Pooling operations reduce feature map size
4:09:30 Loading and normalizing image data for neural networks
4:11:41 Understanding the input shape and layer breakdown
4:15:58 Optimizing model performance with key training strategies
4:17:59 Data augmentation is crucial for training convolutional neural networks with small datasets.
4:22:12 Utilizing pre-trained models for efficient neural network training
4:24:19 Modifying last layers of a neural network for classifying
4:28:24 Using pre-trained model, MobileNet v2, built into TensorFlow
4:30:31 Freezing the base model to prevent retraining
4:34:45 Evaluation of model with random weights before training.
4:36:58 Saving and loading models in TensorFlow
4:41:00 Natural Language Processing (NLP) is about understanding human languages through computing.
4:43:19 Sentiment analysis and text generation using natural language processing model
4:47:46 Introduction to bag of words technique in neural networks
4:49:54 Bag of words technique encodes sentences with the same representation, losing their meaning.
4:54:13 Word embeddings aim to represent similar words with similar numbers to address issues with arbitrary mappings.
4:56:25 Introduction to word embeddings in a 3D space
5:00:59 Difference between feed forward and recurrent neural networks
5:03:22 Explanation of processing words sequentially in a neural network
5:08:01 Introduction to Simple RNN and LSTM layers
5:10:29 Long Short Term Memory (LSTM) allows access to output from any previous state.
5:14:53 Padding sequences to ensure equal length for neural network input
5:17:02 Creating a neural network model for sentiment analysis
5:21:24 Evaluating model accuracy and preparing for predictions
5:23:49 Explanation of padding and sequence processing in TensorFlow 2.0
5:28:20 Analyzing sentiment impact on prediction accuracy
5:30:27 Training neural network to generate text sequences
5:34:48 Creating mapping from characters to indices
5:37:09 Creating training examples for TensorFlow neural network model
5:41:53 Batching and model building process in TensorFlow 2.0
5:44:07 Setting model parameters and layers in TensorFlow 2.0
5:49:05 Explaining model predictions for each element in batch and sequence length
5:51:26 The model outputs a tensor for each training example, and we need to create our own loss function to determine its performance.
5:56:05 Training neural networks with varying epochs for performance evaluation
5:58:29 Generating output sequences using TensorFlow model
6:02:53 Processing steps for text data in TensorFlow 2.0
6:05:05 Building and training the model with different batch sizes and checkpoints
6:09:25 Reinforcement learning involves an agent exploring an environment to achieve objectives.
6:11:43 States, Actions, and Rewards in Reinforcement Learning
6:16:24 Q matrix represents predicted rewards for actions in states.
6:18:43 Maximize agent's reward in the environment
6:23:21 Introducing exploration in reinforcement learning
6:25:26 Balancing Q table and random actions in Q learning algorithm
6:30:03 Discount factor helps in factoring future rewards into the equation for finding the best action in the next state.
6:32:16 Introduction to OpenAI Gym for training reinforcement learning models
6:36:46 Introduction to navigating a frozen lake environment using q learning.
6:38:54 Max steps and learning rate in reinforcement learning
6:43:05 Training the agent using Q-learning algorithm
6:45:18 Training process involves adjusting epsilon and monitoring reward progress.
6:49:39 Focus on a specific area in machine learning or AI for deeper learning.
6:51:47 Largest open source machine learning course in the world focused on TensorFlow and Python.
Crafted by Merlin AI.

The example on Titanic I think you misspoke. It is a linear classifier and not linear regression so that everyone knows.

Update @ 1:13:12: 'sklearn' has been replaced by scikit-learn

Thank you

it seems that tensors linear regression has been depreciated, does anyone know how to do it in the newer versions

🎯 Key Takeaways for quick navigation:

00:00 🎓 Introduction to Course and Audience
– Aimed at beginners in machine learning and artificial intelligence with basic programming knowledge.
03:16 📚 Course Structure and Resources
– Course breakdown, starting with machine learning and AI basics.
10:16 🤖 Understanding Artificial Intelligence, Machine Learning, and Neural Networks
– Definition of Artificial Intelligence (AI) as automating intellectual tasks.
14:25 📊 Importance of Data in Machine Learning
– Example dataset creation for student grades.
16:17 📊 Features and Labels Basics
– Features are input information for machine learning models.
17:42 📈 Importance of Data in Machine Learning
– Data is essential for creating machine learning models.
19:35 🧠 Types of Machine Learning: Supervised Learning
– Supervised learning involves having both features and labels.
22:43 🌐 Types of Machine Learning: Unsupervised Learning
– Unsupervised learning deals with only features and no labels.
25:25 🤖 Types of Machine Learning: Reinforcement Learning
– Reinforcement learning involves an agent, environment, and reward.
30:10 🧠 Introduction to TensorFlow and Module Structure
– TensorFlow is an open-source machine learning library by Google.
31:38 🚀 What can be done with TensorFlow?
– TensorFlow supports various machine learning tasks and neural networks.
32:54 🧠 TensorFlow Overview
– TensorFlow provides a library of tools for machine learning applications.
36:02 🚀 Getting Started with Google Collaboratory
– Google Collaboratory allows using Jupyter Notebooks in the cloud.
– Specify TensorFlow version in Collaboratory with `%tensorflow_version 2.x`.
43:29 🧮 Understanding Tensors
– Tensors are a generalization of vectors and matrices to potentially higher dimensions.
47:42 📏 Rank and Degree of Tensors
49:03 📊 Understanding Tensor Rank and Shape
52:13 🔄 Reshaping Tensors in TensorFlow
55:30 🧠 Types of Tensors in TensorFlow
56:51 🔄 Evaluating Tensors using Sessions
01:00:06 🤖 Introduction to Core Machine Learning Algorithms
01:04:20 📈 Linear Regression Basics
01:05:40 🔄 Using Linear Regression in Prediction
01:10:33 📊 Linear Regression in Three Dimensions
01:12:29 🔍 Examples of Linear Regression
01:15:48 🛳️ Titanic Dataset for Linear Regression
01:19:06 📊 Data Preparation: Understanding Columns in Dataset
01:20:02 📊 Data Preparation: Creating Training and Testing Sets
01:21:19 📊 Data Exploration: Pandas DataFrames and Descriptive Stats
01:26:57 📊 Data Visualization: Creating Histograms and Plots
01:29:47 📊 Data Understanding: Training and Testing Sets Analysis
01:30:14 📊 Feature Columns: Categorical and Numeric Data
01:33:55 🧮 Feature Columns for Categorical Data
01:36:14 📊 Feature Columns for Numeric Data
01:37:08 🔄 Training Process Overview
01:40:20 🤖 Input Function Creation
01:45:02 🤖 Creating Linear Estimator
01:46:25 🚂 Model Training Process
01:47:48 📈 Model Evaluation and Predictions
01:55:04 📊 Introduction to Classification
01:57:25 🗃️ Loading and Preparing Dataset
02:00:08 🔄 Input Function
02:01:36 🧮 Feature Columns
02:03:33 🧠 Building a Deep Neural Network Classifier
02:04:26 🧠 Neural Network Architecture
02:04:56 🤖 Training the Model
02:07:43 🧾 Training Output and Evaluation
02:09:58 🔄 Model Evaluation and Prediction
02:11:53 📊 Predictions on New Data
02:17:20 🤔 Introduction to Clustering (K-Means)
02:20:07 🌐 K-Means Clustering Overview
02:25:14 📊 Hidden Markov Models Introduction
02:28:22 🎲 States, Observations, and Transitions
02:33:56 🔍 Purpose of Hidden Markov Models
02:35:44 🌡️ Hidden Markov Model Introduction,
02:37:09 📊 TensorFlow Probability Distributions,
02:38:33 📉 Building the Hidden Markov Model,
02:41:40 🔄 Modifying Probabilities and Observing Changes,
02:45:47 🧠 Introduction to Neural Networks,
02:50:45 🧠 Neural Network Layers and Output Design
– Single output neuron with a value between 0 and 1 for binary classification.
– Multiple output neurons for predicting probabilities in a classification task.
02:53:07 🔗 Hidden Layer in Neural Networks
02:54:28 🌐 Connectivity: Weights and Biases in Neural Networks
03:00:25 ⚙️ Weighted Sum, Bias, and Information Flow
03:02:39 🔄 Activation Functions in Neural Networks
03:06:50 *URL](https://youtu.be/tPYj3fFJGjk?t=11210s) 🧠 Activation Functions*
03:08:11 *URL](https://youtu.be/tPYj3fFJGjk?t=11691s) 📊 Moving to Higher Dimensions*
03:09:32 *URL](https://youtu.be/tPYj3fFJGjk?t=11747s) 📉 Loss Function Basics*
03:12:50 *URL](https://youtu.be/tPYj3fFJGjk?t=11870s) ⚙️ Optimizing with Gradient Descent*
03:18:01 *URL](https://youtu.be/tPYj3fFJGjk?t=12881s) 🔄 Building the First Neural Network*
03:23:08 🖼️ Image and Label Exploration
03:24:27 🔄 Data Pre-processing
03:27:13 🧠 Model Creation
03:30:04 🤖 Compiling the Model
03:32:20 ⚙️ Training the Model
03:35:05 🧪 Testing and Evaluating the Model
03:38:19 🖼️ Overview of Image Prediction
03:39:16 🧠 Understanding Predictions with Arrays
03:40:07 🥿 Decoding Predictions to Class Names
03:41:25 🤖 Verifying Predictions Script
03:43:17 🌐 Introduction to Convolutional Neural Networks (CNN)
03:45:11 🖼️ Understanding Image Data Dimensions
03:47:01 🔄 Global vs. Local Patterns in Neural Networks
03:51:11 📊 Convolutional Layer Output Feature Maps
03:54:23 🎨 Convolutional Neural Network Overview
03:55:19 🖼️ Looking for Filters in Images
03:56:40 📊 Dot Product and Feature Maps
04:00:23 🔄 Padding, Stride, and Computational Efficiency
04:04:59 🏞️ Pooling Operations
04:08:10 🚀 Building a Convolutional Neural Network with Keras
04:09:30 🖼️ Loading CIFAR-10 Dataset and Normalization
04:12:40 🧱 Convolutional Base Summary
04:14:51 🧠 Adding Dense Layers for Classification
04:15:49 🎓 Model Training and Evaluation
04:18:37 🔄 Data Augmentation
04:22:42 🤖 Using Pre-trained Models
04:24:10 🤖 Using Pre-trained Models
04:25:33 📊 Loading and Preprocessing Data
04:26:56 🖼️ Image Reshaping and Scaling
04:29:43 🧠 Picking a Pre-trained Model
04:31:55 🧊 Freezing the Base Model
04:33:19 🏗️ Adding Custom Classifier
04:34:39 🎓 Model Compilation and Evaluation
04:36:58 🚂 Training the Model
04:37:57 💾 Saving and Loading Models
04:38:24 🔍 Introduction to Object Detection
04:38:50 🧠 Understanding TensorFlow and Facial Recognition
04:41:09 🗣️ Natural Language Processing with Recurrent Neural Networks
04:42:35 📈 Applications of Recurrent Neural Networks
04:44:24 📊 Challenges in Textual Data Processing
04:51:55 🔠 Issues with Direct Word-to-Integer Encoding
04:54:39 📊 Understanding Word Representation Challenges
04:55:32 🛠️ Word Embeddings Overview and Visualization
04:58:44 🧠 Word Embeddings as a Layer in Neural Networks
04:59:41 🔢 Preparing Textual Data for Neural Networks
05:02:26 🔄 Unraveling Recurrent Neural Network Layers
05:09:49 🚀 Long Short-Term Memory (LSTM) Layers
05:11:12 🧠 Understanding Long Short-Term Memory (LSTM)
05:12:34 🎥 Sentiment Analysis on Movie Reviews
05:13:55 📊 Data Preprocessing for Neural Network Input
05:17:11 🧠 Building and Compiling the LSTM Model
05:21:43 📈 Model Evaluation and Results
05:24:19 🧐 Making Predictions with the Trained Model
05:26:44 🤖 Overview of Text Processing
05:28:09 📈 Sentiment Analysis Example
05:30:23 🎭 Recurrent Neural Network for Text Generation
05:31:48 🧠 Data Loading and Preprocessing
05:34:06 🧮 Encoding Characters and Creating Functions
05:37:18 🚀 Creating Training Examples
05:38:39 🔄 Mapping Sequences and Creating Batches
05:41:23 🏗️ Building the Model
05:42:46 🏗️ Building the Model Architecture
05:47:13 📉 Creating a Loss Function
05:55:57 🚂 Compiling and Training the Model
05:57:23 🔄 Rebuilding Model for Inference
05:59:41 🧠 Understanding Text Generation with RNNs
06:07:20 🤖 Recap and Guidance on Complex ML Concepts
06:08:18 🎮 Introduction to Reinforcement Learning
06:09:42 🔄 Key Concepts: Environment, Agent, State, Action, Reward
06:15:10 🧠 Introduction to Q-Learning in Reinforcement Learning
06:15:36 🤖 Q-Learning Introduction
06:17:50 🎨 Q-Learning Example on Whiteboard
06:19:14 🕹️ Navigating the Environment and Learning the Q-Table
06:25:15 🔄 Learning the Q-Table – Constants and Update Formula
06:31:40 🧠 Q-Learning and OpenAI Gym Introduction
06:32:35 🎮 OpenAI Gym Environment Setup
06:33:58 📊 Constants and Environment Setup
06:35:21 🔄 Picking Actions in Q-Learning
06:42:25 🔄 Updating Q-Values
06:44:39 🚀 Training Q-Learning Model
06:45:58 📈 Training Results and Graph
06:47:17 🤖 Q-Learning Example Conclusion
06:48:13 🏁 Conclusion of Reinforcement Learning Module
06:48:41 🚀 Next Steps and Further Learning Recommendations
06:50:26 🎓 Advice for Specialization and General Exploration
06:51:47 🏆 Course Conclusion and Call to Action

Made with HARPA AI

at 1:52:09 , how do you conclude that the survival probability is the last one and not the first one ?

Thank you freecodecamp for providing us with such an amazing course. But just a polite request, could you may do a few videos on Machine Learning or Ai using C++ instead of Python?

1:46:00

3:30:28

Parch typically refers to number of people in family aboard. The siblings column would affirm parent status, and the fare column would be indication of social economic factors.

Windows 11, wasted time @1:13:12 trying to install sklearn (old name??). Apparently colab already has it. "!pip install -U scikit-learn" shows "Requirement already satisfied".

We should give AI some human moral values to abide by before setting rules! Right?

4:20:00