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Introduction To Machine Learning
  1. What Is Machine Learning Beginners Guide
  2. Supervised Vs Unsupervised Learning Key Differences
  3. Scikit Learn Tensorflow Keras Beginners Guide
  4. Setting Up Ml Environment Python Jupyter Conda Vscode
Data Preprocessing And Feature Engineering
  1. Understanding Data Types Machine Learning
  2. Handling Missing Data Outliers Data Preprocessing
  3. Feature Scaling Normalization Vs Standardization
  4. Feature Selection Dimensionality Reduction Pca Lda
Supervised Learning With Scikit Learn
  1. Master Scikit Learn Basics Api Data Splitting Workflows
  2. Predict House Prices Linear Regression Scikit Learn
  3. Logistic Regression Spam Detection Scikit Learn
  4. Decision Trees Random Forests Scikit Learn
  5. Master Support Vector Machines Svm Classification
  6. Model Evaluation Cross Validation Precision Recall F1 Score
Unsupervised Learning With Scikit Learn
  1. Introduction To Clustering Kmeans Dbscan Hierarchical
  2. Master Pca Dimensionality Reduction Scikit Learn
  3. Anomaly Detection Scikit Learn Techniques Applications
Introduction To Deep Learning Tensorflow Keras
  1. What Is Deep Learning Differences Applications
  2. Introduction To Tensorflow Keras Deep Learning
  3. Understanding Neural Networks Beginners Guide
  4. Activation Functions Relu Sigmoid Softmax Neural Networks
  5. Backpropagation Optimization Deep Learning
Building Neural Networks With Keras
  1. Build Simple Neural Network Keras Guide
  2. Split Data Training Validation Testing Keras
  3. Improve Neural Network Performance Keras Dropout Batch Norm
  4. Hyperparameter Tuning Keras Tuner Guide
Cnns For Image Processing
  1. Introduction To Cnns For Image Processing
  2. Build Cnn Mnist Image Classification Keras
  3. Boost Cnn Performance Data Augmentation Transfer Learning
Rnns And Lstms
  1. Understanding Rnns Lstms Time Series Data
  2. Build Lstm Stock Price Prediction Tensorflow
  3. Text Generation Lstms Tensorflow Keras
Natural Language Processing
  1. Text Preprocessing Nlp Tokenization Word Embeddings
  2. Sentiment Analysis Lstm Tensorflow Keras
  3. Text Classification Bert Tensorflow Keras Guide
Deploying Machine Learning Models
  1. Exporting Models Tensorflow Scikit Learn
  2. Deploy Machine Learning Models Flask Fastapi
  3. Deploying Ml Models To Cloud Platforms
All Course > Python Machine Learning > Introduction To Deep Learning Tensorflow Keras Oct 19, 2024

Introduction to TensorFlow & Keras for Deep Learning

In the last lesson, we explored the basics of deep learning, which is a part of machine learning that uses neural networks to solve complex problems. We learned how deep learning mimics the human brain to process data and make decisions. Now, we will dive into TensorFlow and Keras, two powerful tools that make deep learning easier to implement.

Table of Contents

Use-Case: Building a Simple Deep Learning Model

I recently worked on a project where I needed to predict house prices based on features like size, location, and number of rooms. I chose TensorFlow and Keras because they are widely used and have strong community support. At first, I found TensorFlow a bit complex, but Keras made it easier to build and train the model. This experience showed me how these tools can save time and effort when working on deep learning projects.

Overview of TensorFlow and Keras Ecosystems

TensorFlow is an open-source library developed by Google, which is used for building and training machine learning models. It is known for its flexibility and ability to handle large datasets. Keras, on the other hand, is a high-level API that runs on top of TensorFlow. It simplifies the process of building Neural Networks, making it ideal for beginners. Together, they form a powerful ecosystem that supports both research and production-level projects.

Installing TensorFlow and Keras

To get started, you need to install TensorFlow and Keras. If you are using Python, you can install them using pip. Open your terminal or command prompt and type:

pip install tensorflow

This command installs both TensorFlow and Keras since Keras is now part of TensorFlow. Once installed, you can import them in your Python script:

import tensorflow as tf  
from tensorflow import keras

These lines of code will allow you to access all the functions and classes provided by TensorFlow and Keras.

Building a Deep Learning Model with TensorFlow and Keras

Let’s build a simple neural network using Keras. Suppose we want to create a model that classifies images of handwritten digits (0-9). Here’s how you can do it:

# Step 1: Load the dataset  
mnist = keras.datasets.mnist  
(x_train, y_train), (x_test, y_test) = mnist.load_data()  

# Step 2: Normalize the data  
x_train, x_test = x_train / 255.0, x_test / 255.0  

# Step 3: Build the model  
model = keras.Sequential([  
    keras.layers.Flatten(input_shape=(28, 28)),  
    keras.layers.Dense(128, activation='relu'),  
    keras.layers.Dense(10, activation='softmax')  
])  

# Step 4: Compile the model  
model.compile(optimizer='adam',  
              loss='sparse_categorical_crossentropy',  
              metrics=['accuracy'])  

# Step 5: Train the model  
model.fit(x_train, y_train, epochs=5)  

# Step 6: Evaluate the model  
model.evaluate(x_test, y_test)

This code loads the MNIST dataset, normalizes the data, builds a simple neural network, and trains it. The model achieves good accuracy in just a few epochs, showing how easy it is to use Keras for deep learning tasks.

Differences Between TensorFlow and Keras

While TensorFlow and Keras work together, they have some differences. TensorFlow is more flexible and allows you to customize every part of your model. However, this flexibility can make it harder to use for beginners. Keras, on the other hand, is designed to be user-friendly. It provides simple functions for building and training models, which makes it perfect for quick prototyping. If you are new to deep learning, I recommend starting with Keras and then exploring TensorFlow as you gain more experience.

Conclusion

In this tutorial, we introduced TensorFlow and Keras, two essential tools for deep learning. We covered their ecosystems, installation process, and how to build a simple neural network using Keras. We also discussed the differences between TensorFlow and Keras, helping you choose the right tool for your needs.

Now that you have a basic understanding of TensorFlow and Keras, you are ready to dive deeper into Neural Networks. In the next lesson, we will explore how Neural Networks work and how to design them for different tasks.

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