Python for Deep Learning: Build Neural Networks in Python
- Description
- Curriculum
- FAQ
- Reviews
Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it’s no secret that Python’s best application is in deep learning and artificial intelligence tasks.
While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place.
If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans.
There are hundreds of machine learning resources available on the internet. However, you’re at risk of learning unnecessary lessons if you don’t filter what you learn. While creating this course, we’ve helped with filtering to isolate the essential basics you’ll need in your deep learning journey.
It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you.
It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic.
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12Single layer perceptron (SLP) modelVideo lesson
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13Radial Basis Network (RBN)Video lesson
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14Multi-layer perceptron (MLP) Neural NetworkVideo lesson
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15Recurrent neural network (RNN)Video lesson
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16Long Short-Term Memory (LSTM) networksVideo lesson
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17Hopfield neural networkVideo lesson
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18Boltzmann Machine Neural NetworkVideo lesson
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19What is the Activation Function?Video lesson
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20Important TerminologiesVideo lesson
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21The sigmoid functionVideo lesson
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22Hyperbolic tangent functionVideo lesson
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23Softmax functionVideo lesson
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24Rectified Linear Unit (ReLU) functionVideo lesson
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25Leaky Rectified Linear Unit functionVideo lesson
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33IntroductionVideo lesson
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34Exploring the datasetVideo lesson
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35Problem StatementVideo lesson
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36Data Pre-processingVideo lesson
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37Loading the datasetVideo lesson
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38Splitting the dataset into independent and dependent variablesVideo lesson
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39Label encoding using scikit-learnVideo lesson
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40One-hot encoding using scikit-learnVideo lesson
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41Training and Test Sets: Splitting DataVideo lesson
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42Feature scalingVideo lesson
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43Building the Artificial Neural NetworkVideo lesson
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44Adding the input layer and the first hidden layerVideo lesson
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45Adding the next hidden layerVideo lesson
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46Adding the output layerVideo lesson
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47Compiling the artificial neural networkVideo lesson
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48Fitting the ANN model to the training setVideo lesson
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49Predicting the test set resultsVideo lesson
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