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    <title>DEV Community: Hitesh Kumar</title>
    <description>The latest articles on DEV Community by Hitesh Kumar (@heroichitesh).</description>
    <link>https://dev.to/heroichitesh</link>
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      <title>DEV Community: Hitesh Kumar</title>
      <link>https://dev.to/heroichitesh</link>
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    <item>
      <title>DL with PyTorch (Week 3)</title>
      <dc:creator>Hitesh Kumar</dc:creator>
      <pubDate>Sat, 20 Jun 2020 13:28:22 +0000</pubDate>
      <link>https://dev.to/heroichitesh/dl-with-pytorch-week-3-277e</link>
      <guid>https://dev.to/heroichitesh/dl-with-pytorch-week-3-277e</guid>
      <description>&lt;h3&gt;
  
  
  Done with Assignment 3 🤩🎉😎🎊
&lt;/h3&gt;

&lt;p&gt;Done with all the assignments, now, will be participating in my first Kaggle competition(although it's not open to everyone still there will be a lot of competition from the participants of this course) and will start working on the course project.&lt;/p&gt;

&lt;p&gt;This assignment was a part of the 6 week-long free course named, &lt;strong&gt;Deep Learning with PyTorch: Zero to GANs&lt;/strong&gt; provided by &lt;a href="https://www.jovian.ml/"&gt;Jovian.ml&lt;/a&gt; in collaboration with &lt;a href="https://www.freecodecamp.org/"&gt;FreeCodeCamp&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Learned about different &lt;em&gt;hyperparameter tuning&lt;/em&gt;, &lt;em&gt;activation functions&lt;/em&gt; and we trained a model using image classification to do &lt;strong&gt;classification of images of everyday objects using neural networks&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Dataset used: &lt;strong&gt;CIFAR-10&lt;/strong&gt;&lt;br&gt;
Accuracy achieved: 55% to 56%&lt;/p&gt;

&lt;p&gt;My key &lt;strong&gt;&lt;em&gt;observations&lt;/em&gt;&lt;/strong&gt;🧐 during this assignment are mentioned in the compare tab &lt;a href="https://jovian.ml/hiteshkumar-1mv17cs042/03-cifar10-feedforward"&gt;here&lt;/a&gt; Have a look at it, especially the &lt;a href="https://jovian.ml/hiteshkumar-1mv17cs042/03-cifar10-feedforward/v/9"&gt;Version-9&lt;/a&gt; which contains a random selection of activation functions selected JUST FOR FUN🤪.&lt;/p&gt;

&lt;p&gt;To find out what I have been doing in this Lock-down period, click &lt;a href="https://technolgee.blogspot.com"&gt;here&lt;/a&gt;&lt;/p&gt;

</description>
      <category>activation</category>
      <category>classification</category>
      <category>pytorch</category>
      <category>cifar10</category>
    </item>
    <item>
      <title>DL with PyTorch (Week 2)</title>
      <dc:creator>Hitesh Kumar</dc:creator>
      <pubDate>Fri, 19 Jun 2020 15:02:52 +0000</pubDate>
      <link>https://dev.to/heroichitesh/dl-with-pytorch-week-2-280d</link>
      <guid>https://dev.to/heroichitesh/dl-with-pytorch-week-2-280d</guid>
      <description>&lt;h3&gt;
  
  
  Done with Assignment 2 🤩🎉😎🎊
&lt;/h3&gt;

&lt;p&gt;Although I am running a bit late but still I will try to complete the assignments and respective blogs ASAP. &lt;/p&gt;

&lt;p&gt;This assignment was a part of the 6 week-long free course named, &lt;strong&gt;Deep Learning with PyTorch: Zero to GANs&lt;/strong&gt; provided by &lt;a href="https://www.jovian.ml/"&gt;Jovian.ml&lt;/a&gt; in collaboration with &lt;a href="https://www.freecodecamp.org/"&gt;FreeCodeCamp&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Learned about &lt;em&gt;linear regression&lt;/em&gt;, &lt;em&gt;loss functions&lt;/em&gt; and we trained a model using linear regression to do &lt;strong&gt;insurance cost prediction&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;My key &lt;strong&gt;&lt;em&gt;observations&lt;/em&gt;&lt;/strong&gt;🧐 during this assignment were:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;It's better to choose loss function from below list if you are working on linear regression

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://pytorch.org/docs/stable/nn.functional.html#l1-loss"&gt;torch.nn.functional.l1_loss&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://pytorch.org/docs/stable/nn.functional.html#mse-loss"&gt;torch.nn.functional.mse_loss&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://pytorch.org/docs/stable/nn.functional.html#smooth-l1-loss"&gt;torch.nn.functional.smooth_l1_loss&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;


&lt;/li&gt;
&lt;li&gt;Larger the value of no. of epochs and learning rate , the more rapid changes can be seen in the loss_value&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can find a detailed explanation &lt;a href="https://jovian.ml/hiteshkumar-1mv17cs042/02-insurance-linear-regression"&gt;here&lt;/a&gt; Have a look at it.&lt;br&gt;
To find out what I have been doing in this Lock-down period, click &lt;a href="https://technolgee.blogspot.com"&gt;here&lt;/a&gt;&lt;/p&gt;

</description>
      <category>linear</category>
      <category>regression</category>
      <category>pytorch</category>
      <category>loss</category>
    </item>
    <item>
      <title>DL with PyTorch (Week 1)</title>
      <dc:creator>Hitesh Kumar</dc:creator>
      <pubDate>Fri, 29 May 2020 15:20:42 +0000</pubDate>
      <link>https://dev.to/heroichitesh/dl-with-pytorch-week-1-1i8a</link>
      <guid>https://dev.to/heroichitesh/dl-with-pytorch-week-1-1i8a</guid>
      <description>&lt;p&gt;Done with Assignment 1 🤩🎉😎🎊&lt;br&gt;
This assignment was a part of the 6 week long free course named, &lt;strong&gt;Deep Learning with PyTorch: Zero to GANs&lt;/strong&gt; provided by &lt;a href="https://www.jovian.ml/"&gt;Jovian.ml&lt;/a&gt; in collaboration with &lt;a href="https://www.freecodecamp.org/"&gt;FreeCodeCamp&lt;/a&gt;.&lt;br&gt;
Learnt about some functions of PyTorch tensors.&lt;br&gt;
You can find detailed explanation &lt;a href="https://jovian.ml/hiteshkumar-1mv17cs042/01-tensor-operations/v/3"&gt;here&lt;/a&gt; Have a look at it.&lt;br&gt;
To find out what I have been doing in this Lock-down period, click &lt;a href="https://technolgee.blogspot.com"&gt;here&lt;/a&gt;&lt;/p&gt;

</description>
      <category>pytorch</category>
      <category>dl</category>
      <category>jovian</category>
      <category>frecodecamp</category>
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